histogram_results_plotter.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation metrics AP Recall IoU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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      "image 3/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_102_jpg.rf.b7a6c72b0e27ca6bd7e393b73e16319b.jpg: 512x512 9 Aircrafts, 8.7ms\n",
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      "image 679/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_89_jpg.rf.049abf4986640b6d4b2baf1baafcc146.jpg: 512x512 15 Aircrafts, 8.3ms\n",
      "image 680/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_89_jpg.rf.1717c09509c6d3bc2f777130c7fc12ef.jpg: 512x512 13 Aircrafts, 8.3ms\n",
      "image 681/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_89_jpg.rf.7a27b2c357c69dce2b46e1a38ca894a7.jpg: 512x512 16 Aircrafts, 8.3ms\n",
      "image 682/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_8_jpg.rf.5a69ecb25c02c0b121de41532f5ff03b.jpg: 512x512 4 Aircrafts, 8.4ms\n",
      "image 683/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_8_jpg.rf.a3c0753603a8b9cf38e5acc0870a5445.jpg: 512x512 4 Aircrafts, 8.3ms\n",
      "image 684/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_8_jpg.rf.a9a513d363a3afb2e4ebc8e5e899fd56.jpg: 512x512 4 Aircrafts, 8.3ms\n",
      "image 685/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_90_jpg.rf.884ded74e2c6d2749ba0e67b62ad0f94.jpg: 512x512 1 Aircraft, 8.3ms\n",
      "image 686/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_90_jpg.rf.98b39a61f893eb3af3013f5b07e86499.jpg: 512x512 2 Aircrafts, 8.3ms\n",
      "image 687/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_90_jpg.rf.a6d7a134ef39c993fdfca312f44e0cb1.jpg: 512x512 5 Aircrafts, 8.3ms\n",
      "image 688/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_91_jpg.rf.06004d069e27a938ec79868e353b4468.jpg: 512x512 8 Aircrafts, 8.3ms\n",
      "image 689/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_91_jpg.rf.94da681750e211905d4a49c0e6fb0f2a.jpg: 512x512 6 Aircrafts, 8.3ms\n",
      "image 690/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_91_jpg.rf.deb07236560ffa8452a09c7a35fd8fae.jpg: 512x512 4 Aircrafts, 8.3ms\n",
      "image 691/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_92_jpg.rf.49bffd87db60c0297eef727c8f1603f8.jpg: 512x512 26 Aircrafts, 8.3ms\n",
      "image 692/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_92_jpg.rf.4b80767b8d7ad718f37433a4abd91cfb.jpg: 512x512 26 Aircrafts, 8.3ms\n",
      "image 693/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_92_jpg.rf.eb180198ed47e8d2ae2a1e6cffa6579b.jpg: 512x512 24 Aircrafts, 8.4ms\n",
      "image 694/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_94_jpg.rf.16f092be4a7b1a6adff21cd7bb52731c.jpg: 512x512 13 Aircrafts, 8.3ms\n",
      "image 695/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_94_jpg.rf.b2195925e7bf798627126926f569dad4.jpg: 512x512 12 Aircrafts, 8.4ms\n",
      "image 696/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_94_jpg.rf.b38e79c33b918f70a3083073744f94ef.jpg: 512x512 15 Aircrafts, 8.4ms\n",
      "image 697/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_96_jpg.rf.554843e0d50c602ae8fdc2b829c6de24.jpg: 512x512 1 Aircraft, 8.3ms\n",
      "image 698/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_96_jpg.rf.692789536de29e6e224802998a406698.jpg: 512x512 2 Aircrafts, 8.4ms\n",
      "image 699/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_96_jpg.rf.e32445c5592f08f8df6e721a6dfac511.jpg: 512x512 2 Aircrafts, 8.3ms\n",
      "image 700/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_97_jpg.rf.633b7fcb1e3be1f1a8b65c1ccf5225e6.jpg: 512x512 4 Aircrafts, 8.3ms\n",
      "image 701/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_97_jpg.rf.e5f3e8bc20b66768d8402a1dc217b878.jpg: 512x512 (no detections), 8.3ms\n",
      "image 702/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_97_jpg.rf.fdac735ba9434aee6a55e114ce9955be.jpg: 512x512 (no detections), 8.3ms\n",
      "image 703/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_98_jpg.rf.334bc6e785bcc15cf17738c729ba600a.jpg: 512x512 1 Aircraft, 8.3ms\n",
      "image 704/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_98_jpg.rf.6c0b6ad82a82f68d95cff41f2a4cc38d.jpg: 512x512 2 Aircrafts, 8.3ms\n",
      "image 705/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_98_jpg.rf.87b6bdeaac58469228026d17abbc10e5.jpg: 512x512 2 Aircrafts, 8.4ms\n",
      "image 706/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_99_jpg.rf.5bd16a72e88a9b893c0592e505c169f6.jpg: 512x512 5 Aircrafts, 8.3ms\n",
      "image 707/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_99_jpg.rf.6f780a0e547588f50122f2d3333e184b.jpg: 512x512 10 Aircrafts, 8.4ms\n",
      "image 708/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_99_jpg.rf.a9cfa6f56d9ab9e6e3230bacb936fe6a.jpg: 512x512 4 Aircrafts, 8.3ms\n",
      "Speed: 1.4ms preprocess, 8.7ms inference, 0.7ms postprocess per image at shape (1, 3, 512, 512)\n",
      "Results saved to \u001b[1mdata/GDIT_eval/yolov8_inference/predict\u001b[0m\n",
      "657 labels saved to data/GDIT_eval/yolov8_inference/predict/labels\n",
      "\n",
      "image 1/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_100_jpg.rf.f764c95324c38a84bc2af17b1243f913.jpg: 512x512 12 Aircrafts, 8.3ms\n",
      "image 2/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_104_jpg.rf.9ab0c64ca392b7f6d0b1fe5dc12b2a8c.jpg: 512x512 4 Aircrafts, 8.4ms\n",
      "image 3/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_110_jpg.rf.fa9e3404321d04de0f9318c996d2796c.jpg: 512x512 23 Aircrafts, 13.1ms\n",
      "image 4/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_118_jpg.rf.4254cd74c3e555998d50a2982c1263c2.jpg: 512x512 24 Aircrafts, 8.3ms\n",
      "image 5/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_132_jpg.rf.6faa08a244f3677188a72718cc2d1849.jpg: 512x512 3 Aircrafts, 13.2ms\n",
      "image 6/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_133_jpg.rf.33d2673a8d272cf9aabbe29e49ea251a.jpg: 512x512 (no detections), 8.4ms\n",
      "image 7/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_164_jpg.rf.9039b588226dba607e29a8b74cf72705.jpg: 512x512 4 Aircrafts, 8.3ms\n",
      "image 8/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_197_jpg.rf.666646a98fb98e265d87b1f6463d1380.jpg: 512x512 2 Aircrafts, 8.3ms\n",
      "image 9/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_207_jpg.rf.61a2ed7dbe700526ad75a17e9c9304a9.jpg: 512x512 5 Aircrafts, 8.3ms\n",
      "image 10/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_20_jpg.rf.84d6aa6622df2998c395dc7e5fedd94e.jpg: 512x512 13 Aircrafts, 8.3ms\n",
      "image 11/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_211_jpg.rf.0d8bbda9a134692ba9e2f678b397cfa6.jpg: 512x512 2 Aircrafts, 8.3ms\n",
      "image 12/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_214_jpg.rf.3a324502759ab9fd15748acff0fcdafc.jpg: 512x512 7 Aircrafts, 8.4ms\n",
      "image 13/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_218_jpg.rf.feea213d43c59076e96cfc3052f92103.jpg: 512x512 21 Aircrafts, 8.3ms\n",
      "image 14/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_222_jpg.rf.f4ea1438e7f3e40ece0f3ea0b973b33f.jpg: 512x512 20 Aircrafts, 8.4ms\n",
      "image 15/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_227_jpg.rf.0fbf8124220e7d61dc82e7e68a8ac332.jpg: 512x512 19 Aircrafts, 8.3ms\n",
      "image 16/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_230_jpg.rf.99eca2731a28ac2fd39f499ac783b96d.jpg: 512x512 5 Aircrafts, 8.3ms\n",
      "image 17/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_237_jpg.rf.c735c27f0423e4850c5d96950e0a3f14.jpg: 512x512 15 Aircrafts, 8.3ms\n",
      "image 18/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_241_jpg.rf.48233f88e0aba89db4dd06f40b5c3514.jpg: 512x512 6 Aircrafts, 8.3ms\n",
      "image 19/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_246_jpg.rf.3d892810357f48026932d5412fa81574.jpg: 512x512 12 Aircrafts, 8.3ms\n",
      "image 20/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_248_jpg.rf.0af493dc24f46fe0f5ce707d8dc423e5.jpg: 512x512 11 Aircrafts, 8.6ms\n",
      "image 21/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_251_jpg.rf.63c401d0add34e5fe7f0791f74f379ca.jpg: 512x512 5 Aircrafts, 8.6ms\n",
      "image 22/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_252_jpg.rf.7e946be8b045c7ef244a5198dbd91e67.jpg: 512x512 5 Aircrafts, 8.6ms\n",
      "image 23/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_260_jpg.rf.d4aabd5c92ce23464b79d58ed7c4a43d.jpg: 512x512 2 Aircrafts, 8.6ms\n",
      "image 24/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_26_jpg.rf.0cad1a8375b394cab15b53c739ab8535.jpg: 512x512 2 Aircrafts, 8.6ms\n",
      "image 25/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_276_jpg.rf.8096ae3963f06debeecd55c12f622361.jpg: 512x512 6 Aircrafts, 12.4ms\n",
      "image 26/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_27_jpg.rf.d3cf9007a59c6912449d0c3123ce44cc.jpg: 512x512 1 Aircraft, 9.0ms\n",
      "image 27/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_316_jpg.rf.db1acd1bf660918e5eb792cff60af01e.jpg: 512x512 4 Aircrafts, 8.6ms\n",
      "image 28/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_317_jpg.rf.9e7080d66d51be9d6eb97c2ad5018efc.jpg: 512x512 7 Aircrafts, 8.7ms\n",
      "image 29/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_341_jpg.rf.840eeefb022622fc3ac3a43dc2708886.jpg: 512x512 7 Aircrafts, 8.7ms\n",
      "image 30/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_360_jpg.rf.0ca53fdb63f92d932f9a09c38907659b.jpg: 512x512 28 Aircrafts, 8.7ms\n",
      "image 31/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_41_jpg.rf.c544f2d048bda4f7950d83d064743467.jpg: 512x512 17 Aircrafts, 8.7ms\n",
      "image 32/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_77_jpg.rf.e1a1a14a43f962dc16d5c66fffd1ada6.jpg: 512x512 1 Aircraft, 8.7ms\n",
      "image 33/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_93_jpg.rf.8ed8b1cdf8fd88d9198039c76446d113.jpg: 512x512 37 Aircrafts, 12.2ms\n",
      "image 34/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_95_jpg.rf.15b2737daced1d0da0cbd52ee47981fb.jpg: 512x512 5 Aircrafts, 8.7ms\n",
      "Speed: 1.6ms preprocess, 9.0ms inference, 0.7ms postprocess per image at shape (1, 3, 512, 512)\n",
      "Results saved to \u001b[1mdata/GDIT_eval/yolov8_inference/predict2\u001b[0m\n",
      "33 labels saved to data/GDIT_eval/yolov8_inference/predict2/labels\n",
      "\n",
      "image 1/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_101_jpg.rf.7ebc78dda3047bf00cd606af25d2cfc5.jpg: 512x512 45 Aircrafts, 13.9ms\n",
      "image 2/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_106_jpg.rf.95d4408bef9109aa8db68b3c82547534.jpg: 512x512 28 Aircrafts, 8.7ms\n",
      "image 3/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_109_jpg.rf.1a0d654c74bb453e1377da7b4b3c63fd.jpg: 512x512 4 Aircrafts, 15.0ms\n",
      "image 4/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_10_jpg.rf.34f45467386463f1a192d7b707027590.jpg: 512x512 11 Aircrafts, 8.7ms\n",
      "image 5/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_111_jpg.rf.70e0ac3793f8a0746d8e632ac06e3ca4.jpg: 512x512 10 Aircrafts, 8.7ms\n",
      "image 6/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_113_jpg.rf.643164060f6616a64caed8eb44319c80.jpg: 512x512 8 Aircrafts, 8.7ms\n",
      "image 7/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_114_jpg.rf.e35c5616e03466e5b1335bf63bc9c913.jpg: 512x512 12 Aircrafts, 8.7ms\n",
      "image 8/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_119_jpg.rf.157460637b0c151988ba7b7f89f82cec.jpg: 512x512 27 Aircrafts, 8.7ms\n",
      "image 9/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_134_jpg.rf.f6d2601334326ca1c930ad4e1c78a282.jpg: 512x512 10 Aircrafts, 11.4ms\n",
      "image 10/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_138_jpg.rf.69ba88ad6480215890a91e04417c21f6.jpg: 512x512 14 Aircrafts, 13.8ms\n",
      "image 11/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_143_jpg.rf.1736081b3da91618b8eb6e8269ab11b2.jpg: 512x512 6 Aircrafts, 8.7ms\n",
      "image 12/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_155_jpg.rf.ed5244b7809a06b013e751e027ca87c7.jpg: 512x512 (no detections), 8.7ms\n",
      "image 13/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_156_jpg.rf.9e88663de6b61f8c7ddcd5505aad4da5.jpg: 512x512 13 Aircrafts, 8.7ms\n",
      "image 14/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_163_jpg.rf.10d9c3cab912933bf929570d3a1b10e5.jpg: 512x512 7 Aircrafts, 8.6ms\n",
      "image 15/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_165_jpg.rf.d058d70ef99063693ce0ccf17b8e3ee4.jpg: 512x512 1 Aircraft, 8.7ms\n",
      "image 16/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_166_jpg.rf.975c28a11e5b6bd0095e5659db3eed7c.jpg: 512x512 1 Aircraft, 8.7ms\n",
      "image 17/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_167_jpg.rf.365ad41d6a7a85a1fb469afe8377294b.jpg: 512x512 18 Aircrafts, 8.7ms\n",
      "image 18/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_170_jpg.rf.064589704e3e92dc1c2466dd06289785.jpg: 512x512 2 Aircrafts, 8.7ms\n",
      "image 19/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_174_jpg.rf.6e27beceb6fc33d74df6a65736b45ce5.jpg: 512x512 17 Aircrafts, 8.7ms\n",
      "image 20/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_175_jpg.rf.d2240a9175241ea32cfc84094ad78118.jpg: 512x512 (no detections), 13.2ms\n",
      "image 21/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_176_jpg.rf.855c70407d1993a6831dfcae854bc0a0.jpg: 512x512 (no detections), 8.7ms\n",
      "image 22/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_181_jpg.rf.78eea27ef15907e5a6309db64825a783.jpg: 512x512 28 Aircrafts, 8.7ms\n",
      "image 23/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_183_jpg.rf.5b2e914e2fba0bac70810c96014d1ede.jpg: 512x512 9 Aircrafts, 8.7ms\n",
      "image 24/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_186_jpg.rf.24a83fedba3950b190abc62b7de156c7.jpg: 512x512 14 Aircrafts, 8.7ms\n",
      "image 25/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_191_jpg.rf.5a397f662fd8e48b9fedcfe0bef991b2.jpg: 512x512 (no detections), 12.6ms\n",
      "image 26/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_19_jpg.rf.e152558eeb7929c019685cf49979c877.jpg: 512x512 8 Aircrafts, 8.7ms\n",
      "image 27/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_210_jpg.rf.f39076a3d9c402c209214a07e0eb8e92.jpg: 512x512 6 Aircrafts, 8.7ms\n",
      "image 28/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_215_jpg.rf.70ca8860b74c5150ec78edf1a9273d0a.jpg: 512x512 7 Aircrafts, 8.7ms\n",
      "image 29/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_21_jpg.rf.f400025be981c3839f942914914a526e.jpg: 512x512 7 Aircrafts, 8.7ms\n",
      "image 30/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_221_jpg.rf.b47f2d7153522625ba97db2c599d76ba.jpg: 512x512 5 Aircrafts, 8.7ms\n",
      "image 31/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_226_jpg.rf.0a1ad03884ab9c2d02153841b58e528b.jpg: 512x512 9 Aircrafts, 8.6ms\n",
      "image 32/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_231_jpg.rf.86377be31c6b4040a697f29a763fd351.jpg: 512x512 (no detections), 8.6ms\n",
      "image 33/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_232_jpg.rf.330af79ea7ca7ae6934e55d3c816d6dd.jpg: 512x512 1 Aircraft, 8.7ms\n",
      "image 34/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_240_jpg.rf.63137666867dc73d271bdda6388b53bb.jpg: 512x512 (no detections), 8.6ms\n",
      "image 35/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_242_jpg.rf.0a9510c1f1f06d3e3bde4de9bc963e2c.jpg: 512x512 9 Aircrafts, 8.7ms\n",
      "image 36/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_243_jpg.rf.828b4b03d80a28cd1c937f85bf38e90e.jpg: 512x512 9 Aircrafts, 8.6ms\n",
      "image 37/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_24_jpg.rf.4b9cdd87e524ee60256bd7aefcf27753.jpg: 512x512 5 Aircrafts, 8.6ms\n",
      "image 38/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_255_jpg.rf.7b6ef9b3beaedfdd83f2abd25890653f.jpg: 512x512 3 Aircrafts, 8.7ms\n",
      "image 39/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_258_jpg.rf.be138b5c1078d0cc744a0985fd93c3cc.jpg: 512x512 3 Aircrafts, 8.6ms\n",
      "image 40/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_259_jpg.rf.e689cde6752888142428f468b2ffee21.jpg: 512x512 24 Aircrafts, 8.6ms\n",
      "image 41/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_271_jpg.rf.f2ea82fb125a966b4be23bf72a73341a.jpg: 512x512 17 Aircrafts, 8.7ms\n",
      "image 42/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_275_jpg.rf.2cf97d8551e6f78ab825c29a8c42b112.jpg: 512x512 5 Aircrafts, 8.7ms\n",
      "image 43/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_285_jpg.rf.61aa67e4a92216331abd479d0c5d0094.jpg: 512x512 19 Aircrafts, 8.6ms\n",
      "image 44/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_286_jpg.rf.2e67e6c86a7887db6b45ff1d5d7a049c.jpg: 512x512 9 Aircrafts, 8.6ms\n",
      "image 45/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_287_jpg.rf.5a1bc27eb2b16a34b32d79ad6f1e74bc.jpg: 512x512 10 Aircrafts, 8.7ms\n",
      "image 46/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_288_jpg.rf.5779abbab8322edebe1786193e20bbd6.jpg: 512x512 12 Aircrafts, 8.6ms\n",
      "image 47/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_296_jpg.rf.975d8d895b5ac7fac4c91bf1af1b9df3.jpg: 512x512 4 Aircrafts, 10.9ms\n",
      "image 48/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_297_jpg.rf.5adf3c79abed2682245acec6b174541a.jpg: 512x512 11 Aircrafts, 8.7ms\n",
      "image 49/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_29_jpg.rf.6503f9394cc2b23c18a33d41a3a0350c.jpg: 512x512 6 Aircrafts, 8.7ms\n",
      "image 50/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_306_jpg.rf.fe0d75e224ef145d2ab87c5f73a385c2.jpg: 512x512 4 Aircrafts, 8.7ms\n",
      "image 51/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_312_jpg.rf.8f427b6cc53d2fa87834c99d93225199.jpg: 512x512 4 Aircrafts, 8.7ms\n",
      "image 52/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_313_jpg.rf.21ef4e4426961b099ead5d7ff8dec30e.jpg: 512x512 28 Aircrafts, 8.6ms\n",
      "image 53/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_31_jpg.rf.b5d449e10cc1f6e449780f182b7b802d.jpg: 512x512 10 Aircrafts, 8.6ms\n",
      "image 54/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_32_jpg.rf.e6947c4de7e5510ba618f46c4eca2697.jpg: 512x512 2 Aircrafts, 8.7ms\n",
      "image 55/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_335_jpg.rf.ebf433597c7662989dea5fb9a0ab1709.jpg: 512x512 43 Aircrafts, 8.7ms\n",
      "image 56/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_337_jpg.rf.babdbffdf3495b48f0a6eb7de269be0c.jpg: 512x512 15 Aircrafts, 8.7ms\n",
      "image 57/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_342_jpg.rf.56090b724353ccde8449e4188eb0a370.jpg: 512x512 5 Aircrafts, 8.7ms\n",
      "image 58/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_343_jpg.rf.703dbe9f1b68b4d5abc51da07bdf6f8e.jpg: 512x512 2 Aircrafts, 8.6ms\n",
      "image 59/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_344_jpg.rf.a55b5ae1548aec96c2390d5f06b418f9.jpg: 512x512 3 Aircrafts, 8.7ms\n",
      "image 60/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_350_jpg.rf.86636ca6e9be71317ce1c0f9e3b75493.jpg: 512x512 40 Aircrafts, 8.7ms\n",
      "image 61/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_352_jpg.rf.074068c60ab94ffaa6a2c763b7941f37.jpg: 512x512 12 Aircrafts, 8.7ms\n",
      "image 62/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_355_jpg.rf.53aaa2e9c670952b970a79cb2d97fa8e.jpg: 512x512 6 Aircrafts, 8.6ms\n",
      "image 63/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_40_jpg.rf.a8d41d28953de210a88b4ff348fea812.jpg: 512x512 16 Aircrafts, 8.7ms\n",
      "image 64/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_50_jpg.rf.06542146f4aa2e32dfffbc95c76b0d58.jpg: 512x512 1 Aircraft, 8.7ms\n",
      "image 65/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_59_jpg.rf.7502d161b396fcdcd5a7804f583ebf77.jpg: 512x512 6 Aircrafts, 8.6ms\n",
      "image 66/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_73_jpg.rf.d8c82ee0d624da5610e688dea785ae77.jpg: 512x512 1 Aircraft, 8.7ms\n",
      "image 67/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_7_jpg.rf.50cdc869d863fd877da947be98281d41.jpg: 512x512 6 Aircrafts, 8.7ms\n",
      "image 68/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_9_jpg.rf.eb369b638840333bc296c124addba6db.jpg: 512x512 7 Aircrafts, 8.7ms\n",
      "Speed: 1.5ms preprocess, 9.1ms inference, 0.7ms postprocess per image at shape (1, 3, 512, 512)\n",
      "Results saved to \u001b[1mdata/GDIT_eval/yolov8_inference/predict3\u001b[0m\n",
      "62 labels saved to data/GDIT_eval/yolov8_inference/predict3/labels\n"
     ]
    }
   ],
   "source": [
    "############### YOLOv8 ###############\n",
    "MODEL_PATH = \"output_tensor/yolov8/weights/best.pt\"\n",
    "\n",
    "AC_model = YOLO(model=MODEL_PATH)\n",
    "\n",
    "results = AC_model(source=\"data/GDIT_dataset/train/images\", conf = 0.1, save = True,\n",
    "                        save_txt = True, save_conf = True, project = \"data/GDIT_eval/yolov8_inference/\")  # predict on unseen images\n",
    "\n",
    "results1 = AC_model(source=\"data/GDIT_dataset/test/images\", conf = 0.1, save = True,\n",
    "                        save_txt = True, save_conf = True, project = \"data/GDIT_eval/yolov8_inference/\")  # predict on unseen images\n",
    "\n",
    "results2 = AC_model(source=\"data/GDIT_dataset/valid/images\", conf = 0.1, save = True,\n",
    "                        save_txt = True, save_conf = True, project = \"data/GDIT_eval/yolov8_inference/\")  # predict on unseen images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['output_tensor/yolov5/weights/best.pt'], source=data/GDIT_dataset/train/images, data=algorithms/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.3, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_csv=False, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=algorithms/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-272-gde64179 Python-3.8.18 torch-2.0.1 CUDA:0 (NVIDIA RTX A6000, 48677MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5x summary: 322 layers, 86173414 parameters, 0 gradients, 203.8 GFLOPs\n",
      "image 1/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_102_jpg.rf.0bc87c7c57f68d9a0acb281b532ce2f8.jpg: 640x640 10 Aircrafts, 11.9ms\n",
      "image 2/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_102_jpg.rf.66a4aaa8e7aea504aa7b74ad85d0212e.jpg: 640x640 11 Aircrafts, 11.8ms\n",
      "image 3/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_102_jpg.rf.b7a6c72b0e27ca6bd7e393b73e16319b.jpg: 640x640 9 Aircrafts, 11.9ms\n",
      "image 4/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_103_jpg.rf.276280193b3e71b4d17f2653ca4b8369.jpg: 640x640 7 Aircrafts, 11.9ms\n",
      "image 5/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_103_jpg.rf.86d22b81e39258c70a3ae564ed524669.jpg: 640x640 7 Aircrafts, 11.9ms\n",
      "image 6/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_103_jpg.rf.8f798c41fe3e9bda1974753b242b270b.jpg: 640x640 7 Aircrafts, 11.9ms\n",
      "image 7/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_105_jpg.rf.9a496f3c717db4c7bb1513a9e097d88c.jpg: 640x640 17 Aircrafts, 11.9ms\n",
      "image 8/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_105_jpg.rf.d28afa1caaec98a96a4132ecef10c940.jpg: 640x640 17 Aircrafts, 11.9ms\n",
      "image 9/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_105_jpg.rf.d726ec06557f14c587524294ccabd277.jpg: 640x640 18 Aircrafts, 11.9ms\n",
      "image 10/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_107_jpg.rf.8da722f8620cb178ee73bde016ed7e71.jpg: 640x640 55 Aircrafts, 11.9ms\n",
      "image 11/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_107_jpg.rf.b26295378c61f4c7717be4bc813d36e5.jpg: 640x640 58 Aircrafts, 11.9ms\n",
      "image 12/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_107_jpg.rf.ddff271920211d5c5204fe1d6c7e41da.jpg: 640x640 59 Aircrafts, 11.9ms\n",
      "image 13/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_108_jpg.rf.08dd9b129c7586f32e45eedccdc287f5.jpg: 640x640 20 Aircrafts, 11.9ms\n",
      "image 14/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_108_jpg.rf.6d478791cb6bae635017e69e1acf9403.jpg: 640x640 21 Aircrafts, 11.9ms\n",
      "image 15/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_108_jpg.rf.ba7b893e82e01bfbaa66bf111b2e4006.jpg: 640x640 20 Aircrafts, 11.9ms\n",
      "image 16/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_112_jpg.rf.a9c36becc6c83188a2d4441b5c9f4b92.jpg: 640x640 12 Aircrafts, 11.9ms\n",
      "image 17/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_112_jpg.rf.dd50231c80ecdecd47050acc73354e0b.jpg: 640x640 12 Aircrafts, 11.9ms\n",
      "image 18/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_112_jpg.rf.f89824da1c3a06d8b295dcde79660d3b.jpg: 640x640 13 Aircrafts, 11.9ms\n",
      "image 19/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_115_jpg.rf.422b6254c49ecb88247ed5c307a1d593.jpg: 640x640 7 Aircrafts, 11.9ms\n",
      "image 20/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_115_jpg.rf.7832d90809f097eb29a78153b31a4ab5.jpg: 640x640 7 Aircrafts, 11.9ms\n",
      "image 21/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_115_jpg.rf.a8dfc82eed60e8eb9c7e5a1da4a38131.jpg: 640x640 6 Aircrafts, 11.9ms\n",
      "image 22/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_116_jpg.rf.2a4b8f1b881d4182e51d0803e7a26813.jpg: 640x640 19 Aircrafts, 11.9ms\n",
      "image 23/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_116_jpg.rf.6d492ac9d03884139f63a091e9d34b6e.jpg: 640x640 19 Aircrafts, 11.9ms\n",
      "image 24/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_116_jpg.rf.f5a2bfa10d8c1ede425f200ebac07ba6.jpg: 640x640 19 Aircrafts, 11.9ms\n",
      "image 25/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_117_jpg.rf.48e775da36261ee206cb48af4c0f888c.jpg: 640x640 21 Aircrafts, 11.7ms\n",
      "image 26/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_117_jpg.rf.e8b8c33ff7e0ada285c34d874463d5bc.jpg: 640x640 21 Aircrafts, 11.3ms\n",
      "image 27/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_117_jpg.rf.f3e6b7855f0c8b379fceebd9f410bd81.jpg: 640x640 22 Aircrafts, 11.3ms\n",
      "image 28/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_11_jpg.rf.0a61773e396d3e12fafe8692ff450721.jpg: 640x640 10 Aircrafts, 11.4ms\n",
      "image 29/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_11_jpg.rf.cab9024789e56121b3600434b50b7199.jpg: 640x640 10 Aircrafts, 11.3ms\n",
      "image 30/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_11_jpg.rf.fa5cdd92e20bd6fdcc287ced8005a674.jpg: 640x640 10 Aircrafts, 11.3ms\n",
      "image 31/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_120_jpg.rf.3bde974032897aa99e51a258c2f53239.jpg: 640x640 6 Aircrafts, 11.3ms\n",
      "image 32/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_120_jpg.rf.69d8ebe14a2cac7dde984d259bb825f3.jpg: 640x640 7 Aircrafts, 11.3ms\n",
      "image 33/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_120_jpg.rf.e58413e4ca2fcfc22354241acb9190da.jpg: 640x640 5 Aircrafts, 11.3ms\n",
      "image 34/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_122_jpg.rf.0b2d21e5ed47dcbe403f751ea07cd6f0.jpg: 640x640 17 Aircrafts, 11.3ms\n",
      "image 35/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_122_jpg.rf.c5f23274af98a35a63863dfe6939db90.jpg: 640x640 18 Aircrafts, 11.3ms\n",
      "image 36/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_122_jpg.rf.e55f1072c1dda59655594edd787d5a48.jpg: 640x640 19 Aircrafts, 11.3ms\n",
      "image 37/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_123_jpg.rf.44c0615211b4138c1b509f2a0c54dc67.jpg: 640x640 17 Aircrafts, 11.3ms\n",
      "image 38/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_123_jpg.rf.87297b797e377d92cc0e844bb60d1496.jpg: 640x640 19 Aircrafts, 11.3ms\n",
      "image 39/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_123_jpg.rf.c723a1b739eaac59698922840971f257.jpg: 640x640 18 Aircrafts, 11.3ms\n",
      "image 40/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_124_jpg.rf.9bbaa833a8351b84c90a0e58c09bde42.jpg: 640x640 22 Aircrafts, 11.3ms\n",
      "image 41/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_124_jpg.rf.aa90fa8b46a74aaedcb9c0b7a000028c.jpg: 640x640 29 Aircrafts, 11.3ms\n",
      "image 42/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_124_jpg.rf.d42f8f2d14eae6ba1087d724e528bf8c.jpg: 640x640 25 Aircrafts, 11.3ms\n",
      "image 43/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_125_jpg.rf.7824686eb1580d6150e2d8d99ca3a95d.jpg: 640x640 10 Aircrafts, 11.3ms\n",
      "image 44/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_125_jpg.rf.999c49fa380fcb4a48a575c456557221.jpg: 640x640 14 Aircrafts, 11.3ms\n",
      "image 45/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_125_jpg.rf.f36b1320b4df24abb21fe8d2ae431de6.jpg: 640x640 2 Aircrafts, 11.3ms\n",
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      "image 636/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_72_jpg.rf.74b1fa6e0b6eafb32bc6b53988917b8c.jpg: 640x640 18 Aircrafts, 11.5ms\n",
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      "image 660/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_82_jpg.rf.e6e90c986620b2c41595297773a1dfab.jpg: 640x640 12 Aircrafts, 11.4ms\n",
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      "image 665/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_84_jpg.rf.9fee4c884f6d462160fde7c3a6168586.jpg: 640x640 1 Aircraft, 11.4ms\n",
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      "image 689/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_91_jpg.rf.94da681750e211905d4a49c0e6fb0f2a.jpg: 640x640 3 Aircrafts, 11.5ms\n",
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      "image 691/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_92_jpg.rf.49bffd87db60c0297eef727c8f1603f8.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 692/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_92_jpg.rf.4b80767b8d7ad718f37433a4abd91cfb.jpg: 640x640 14 Aircrafts, 11.4ms\n",
      "image 693/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_92_jpg.rf.eb180198ed47e8d2ae2a1e6cffa6579b.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 694/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_94_jpg.rf.16f092be4a7b1a6adff21cd7bb52731c.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 695/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_94_jpg.rf.b2195925e7bf798627126926f569dad4.jpg: 640x640 14 Aircrafts, 11.4ms\n",
      "image 696/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_94_jpg.rf.b38e79c33b918f70a3083073744f94ef.jpg: 640x640 13 Aircrafts, 11.4ms\n",
      "image 697/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_96_jpg.rf.554843e0d50c602ae8fdc2b829c6de24.jpg: 640x640 1 Aircraft, 11.5ms\n",
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      "image 699/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_96_jpg.rf.e32445c5592f08f8df6e721a6dfac511.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 700/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_97_jpg.rf.633b7fcb1e3be1f1a8b65c1ccf5225e6.jpg: 640x640 (no detections), 11.4ms\n",
      "image 701/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_97_jpg.rf.e5f3e8bc20b66768d8402a1dc217b878.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 702/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_97_jpg.rf.fdac735ba9434aee6a55e114ce9955be.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 703/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_98_jpg.rf.334bc6e785bcc15cf17738c729ba600a.jpg: 640x640 1 Aircraft, 11.5ms\n",
      "image 704/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_98_jpg.rf.6c0b6ad82a82f68d95cff41f2a4cc38d.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 705/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_98_jpg.rf.87b6bdeaac58469228026d17abbc10e5.jpg: 640x640 1 Aircraft, 11.5ms\n",
      "image 706/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_99_jpg.rf.5bd16a72e88a9b893c0592e505c169f6.jpg: 640x640 10 Aircrafts, 11.4ms\n",
      "image 707/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_99_jpg.rf.6f780a0e547588f50122f2d3333e184b.jpg: 640x640 11 Aircrafts, 11.5ms\n",
      "image 708/708 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/images/airport_99_jpg.rf.a9cfa6f56d9ab9e6e3230bacb936fe6a.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "Speed: 0.4ms pre-process, 11.4ms inference, 0.5ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1malgorithms/yolov5/runs/detect/exp4\u001b[0m\n",
      "644 labels saved to algorithms/yolov5/runs/detect/exp4/labels\n",
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['output_tensor/yolov5/weights/best.pt'], source=data/GDIT_dataset/test/images, data=algorithms/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.3, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_csv=False, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=algorithms/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-272-gde64179 Python-3.8.18 torch-2.0.1 CUDA:0 (NVIDIA RTX A6000, 48677MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5x summary: 322 layers, 86173414 parameters, 0 gradients, 203.8 GFLOPs\n",
      "image 1/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_100_jpg.rf.f764c95324c38a84bc2af17b1243f913.jpg: 640x640 3 Aircrafts, 11.9ms\n",
      "image 2/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_104_jpg.rf.9ab0c64ca392b7f6d0b1fe5dc12b2a8c.jpg: 640x640 (no detections), 11.9ms\n",
      "image 3/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_110_jpg.rf.fa9e3404321d04de0f9318c996d2796c.jpg: 640x640 23 Aircrafts, 11.9ms\n",
      "image 4/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_118_jpg.rf.4254cd74c3e555998d50a2982c1263c2.jpg: 640x640 24 Aircrafts, 11.8ms\n",
      "image 5/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_132_jpg.rf.6faa08a244f3677188a72718cc2d1849.jpg: 640x640 (no detections), 11.9ms\n",
      "image 6/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_133_jpg.rf.33d2673a8d272cf9aabbe29e49ea251a.jpg: 640x640 1 Aircraft, 11.9ms\n",
      "image 7/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_164_jpg.rf.9039b588226dba607e29a8b74cf72705.jpg: 640x640 3 Aircrafts, 11.9ms\n",
      "image 8/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_197_jpg.rf.666646a98fb98e265d87b1f6463d1380.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 9/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_207_jpg.rf.61a2ed7dbe700526ad75a17e9c9304a9.jpg: 640x640 3 Aircrafts, 11.4ms\n",
      "image 10/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_20_jpg.rf.84d6aa6622df2998c395dc7e5fedd94e.jpg: 640x640 13 Aircrafts, 11.4ms\n",
      "image 11/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_211_jpg.rf.0d8bbda9a134692ba9e2f678b397cfa6.jpg: 640x640 (no detections), 11.4ms\n",
      "image 12/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_214_jpg.rf.3a324502759ab9fd15748acff0fcdafc.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "image 13/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_218_jpg.rf.feea213d43c59076e96cfc3052f92103.jpg: 640x640 23 Aircrafts, 11.4ms\n",
      "image 14/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_222_jpg.rf.f4ea1438e7f3e40ece0f3ea0b973b33f.jpg: 640x640 4 Aircrafts, 11.4ms\n",
      "image 15/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_227_jpg.rf.0fbf8124220e7d61dc82e7e68a8ac332.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 16/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_230_jpg.rf.99eca2731a28ac2fd39f499ac783b96d.jpg: 640x640 3 Aircrafts, 11.4ms\n",
      "image 17/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_237_jpg.rf.c735c27f0423e4850c5d96950e0a3f14.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 18/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_241_jpg.rf.48233f88e0aba89db4dd06f40b5c3514.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "image 19/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_246_jpg.rf.3d892810357f48026932d5412fa81574.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 20/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_248_jpg.rf.0af493dc24f46fe0f5ce707d8dc423e5.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 21/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_251_jpg.rf.63c401d0add34e5fe7f0791f74f379ca.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 22/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_252_jpg.rf.7e946be8b045c7ef244a5198dbd91e67.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 23/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_260_jpg.rf.d4aabd5c92ce23464b79d58ed7c4a43d.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 24/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_26_jpg.rf.0cad1a8375b394cab15b53c739ab8535.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 25/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_276_jpg.rf.8096ae3963f06debeecd55c12f622361.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "image 26/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_27_jpg.rf.d3cf9007a59c6912449d0c3123ce44cc.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 27/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_316_jpg.rf.db1acd1bf660918e5eb792cff60af01e.jpg: 640x640 4 Aircrafts, 11.4ms\n",
      "image 28/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_317_jpg.rf.9e7080d66d51be9d6eb97c2ad5018efc.jpg: 640x640 7 Aircrafts, 11.4ms\n",
      "image 29/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_341_jpg.rf.840eeefb022622fc3ac3a43dc2708886.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "image 30/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_360_jpg.rf.0ca53fdb63f92d932f9a09c38907659b.jpg: 640x640 24 Aircrafts, 11.4ms\n",
      "image 31/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_41_jpg.rf.c544f2d048bda4f7950d83d064743467.jpg: 640x640 18 Aircrafts, 11.4ms\n",
      "image 32/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_77_jpg.rf.e1a1a14a43f962dc16d5c66fffd1ada6.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 33/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_93_jpg.rf.8ed8b1cdf8fd88d9198039c76446d113.jpg: 640x640 31 Aircrafts, 11.4ms\n",
      "image 34/34 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/images/airport_95_jpg.rf.15b2737daced1d0da0cbd52ee47981fb.jpg: 640x640 3 Aircrafts, 11.4ms\n",
      "Speed: 0.4ms pre-process, 11.5ms inference, 0.6ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1malgorithms/yolov5/runs/detect/exp5\u001b[0m\n",
      "31 labels saved to algorithms/yolov5/runs/detect/exp5/labels\n",
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['output_tensor/yolov5/weights/best.pt'], source=data/GDIT_dataset/valid/images, data=algorithms/yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.3, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_csv=False, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=algorithms/yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-272-gde64179 Python-3.8.18 torch-2.0.1 CUDA:0 (NVIDIA RTX A6000, 48677MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5x summary: 322 layers, 86173414 parameters, 0 gradients, 203.8 GFLOPs\n",
      "image 1/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_101_jpg.rf.7ebc78dda3047bf00cd606af25d2cfc5.jpg: 640x640 21 Aircrafts, 11.9ms\n",
      "image 2/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_106_jpg.rf.95d4408bef9109aa8db68b3c82547534.jpg: 640x640 32 Aircrafts, 11.9ms\n",
      "image 3/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_109_jpg.rf.1a0d654c74bb453e1377da7b4b3c63fd.jpg: 640x640 4 Aircrafts, 11.9ms\n",
      "image 4/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_10_jpg.rf.34f45467386463f1a192d7b707027590.jpg: 640x640 11 Aircrafts, 11.9ms\n",
      "image 5/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_111_jpg.rf.70e0ac3793f8a0746d8e632ac06e3ca4.jpg: 640x640 10 Aircrafts, 11.9ms\n",
      "image 6/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_113_jpg.rf.643164060f6616a64caed8eb44319c80.jpg: 640x640 9 Aircrafts, 11.9ms\n",
      "image 7/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_114_jpg.rf.e35c5616e03466e5b1335bf63bc9c913.jpg: 640x640 13 Aircrafts, 11.9ms\n",
      "image 8/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_119_jpg.rf.157460637b0c151988ba7b7f89f82cec.jpg: 640x640 29 Aircrafts, 11.9ms\n",
      "image 9/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_134_jpg.rf.f6d2601334326ca1c930ad4e1c78a282.jpg: 640x640 9 Aircrafts, 11.8ms\n",
      "image 10/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_138_jpg.rf.69ba88ad6480215890a91e04417c21f6.jpg: 640x640 10 Aircrafts, 11.4ms\n",
      "image 11/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_143_jpg.rf.1736081b3da91618b8eb6e8269ab11b2.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "image 12/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_155_jpg.rf.ed5244b7809a06b013e751e027ca87c7.jpg: 640x640 (no detections), 11.4ms\n",
      "image 13/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_156_jpg.rf.9e88663de6b61f8c7ddcd5505aad4da5.jpg: 640x640 13 Aircrafts, 11.4ms\n",
      "image 14/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_163_jpg.rf.10d9c3cab912933bf929570d3a1b10e5.jpg: 640x640 9 Aircrafts, 11.4ms\n",
      "image 15/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_165_jpg.rf.d058d70ef99063693ce0ccf17b8e3ee4.jpg: 640x640 (no detections), 11.4ms\n",
      "image 16/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_166_jpg.rf.975c28a11e5b6bd0095e5659db3eed7c.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 17/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_167_jpg.rf.365ad41d6a7a85a1fb469afe8377294b.jpg: 640x640 13 Aircrafts, 11.4ms\n",
      "image 18/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_170_jpg.rf.064589704e3e92dc1c2466dd06289785.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 19/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_174_jpg.rf.6e27beceb6fc33d74df6a65736b45ce5.jpg: 640x640 15 Aircrafts, 11.4ms\n",
      "image 20/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_175_jpg.rf.d2240a9175241ea32cfc84094ad78118.jpg: 640x640 (no detections), 11.5ms\n",
      "image 21/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_176_jpg.rf.855c70407d1993a6831dfcae854bc0a0.jpg: 640x640 (no detections), 11.4ms\n",
      "image 22/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_181_jpg.rf.78eea27ef15907e5a6309db64825a783.jpg: 640x640 25 Aircrafts, 11.4ms\n",
      "image 23/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_183_jpg.rf.5b2e914e2fba0bac70810c96014d1ede.jpg: 640x640 9 Aircrafts, 11.5ms\n",
      "image 24/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_186_jpg.rf.24a83fedba3950b190abc62b7de156c7.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 25/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_191_jpg.rf.5a397f662fd8e48b9fedcfe0bef991b2.jpg: 640x640 (no detections), 11.4ms\n",
      "image 26/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_19_jpg.rf.e152558eeb7929c019685cf49979c877.jpg: 640x640 7 Aircrafts, 11.4ms\n",
      "image 27/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_210_jpg.rf.f39076a3d9c402c209214a07e0eb8e92.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 28/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_215_jpg.rf.70ca8860b74c5150ec78edf1a9273d0a.jpg: 640x640 3 Aircrafts, 11.4ms\n",
      "image 29/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_21_jpg.rf.f400025be981c3839f942914914a526e.jpg: 640x640 7 Aircrafts, 11.4ms\n",
      "image 30/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_221_jpg.rf.b47f2d7153522625ba97db2c599d76ba.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 31/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_226_jpg.rf.0a1ad03884ab9c2d02153841b58e528b.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 32/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_231_jpg.rf.86377be31c6b4040a697f29a763fd351.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 33/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_232_jpg.rf.330af79ea7ca7ae6934e55d3c816d6dd.jpg: 640x640 (no detections), 11.4ms\n",
      "image 34/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_240_jpg.rf.63137666867dc73d271bdda6388b53bb.jpg: 640x640 (no detections), 11.4ms\n",
      "image 35/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_242_jpg.rf.0a9510c1f1f06d3e3bde4de9bc963e2c.jpg: 640x640 8 Aircrafts, 11.4ms\n",
      "image 36/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_243_jpg.rf.828b4b03d80a28cd1c937f85bf38e90e.jpg: 640x640 9 Aircrafts, 11.5ms\n",
      "image 37/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_24_jpg.rf.4b9cdd87e524ee60256bd7aefcf27753.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 38/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_255_jpg.rf.7b6ef9b3beaedfdd83f2abd25890653f.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 39/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_258_jpg.rf.be138b5c1078d0cc744a0985fd93c3cc.jpg: 640x640 3 Aircrafts, 11.4ms\n",
      "image 40/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_259_jpg.rf.e689cde6752888142428f468b2ffee21.jpg: 640x640 24 Aircrafts, 11.4ms\n",
      "image 41/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_271_jpg.rf.f2ea82fb125a966b4be23bf72a73341a.jpg: 640x640 16 Aircrafts, 11.4ms\n",
      "image 42/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_275_jpg.rf.2cf97d8551e6f78ab825c29a8c42b112.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 43/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_285_jpg.rf.61aa67e4a92216331abd479d0c5d0094.jpg: 640x640 19 Aircrafts, 11.4ms\n",
      "image 44/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_286_jpg.rf.2e67e6c86a7887db6b45ff1d5d7a049c.jpg: 640x640 11 Aircrafts, 11.5ms\n",
      "image 45/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_287_jpg.rf.5a1bc27eb2b16a34b32d79ad6f1e74bc.jpg: 640x640 10 Aircrafts, 11.4ms\n",
      "image 46/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_288_jpg.rf.5779abbab8322edebe1786193e20bbd6.jpg: 640x640 11 Aircrafts, 11.4ms\n",
      "image 47/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_296_jpg.rf.975d8d895b5ac7fac4c91bf1af1b9df3.jpg: 640x640 4 Aircrafts, 11.4ms\n",
      "image 48/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_297_jpg.rf.5adf3c79abed2682245acec6b174541a.jpg: 640x640 11 Aircrafts, 11.5ms\n",
      "image 49/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_29_jpg.rf.6503f9394cc2b23c18a33d41a3a0350c.jpg: 640x640 5 Aircrafts, 11.5ms\n",
      "image 50/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_306_jpg.rf.fe0d75e224ef145d2ab87c5f73a385c2.jpg: 640x640 3 Aircrafts, 11.4ms\n",
      "image 51/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_312_jpg.rf.8f427b6cc53d2fa87834c99d93225199.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 52/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_313_jpg.rf.21ef4e4426961b099ead5d7ff8dec30e.jpg: 640x640 29 Aircrafts, 11.4ms\n",
      "image 53/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_31_jpg.rf.b5d449e10cc1f6e449780f182b7b802d.jpg: 640x640 10 Aircrafts, 11.5ms\n",
      "image 54/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_32_jpg.rf.e6947c4de7e5510ba618f46c4eca2697.jpg: 640x640 2 Aircrafts, 11.5ms\n",
      "image 55/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_335_jpg.rf.ebf433597c7662989dea5fb9a0ab1709.jpg: 640x640 38 Aircrafts, 11.4ms\n",
      "image 56/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_337_jpg.rf.babdbffdf3495b48f0a6eb7de269be0c.jpg: 640x640 15 Aircrafts, 11.4ms\n",
      "image 57/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_342_jpg.rf.56090b724353ccde8449e4188eb0a370.jpg: 640x640 4 Aircrafts, 11.4ms\n",
      "image 58/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_343_jpg.rf.703dbe9f1b68b4d5abc51da07bdf6f8e.jpg: 640x640 2 Aircrafts, 11.5ms\n",
      "image 59/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_344_jpg.rf.a55b5ae1548aec96c2390d5f06b418f9.jpg: 640x640 2 Aircrafts, 11.4ms\n",
      "image 60/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_350_jpg.rf.86636ca6e9be71317ce1c0f9e3b75493.jpg: 640x640 35 Aircrafts, 11.4ms\n",
      "image 61/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_352_jpg.rf.074068c60ab94ffaa6a2c763b7941f37.jpg: 640x640 12 Aircrafts, 11.4ms\n",
      "image 62/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_355_jpg.rf.53aaa2e9c670952b970a79cb2d97fa8e.jpg: 640x640 6 Aircrafts, 11.4ms\n",
      "image 63/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_40_jpg.rf.a8d41d28953de210a88b4ff348fea812.jpg: 640x640 13 Aircrafts, 11.4ms\n",
      "image 64/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_50_jpg.rf.06542146f4aa2e32dfffbc95c76b0d58.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 65/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_59_jpg.rf.7502d161b396fcdcd5a7804f583ebf77.jpg: 640x640 7 Aircrafts, 11.4ms\n",
      "image 66/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_73_jpg.rf.d8c82ee0d624da5610e688dea785ae77.jpg: 640x640 1 Aircraft, 11.4ms\n",
      "image 67/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_7_jpg.rf.50cdc869d863fd877da947be98281d41.jpg: 640x640 5 Aircrafts, 11.4ms\n",
      "image 68/68 /home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/images/airport_9_jpg.rf.eb369b638840333bc296c124addba6db.jpg: 640x640 7 Aircrafts, 11.4ms\n",
      "Speed: 0.4ms pre-process, 11.5ms inference, 0.6ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1malgorithms/yolov5/runs/detect/exp6\u001b[0m\n",
      "61 labels saved to algorithms/yolov5/runs/detect/exp6/labels\n"
     ]
    }
   ],
   "source": [
    "############### YOLOv5 ###############\n",
    "!python algorithms/yolov5/detect.py --weights output_tensor/yolov5/weights/best.pt --conf 0.3 --source data/GDIT_dataset/train/images  --save-txt --save-conf \n",
    "!python algorithms/yolov5/detect.py --weights output_tensor/yolov5/weights/best.pt --conf 0.3 --source data/GDIT_dataset/test/images  --save-txt --save-conf  \n",
    "!python algorithms/yolov5/detect.py --weights output_tensor/yolov5/weights/best.pt --conf 0.3 --source data/GDIT_dataset/valid/images  --save-txt --save-conf  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loads checkpoint by local backend from path: output_tensor/ssd/best.pth\n",
      "01/23 15:32:17 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 15:32:17 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m10.0 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m10.0 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m6.3 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/ssd_inference/predict_train\n",
      "Loads checkpoint by local backend from path: output_tensor/ssd/best.pth\n",
      "01/23 15:34:15 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 15:34:15 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m6.2 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/ssd_inference/predict_test\n",
      "Loads checkpoint by local backend from path: output_tensor/ssd/best.pth\n",
      "01/23 15:34:28 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 15:34:28 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m6.5 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/ssd_inference/predict_val\n"
     ]
    }
   ],
   "source": [
    "###############   SSD  ###############\n",
    "!python image_inference.py data/GDIT_dataset/train/images output_tensor/ssd/ssd_hrplanesv2.py --weights output_tensor/ssd/best.pth --out-dir data/GDIT_eval/ssd_inference/predict_train --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/test/images output_tensor/ssd/ssd_hrplanesv2.py --weights output_tensor/ssd/best.pth --out-dir data/GDIT_eval/ssd_inference/predict_train --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/valid/images output_tensor/ssd/ssd_hrplanesv2.py --weights output_tensor/ssd/best.pth --out-dir data/GDIT_eval/ssd_inference/predict_val --pred-score-thr 0.3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loads checkpoint by local backend from path: output_tensor/faster-rcnn/best.pth\n",
      "01/23 19:26:18 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 19:26:18 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m \u001b[35m8.1 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m \u001b[35m8.1 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m \u001b[35m8.4 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/faster-rcnn_inference/predict_train\n",
      "Loads checkpoint by local backend from path: output_tensor/faster-rcnn/best.pth\n",
      "01/23 19:27:51 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 19:27:51 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.5 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/faster-rcnn_inference/predict_test\n",
      "Loads checkpoint by local backend from path: output_tensor/faster-rcnn/best.pth\n",
      "01/23 19:28:01 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 19:28:01 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m8.4 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m8.4 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m8.4 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/faster-rcnn_inference/predict_val\n"
     ]
    }
   ],
   "source": [
    "############# Faster RCNN ############\n",
    "!python image_inference.py data/GDIT_dataset/train/images output_tensor/faster-rcnn/faster-rcnn_hrplanesv2.py --weights output_tensor/faster-rcnn/best.pth --out-dir data/GDIT_eval/faster-rcnn_inference/predict_train --pred-score-thr 0.5\n",
    "!python image_inference.py data/GDIT_dataset/test/images output_tensor/faster-rcnn/faster-rcnn_hrplanesv2.py --weights output_tensor/faster-rcnn/best.pth --out-dir data/GDIT_eval/faster-rcnn_inference/predict_test --pred-score-thr 0.5\n",
    "!python image_inference.py data/GDIT_dataset/valid/images output_tensor/faster-rcnn/faster-rcnn_hrplanesv2.py --weights output_tensor/faster-rcnn/best.pth --out-dir data/GDIT_eval/faster-rcnn_inference/predict_val --pred-score-thr 0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loads checkpoint by local backend from path: output_tensor/retinanet/best.pth\n",
      "01/23 18:52:12 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 18:52:12 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m7.5 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m7.5 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.4 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/retinanet_inference/predict_train\n",
      "Loads checkpoint by local backend from path: output_tensor/retinanet/best.pth\n",
      "01/23 18:53:43 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 18:53:43 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m8.1 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/retinanet_inference/predict_test\n",
      "Loads checkpoint by local backend from path: output_tensor/retinanet/best.pth\n",
      "01/23 18:53:53 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 18:53:53 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.3 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/retinanet_inference/predict_val\n"
     ]
    }
   ],
   "source": [
    "############# RetinaNet ##############\n",
    "!python image_inference.py data/GDIT_dataset/train/images output_tensor/retinanet/retinanet_hrplanesv2.py --weights output_tensor/retinanet/best.pth --out-dir data/GDIT_eval/retinanet_inference/predict_train --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/test/images output_tensor/retinanet/retinanet_hrplanesv2.py --weights output_tensor/retinanet/best.pth --out-dir data/GDIT_eval/retinanet_inference/predict_test --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/valid/images output_tensor/retinanet/retinanet_hrplanesv2.py --weights output_tensor/retinanet/best.pth --out-dir data/GDIT_eval/retinanet_inference/predict_val --pred-score-thr 0.3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loads checkpoint by local backend from path: output_tensor/centernet/best.pth\n",
      "01/23 18:54:07 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 18:54:07 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/funct\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "ional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be \n",
      "required to pass the indexing argument. (Triggered internally at \n",
      "/opt/conda/conda-bld/pytorch_1682343962757/work/aten/src/ATen/native/TensorShape\n",
      ".cpp:3483.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.0 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.0 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.5 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/centernet_inference/predict_train\n",
      "Loads checkpoint by local backend from path: output_tensor/centernet/best.pth\n",
      "01/23 18:55:37 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 18:55:37 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/funct\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "ional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be \n",
      "required to pass the indexing argument. (Triggered internally at \n",
      "/opt/conda/conda-bld/pytorch_1682343962757/work/aten/src/ATen/native/TensorShape\n",
      ".cpp:3483.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m \u001b[35m8.6 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/centernet_inference/predict_test\n",
      "Loads checkpoint by local backend from path: output_tensor/centernet/best.pth\n",
      "01/23 18:55:46 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 18:55:46 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/funct\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "ional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be \n",
      "required to pass the indexing argument. (Triggered internally at \n",
      "/opt/conda/conda-bld/pytorch_1682343962757/work/aten/src/ATen/native/TensorShape\n",
      ".cpp:3483.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.1 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m8.1 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m8.1 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/centernet_inference/predict_val\n"
     ]
    }
   ],
   "source": [
    "############# CenterNet ##############\n",
    "!python image_inference.py data/GDIT_dataset/train/images output_tensor/centernet/centernet_hrplanesv2.py --weights output_tensor/centernet/best.pth --out-dir data/GDIT_eval/centernet_inference/predict_train --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/test/images output_tensor/centernet/centernet_hrplanesv2.py --weights output_tensor/centernet/best.pth --out-dir data/GDIT_eval/centernet_inference/predict_test --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/valid/images output_tensor/centernet/centernet_hrplanesv2.py --weights output_tensor/centernet/best.pth --out-dir data/GDIT_eval/centernet_inference/predict_val --pred-score-thr 0.3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loads checkpoint by local backend from path: output_tensor/detr/best.pth\n",
      "01/23 16:19:19 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 16:19:19 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m5.9 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m5.9 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m4.7 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:505: UserWarning: Warning: The text is out of bounds, the drawn text may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m4.7 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/detr_inference/predict_train\n",
      "Loads checkpoint by local backend from path: output_tensor/detr/best.pth\n",
      "01/23 16:21:54 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 16:21:54 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m4.7 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m4.7 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m4.8 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/detr_inference/predict_test\n",
      "Loads checkpoint by local backend from path: output_tensor/detr/best.pth\n",
      "01/23 16:22:10 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 16:22:10 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m10.0 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m10.0 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m \u001b[35m5.4 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "zer.py:505: UserWarning: Warning: The text is out of bounds, the drawn text may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m4.6 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/detr_inference/predict_val\n"
     ]
    }
   ],
   "source": [
    "###############  DETR  ###############\n",
    "!python image_inference.py data/GDIT_dataset/train/images output_tensor/detr/detr_hrplanesv2.py --weights output_tensor/detr/best.pth --out-dir data/GDIT_eval/detr_inference/predict_train --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/test/images output_tensor/detr/detr_hrplanesv2.py --weights output_tensor/detr/best.pth --out-dir data/GDIT_eval/detr_inference/predict_test --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/valid/images output_tensor/detr/detr_hrplanesv2.py --weights output_tensor/detr/best.pth --out-dir data/GDIT_eval/detr_inference/predict_val --pred-score-thr 0.3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loads checkpoint by local backend from path: output_tensor/rtmdet/best.pth\n",
      "01/23 19:16:11 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 19:16:11 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/funct\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "ional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be \n",
      "required to pass the indexing argument. (Triggered internally at \n",
      "/opt/conda/conda-bld/pytorch_1682343962757/work/aten/src/ATen/native/TensorShape\n",
      ".cpp:3483.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m7.3 it/s\u001b[0m \u001b[36m \u001b[0mm\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m7.3 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m8.3 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/rtmdet_inference/predict_train\n",
      "Loads checkpoint by local backend from path: output_tensor/rtmdet/best.pth\n",
      "01/23 19:17:45 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 19:17:45 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/funct\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "ional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be \n",
      "required to pass the indexing argument. (Triggered internally at \n",
      "/opt/conda/conda-bld/pytorch_1682343962757/work/aten/src/ATen/native/TensorShape\n",
      ".cpp:3483.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m7.9 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m7.9 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m \u001b[35m8.3 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/rtmdet_inference/predict_test\n",
      "Loads checkpoint by local backend from path: output_tensor/rtmdet/best.pth\n",
      "01/23 19:17:55 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Failed to search registry with scope \"mmdet\" in the \"function\" registry tree. As a workaround, the current \"function\" registry in \"mmengine\" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether \"mmdet\" is a correct scope, or whether the registry is initialized.\n",
      "01/23 19:17:55 - mmengine - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - `Visualizer` backend is not initialized because save_dir is None.\n",
      "\u001b[2K/home/safouane/miniconda3/envs/openmmlab/lib/python3.8/site-packages/torch/funct\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m  \u001b[36m \u001b[0m\n",
      "ional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be \n",
      "required to pass the indexing argument. (Triggered internally at \n",
      "/opt/conda/conda-bld/pytorch_1682343962757/work/aten/src/ATen/native/TensorShape\n",
      ".cpp:3483.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visuali\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m7.5 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:757: UserWarning: Warning: The bbox is out of bounds, the drawn bbox may \n",
      "not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2K/home/safouane/.local/lib/python3.8/site-packages/mmengine/visualization/visualim━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m \u001b[35m7.5 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "zer.py:828: UserWarning: Warning: The polygon is out of bounds, the drawn \n",
      "polygon may not be in the image\n",
      "  warnings.warn(\n",
      "\u001b[2KInference \u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[91m━\u001b[0m\u001b[35m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[90m━\u001b[0m\u001b[35m━\u001b[0m \u001b[35m7.9 it/s\u001b[0m \u001b[36m \u001b[0m\n",
      "\u001b[?25hresults have been saved at data/GDIT_eval/rtmdet_inference/predict_val\n"
     ]
    }
   ],
   "source": [
    "############### RTMdet ###############\n",
    "!python image_inference.py data/GDIT_dataset/train/images output_tensor/rtmdet/rtmdet_hrplanesv2.py --weights output_tensor/rtmdet/best.pth --out-dir data/GDIT_eval/rtmdet_inference/predict_train --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/test/images output_tensor/rtmdet/rtmdet_hrplanesv2.py --weights output_tensor/rtmdet/best.pth --out-dir data/GDIT_eval/rtmdet_inference/predict_test --pred-score-thr 0.3\n",
    "!python image_inference.py data/GDIT_dataset/valid/images output_tensor/rtmdet/rtmdet_hrplanesv2.py --weights output_tensor/rtmdet/best.pth --out-dir data/GDIT_eval/rtmdet_inference/predict_val --pred-score-thr 0.3\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.metrics import average_precision_score, precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_yolo_annotation(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "        annotations = [list(map(float, line.strip().split())) for line in lines]\n",
    "    return annotations\n",
    "\n",
    "def calculate_iou(box1, box2):\n",
    "    x1, y1, w1, h1 = box1\n",
    "    x2, y2, w2, h2 = box2\n",
    "\n",
    "    intersection_x = max(0, min(x1 + w1, x2 + w2) - max(x1, x2))\n",
    "    intersection_y = max(0, min(y1 + h1, y2 + h2) - max(y1, y2))\n",
    "    intersection_area = intersection_x * intersection_y\n",
    "\n",
    "    area1 = w1 * h1\n",
    "    area2 = w2 * h2\n",
    "    union_area = area1 + area2 - intersection_area\n",
    "\n",
    "    iou = intersection_area / union_area if union_area > 0 else 0\n",
    "    return iou\n",
    "\n",
    "def calculate_precision_recall_iou(gt_annotations, est_annotations, iou_threshold=0.5):\n",
    "    true_positives = 0\n",
    "    false_positives = 0\n",
    "    false_negatives = 0\n",
    "    total_iou = 0\n",
    "\n",
    "    for est_box in est_annotations:\n",
    "        iou_max_for_box = 0\n",
    "        for gt_box in gt_annotations:\n",
    "            iou = calculate_iou(gt_box[1:], est_box[1:5])\n",
    "            iou_max_for_box = max(iou_max_for_box, iou)\n",
    "\n",
    "        total_iou += iou_max_for_box\n",
    "\n",
    "        if iou_max_for_box >= iou_threshold:\n",
    "            true_positives += 1\n",
    "        else:\n",
    "            false_positives += 1\n",
    "\n",
    "    false_negatives = len(gt_annotations) - true_positives\n",
    "\n",
    "    precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0\n",
    "    recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0\n",
    "\n",
    "    mean_iou = total_iou / len(est_annotations) if len(est_annotations) > 0 else 0\n",
    "\n",
    "    return precision, recall, mean_iou\n",
    "\n",
    "\n",
    "def evaluate_yolo_folders(GT_folder, EST_folder):\n",
    "    gt_files = os.listdir(GT_folder)\n",
    "\n",
    "    total_precision = 0\n",
    "    total_recall = 0\n",
    "    total_iou = 0\n",
    "    total_files = 0\n",
    "\n",
    "    for gt_file in gt_files:\n",
    "        gt_file_path = os.path.join(GT_folder, gt_file)\n",
    "        est_file_path = os.path.join(EST_folder, gt_file)\n",
    "\n",
    "        if os.path.exists(est_file_path):\n",
    "            gt_annotations = parse_yolo_annotation(gt_file_path)\n",
    "            est_annotations = parse_yolo_annotation(est_file_path)\n",
    "\n",
    "            precision, recall, iou = calculate_precision_recall_iou(gt_annotations, est_annotations)\n",
    "\n",
    "            total_precision += precision\n",
    "            total_recall += recall\n",
    "            total_iou += iou\n",
    "            total_files += 1\n",
    "\n",
    "    if total_files > 0:\n",
    "        mean_precision = total_precision / total_files\n",
    "        mean_recall = total_recall / total_files\n",
    "        mean_iou = total_iou / total_files\n",
    "\n",
    "        print(f\"Overall Mean Precision: {mean_precision:.4f}\")\n",
    "        print(f\"Overall Mean Recall: {mean_recall:.4f}\")\n",
    "        print(f\"Overall Mean IoU: {mean_iou:.4f}\")\n",
    "    else:\n",
    "        print(\"No valid files found.\")\n",
    "    \n",
    "    return mean_precision, mean_recall, mean_iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "YOLOv5 Test:\n",
      "\n",
      "Overall Mean Precision: 0.9684\n",
      "Overall Mean Recall: 0.6607\n",
      "Overall Mean IoU: 0.7399\n",
      "\n",
      "YOLOv8 Test:\n",
      "\n",
      "Overall Mean Precision: 0.9188\n",
      "Overall Mean Recall: 0.6864\n",
      "Overall Mean IoU: 0.7108\n",
      "\n",
      "\n",
      "##################################\n",
      "\n",
      "YOLOv5 Train:\n",
      "\n",
      "Overall Mean Precision: 0.9559\n",
      "Overall Mean Recall: 0.7048\n",
      "Overall Mean IoU: 0.7523\n",
      "\n",
      "YOLOv8 Train:\n",
      "\n",
      "Overall Mean Precision: 0.9027\n",
      "Overall Mean Recall: 0.7095\n",
      "Overall Mean IoU: 0.7044\n",
      "\n",
      "\n",
      "##################################\n",
      "\n",
      "YOLOv5 Val:\n",
      "\n",
      "Overall Mean Precision: 0.9420\n",
      "Overall Mean Recall: 0.7695\n",
      "Overall Mean IoU: 0.7418\n",
      "\n",
      "YOLOv8 Val:\n",
      "\n",
      "Overall Mean Precision: 0.9000\n",
      "Overall Mean Recall: 0.7809\n",
      "Overall Mean IoU: 0.6960\n"
     ]
    }
   ],
   "source": [
    "#################### Test #####################\n",
    "GT_test_folder = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/labels/\"\n",
    "EST_test_foldery5 = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/yolov5_inference/predict_test/labels/\"\n",
    "EST_test_foldery8 = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/yolov8_inference/predict_test/labels/\"\n",
    "\n",
    "print(\"\\nYOLOv5 Test:\\n\")\n",
    "ap_test_y5, rec_test_y5, iou_test_y5 = evaluate_yolo_folders(GT_test_folder, EST_test_foldery5)\n",
    "print(\"\\nYOLOv8 Test:\\n\")\n",
    "ap_test_y8, rec_test_y8, iou_test_y8 = evaluate_yolo_folders(GT_test_folder, EST_test_foldery8)\n",
    "\n",
    "print(\"\\n\\n##################################\")\n",
    "#################### Train #####################\n",
    "GT_train_folder = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/labels/\"\n",
    "EST_train_foldery5 = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/yolov5_inference/predict_train/labels/\"\n",
    "EST_train_foldery8 = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/yolov8_inference/predict_train/labels/\"\n",
    "\n",
    "print(\"\\nYOLOv5 Train:\\n\")\n",
    "ap_train_y5, rec_train_y5, iou_train_y5 = evaluate_yolo_folders(GT_train_folder, EST_train_foldery5)\n",
    "print(\"\\nYOLOv8 Train:\\n\")\n",
    "ap_train_y8, rec_train_y8, iou_train_y8 = evaluate_yolo_folders(GT_train_folder, EST_train_foldery8)\n",
    "\n",
    "print(\"\\n\\n##################################\")\n",
    "#################### Val #####################\n",
    "GT_val_folder = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/labels/\"\n",
    "EST_val_foldery5 = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/yolov5_inference/predict_val/labels/\"\n",
    "EST_val_foldery8 = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/yolov8_inference/predict_val/labels/\"\n",
    "\n",
    "print(\"\\nYOLOv5 Val:\\n\")\n",
    "ap_val_y5, rec_val_y5, iou_val_y5 = evaluate_yolo_folders(GT_val_folder, EST_val_foldery5)\n",
    "print(\"\\nYOLOv8 Val:\\n\")\n",
    "ap_val_y8, rec_val_y8, iou_val_y8 = evaluate_yolo_folders(GT_val_folder, EST_val_foldery8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "def convert_json_to_yolo(json_folder_path, output_folder_path):\n",
    "    # Create output folder if it doesn't exist\n",
    "    os.makedirs(output_folder_path, exist_ok=True)\n",
    "\n",
    "    # List all JSON files in the input folder\n",
    "    json_files = [f for f in os.listdir(json_folder_path) if f.endswith('.json')]\n",
    "\n",
    "    for json_file in json_files:\n",
    "        # Load the JSON file\n",
    "        json_path = os.path.join(json_folder_path, json_file)\n",
    "        with open(json_path, 'r') as f:\n",
    "            data = json.load(f)\n",
    "\n",
    "        # Extract relevant information\n",
    "        labels = data['labels']\n",
    "        scores = data['scores']\n",
    "        bboxes = data['bboxes']\n",
    "\n",
    "        # Create YOLO annotation format and filter based on score constraint\n",
    "        yolo_lines = []\n",
    "        for label, score, bbox in zip(labels, scores, bboxes):\n",
    "            if score >= 0.3:\n",
    "                x_center = (bbox[0] + bbox[2]) / 2\n",
    "                y_center = (bbox[1] + bbox[3]) / 2\n",
    "                width = bbox[2] - bbox[0]\n",
    "                height = bbox[3] - bbox[1]\n",
    "\n",
    "                # Normalize coordinates to be in the range [0, 1]\n",
    "                x_center /= 600\n",
    "                y_center /= 600\n",
    "                width /= 600\n",
    "                height /= 600\n",
    "\n",
    "                yolo_line = f\"{label} {x_center} {y_center} {width} {height}\"\n",
    "                yolo_lines.append(yolo_line)\n",
    "\n",
    "        # Save the YOLO annotation to a text file\n",
    "        output_path = os.path.join(output_folder_path, os.path.splitext(json_file)[0] + '.txt')\n",
    "        with open(output_path, 'w') as f:\n",
    "            f.write('\\n'.join(yolo_lines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "######### SSD ##########\n",
    "json_folder_path_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_test/preds\"\n",
    "output_folder_path_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_test/labels\"\n",
    "convert_json_to_yolo(json_folder_path_ssd, output_folder_path_ssd)\n",
    "\n",
    "json_folder_path_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_train/preds\"\n",
    "output_folder_path_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_train/labels\"\n",
    "convert_json_to_yolo(json_folder_path_ssd, output_folder_path_ssd)\n",
    "\n",
    "json_folder_path_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_val/preds\"\n",
    "output_folder_path_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_val/labels\"\n",
    "convert_json_to_yolo(json_folder_path_ssd, output_folder_path_ssd)\n",
    "\n",
    "######### Faster RCNN ##########\n",
    "json_folder_path_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_test/preds\"\n",
    "output_folder_path_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_test/labels\"\n",
    "convert_json_to_yolo(json_folder_path_fasterrcnn, output_folder_path_fasterrcnn)\n",
    "\n",
    "json_folder_path_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_train/preds\"\n",
    "output_folder_path_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_train/labels\"\n",
    "convert_json_to_yolo(json_folder_path_fasterrcnn, output_folder_path_fasterrcnn)\n",
    "\n",
    "json_folder_path_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_val/preds\"\n",
    "output_folder_path_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_val/labels\"\n",
    "convert_json_to_yolo(json_folder_path_fasterrcnn, output_folder_path_fasterrcnn)\n",
    "\n",
    "######### CenterNet ##########\n",
    "json_folder_path_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_test/preds\"\n",
    "output_folder_path_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_test/labels\"\n",
    "convert_json_to_yolo(json_folder_path_centernet, output_folder_path_centernet)\n",
    "\n",
    "json_folder_path_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_train/preds\"\n",
    "output_folder_path_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_train/labels\"\n",
    "convert_json_to_yolo(json_folder_path_centernet, output_folder_path_centernet)\n",
    "\n",
    "json_folder_path_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_val/preds\"\n",
    "output_folder_path_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_val/labels\"\n",
    "convert_json_to_yolo(json_folder_path_centernet, output_folder_path_centernet)\n",
    "\n",
    "\n",
    "######### RetinaNet ##########\n",
    "json_folder_path_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_test/preds\"\n",
    "output_folder_path_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_test/labels\"\n",
    "convert_json_to_yolo(json_folder_path_retinanet, output_folder_path_retinanet)\n",
    "\n",
    "json_folder_path_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_train/preds\"\n",
    "output_folder_path_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_train/labels\"\n",
    "convert_json_to_yolo(json_folder_path_retinanet, output_folder_path_retinanet)\n",
    "\n",
    "json_folder_path_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_val/preds\"\n",
    "output_folder_path_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_val/labels\"\n",
    "convert_json_to_yolo(json_folder_path_retinanet, output_folder_path_retinanet)\n",
    "\n",
    "\n",
    "######### DETR ##########\n",
    "json_folder_path_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_test/preds\"\n",
    "output_folder_path_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_test/labels\"\n",
    "convert_json_to_yolo(json_folder_path_detr, output_folder_path_detr)\n",
    "\n",
    "json_folder_path_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_train/preds\"\n",
    "output_folder_path_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_train/labels\"\n",
    "convert_json_to_yolo(json_folder_path_detr, output_folder_path_detr)\n",
    "\n",
    "json_folder_path_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_val/preds\"\n",
    "output_folder_path_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_val/labels\"\n",
    "convert_json_to_yolo(json_folder_path_detr, output_folder_path_detr)\n",
    "\n",
    "\n",
    "######### RTMdet ##########\n",
    "json_folder_path_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_test/preds\"\n",
    "output_folder_path_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_test/labels\"\n",
    "convert_json_to_yolo(json_folder_path_rtmdet, output_folder_path_rtmdet)\n",
    "\n",
    "json_folder_path_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_train/preds\"\n",
    "output_folder_path_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_train/labels\"\n",
    "convert_json_to_yolo(json_folder_path_rtmdet, output_folder_path_rtmdet)\n",
    "\n",
    "json_folder_path_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_val/preds\"\n",
    "output_folder_path_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_val/labels\"\n",
    "convert_json_to_yolo(json_folder_path_rtmdet, output_folder_path_rtmdet)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SSD Test:\n",
      "\n",
      "Overall Mean Precision: 0.5937\n",
      "Overall Mean Recall: 0.3864\n",
      "Overall Mean IoU: 0.4919\n",
      "\n",
      "\n",
      "Faster RCNN Test:\n",
      "\n",
      "Overall Mean Precision: 0.5728\n",
      "Overall Mean Recall: 0.4215\n",
      "Overall Mean IoU: 0.4726\n",
      "\n",
      "\n",
      "RetinaNet Test:\n",
      "\n",
      "Overall Mean Precision: 0.8186\n",
      "Overall Mean Recall: 0.5660\n",
      "Overall Mean IoU: 0.6469\n",
      "\n",
      "\n",
      "CenterNet Test:\n",
      "\n",
      "Overall Mean Precision: 0.5661\n",
      "Overall Mean Recall: 0.4337\n",
      "Overall Mean IoU: 0.4734\n",
      "\n",
      "\n",
      "DETR Test:\n",
      "\n",
      "Overall Mean Precision: 0.7050\n",
      "Overall Mean Recall: 0.6103\n",
      "Overall Mean IoU: 0.5532\n",
      "\n",
      "\n",
      "RTMdet Test:\n",
      "\n",
      "Overall Mean Precision: 0.8631\n",
      "Overall Mean Recall: 0.6702\n",
      "Overall Mean IoU: 0.6715\n",
      "\n",
      "\n",
      "#####################################################################################\n",
      "\n",
      "SSD Train:\n",
      "\n",
      "Overall Mean Precision: 0.6525\n",
      "Overall Mean Recall: 0.4316\n",
      "Overall Mean IoU: 0.5169\n",
      "\n",
      "\n",
      "Faster RCNN Train:\n",
      "\n",
      "Overall Mean Precision: 0.7137\n",
      "Overall Mean Recall: 0.4125\n",
      "Overall Mean IoU: 0.5640\n",
      "\n",
      "\n",
      "RetinaNet Train:\n",
      "\n",
      "Overall Mean Precision: 0.8297\n",
      "Overall Mean Recall: 0.6202\n",
      "Overall Mean IoU: 0.6524\n",
      "\n",
      "\n",
      "CenterNet Train:\n",
      "\n",
      "Overall Mean Precision: 0.7416\n",
      "Overall Mean Recall: 0.4414\n",
      "Overall Mean IoU: 0.5926\n",
      "\n",
      "\n",
      "DETR Train:\n",
      "\n",
      "Overall Mean Precision: 0.7250\n",
      "Overall Mean Recall: 0.6359\n",
      "Overall Mean IoU: 0.5764\n",
      "\n",
      "\n",
      "RTMdet Train:\n",
      "\n",
      "Overall Mean Precision: 0.8503\n",
      "Overall Mean Recall: 0.6854\n",
      "Overall Mean IoU: 0.6759\n",
      "\n",
      "\n",
      "#####################################################################################\n",
      "\n",
      "SSD Val:\n",
      "\n",
      "Overall Mean Precision: 0.6376\n",
      "Overall Mean Recall: 0.4964\n",
      "Overall Mean IoU: 0.5159\n",
      "\n",
      "\n",
      "Faster RCNN Val:\n",
      "\n",
      "Overall Mean Precision: 0.7255\n",
      "Overall Mean Recall: 0.4908\n",
      "Overall Mean IoU: 0.5636\n",
      "\n",
      "\n",
      "RetinaNet Val:\n",
      "\n",
      "Overall Mean Precision: 0.8145\n",
      "Overall Mean Recall: 0.6444\n",
      "Overall Mean IoU: 0.6411\n",
      "\n",
      "\n",
      "CenterNet Val:\n",
      "\n",
      "Overall Mean Precision: 0.7583\n",
      "Overall Mean Recall: 0.5361\n",
      "Overall Mean IoU: 0.6030\n",
      "\n",
      "\n",
      "DETR Val:\n",
      "\n",
      "Overall Mean Precision: 0.7183\n",
      "Overall Mean Recall: 0.6801\n",
      "Overall Mean IoU: 0.5581\n",
      "\n",
      "\n",
      "RTMdet Val:\n",
      "\n",
      "Overall Mean Precision: 0.8355\n",
      "Overall Mean Recall: 0.7180\n",
      "Overall Mean IoU: 0.6644\n",
      "\n",
      "\n",
      "#####################################################################################\n",
      "\n",
      "\n",
      "#####################################################################################\n"
     ]
    }
   ],
   "source": [
    "######################################### Test ##########################################\n",
    "GT_test_folder = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/test/labels/\"\n",
    "\n",
    "#################### SSD\n",
    "EST_test_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_test/labels/\"\n",
    "print(\"\\nSSD Test:\\n\")\n",
    "ap_test_ssd, rec_test_ssd, iou_test_ssd = evaluate_yolo_folders(GT_test_folder, EST_test_ssd)\n",
    "\n",
    "#################### Faster RCNN\n",
    "EST_test_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_test/labels/\"\n",
    "print(\"\\n\\nFaster RCNN Test:\\n\")\n",
    "ap_test_fasterrcnn, rec_test_fasterrcnn, iou_test_fasterrcnn = evaluate_yolo_folders(GT_test_folder, EST_test_fasterrcnn)\n",
    "\n",
    "#################### RetinaNet\n",
    "EST_test_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_test/labels/\"\n",
    "print(\"\\n\\nRetinaNet Test:\\n\")\n",
    "ap_test_retinanet, rec_test_retinanet, iou_test_retinanet = evaluate_yolo_folders(GT_test_folder, EST_test_retinanet)\n",
    "\n",
    "#################### CenterNet\n",
    "EST_test_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_test/labels/\"\n",
    "print(\"\\n\\nCenterNet Test:\\n\")\n",
    "ap_test_centernet, rec_test_centernet, iou_test_centernet = evaluate_yolo_folders(GT_test_folder, EST_test_centernet)\n",
    "\n",
    "#################### DETR\n",
    "EST_test_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_test/labels/\"\n",
    "print(\"\\n\\nDETR Test:\\n\")\n",
    "ap_test_detr, rec_test_detr, iou_test_detr = evaluate_yolo_folders(GT_test_folder, EST_test_detr)\n",
    "\n",
    "#################### RTMdet\n",
    "EST_test_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_test/labels/\"\n",
    "print(\"\\n\\nRTMdet Test:\\n\")\n",
    "ap_test_rtmdet, rec_test_rtmdet, iou_test_rtmdet = evaluate_yolo_folders(GT_test_folder, EST_test_rtmdet)\n",
    "\n",
    "\n",
    "print(\"\\n\\n#####################################################################################\")\n",
    "########################################## Train ##########################################\n",
    "GT_train_folder = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/train/labels/\"\n",
    "\n",
    "#################### SSD\n",
    "EST_train_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_train/labels/\"\n",
    "print(\"\\nSSD Train:\\n\")\n",
    "ap_train_ssd, rec_train_ssd, iou_train_ssd = evaluate_yolo_folders(GT_train_folder, EST_train_ssd)\n",
    "\n",
    "#################### Faster RCNN\n",
    "EST_train_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_train/labels/\"\n",
    "print(\"\\n\\nFaster RCNN Train:\\n\")\n",
    "ap_train_fasterrcnn, rec_train_fasterrcnn, iou_train_fasterrcnn = evaluate_yolo_folders(GT_train_folder, EST_train_fasterrcnn)\n",
    "\n",
    "#################### RetinaNet\n",
    "EST_train_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_train/labels/\"\n",
    "print(\"\\n\\nRetinaNet Train:\\n\")\n",
    "ap_train_retinanet, rec_train_retinanet, iou_train_retinanet = evaluate_yolo_folders(GT_train_folder, EST_train_retinanet)\n",
    "\n",
    "#################### CenterNet\n",
    "EST_train_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_train/labels/\"\n",
    "print(\"\\n\\nCenterNet Train:\\n\")\n",
    "ap_train_centernet, rec_train_centernet, iou_train_centernet = evaluate_yolo_folders(GT_train_folder, EST_train_centernet)\n",
    "\n",
    "#################### DETR\n",
    "EST_train_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_train/labels/\"\n",
    "print(\"\\n\\nDETR Train:\\n\")\n",
    "ap_train_detr, rec_train_detr, iou_train_detr = evaluate_yolo_folders(GT_train_folder, EST_train_detr)\n",
    "\n",
    "#################### RTMdet\n",
    "EST_train_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_train/labels/\"\n",
    "print(\"\\n\\nRTMdet Train:\\n\")\n",
    "ap_train_rtmdet, rec_train_rtmdet, iou_train_rtmdet = evaluate_yolo_folders(GT_train_folder, EST_train_rtmdet)\n",
    "\n",
    "\n",
    "print(\"\\n\\n#####################################################################################\")\n",
    "######################################### Val ##########################################\n",
    "GT_val_folder = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_dataset/valid/labels/\"\n",
    "\n",
    "#################### SSD\n",
    "EST_val_ssd = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/ssd_inference/predict_val/labels/\"\n",
    "print(\"\\nSSD Val:\\n\")\n",
    "ap_val_ssd, rec_val_ssd, iou_val_ssd = evaluate_yolo_folders(GT_val_folder, EST_val_ssd)\n",
    "\n",
    "#################### Faster RCNN\n",
    "EST_val_fasterrcnn = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/faster-rcnn_inference/predict_val/labels/\"\n",
    "print(\"\\n\\nFaster RCNN Val:\\n\")\n",
    "ap_val_fasterrcnn, rec_val_fasterrcnn, iou_val_fasterrcnn = evaluate_yolo_folders(GT_val_folder, EST_val_fasterrcnn)\n",
    "\n",
    "#################### RetinaNet\n",
    "EST_val_retinanet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/retinanet_inference/predict_val/labels/\"\n",
    "print(\"\\n\\nRetinaNet Val:\\n\")\n",
    "ap_val_retinanet, rec_val_retinanet, iou_val_retinanet = evaluate_yolo_folders(GT_val_folder, EST_val_retinanet)\n",
    "\n",
    "#################### CenterNet\n",
    "EST_val_centernet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/centernet_inference/predict_val/labels/\"\n",
    "print(\"\\n\\nCenterNet Val:\\n\")\n",
    "ap_val_centernet, rec_val_centernet, iou_val_centernet = evaluate_yolo_folders(GT_val_folder, EST_val_centernet)\n",
    "\n",
    "#################### DETR\n",
    "EST_val_detr = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/detr_inference/predict_val/labels/\"\n",
    "print(\"\\n\\nDETR Val:\\n\")\n",
    "ap_val_detr, rec_val_detr, iou_val_detr = evaluate_yolo_folders(GT_val_folder, EST_val_detr)\n",
    "\n",
    "#################### RTMdet\n",
    "EST_val_rtmdet = \"/home/safouane/Downloads/FlightScope_Bench/data/GDIT_eval/rtmdet_inference/predict_val/labels/\"\n",
    "print(\"\\n\\nRTMdet Val:\\n\")\n",
    "ap_val_rtmdet, rec_val_rtmdet, iou_val_rtmdet = evaluate_yolo_folders(GT_val_folder, EST_val_rtmdet)\n",
    "\n",
    "\n",
    "print(\"\\n\\n#####################################################################################\")\n",
    "print(\"\\n\\n#####################################################################################\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def get_average_metrics(metrics_dict):\n",
    "    if not isinstance(metrics_dict, dict):\n",
    "        raise TypeError(\"metrics_dict must be a dictionary\")\n",
    "    if 'AP' not in metrics_dict or 'Recall' not in metrics_dict or 'IoU' not in metrics_dict:\n",
    "        raise KeyError(\"metrics_dict must contain 'AP', 'Recall', and 'IoU' keys\")\n",
    "    avg_ap = np.mean([metrics_dict['AP']])\n",
    "    avg_recall = np.mean([metrics_dict['Recall']])\n",
    "    avg_iou = np.mean([metrics_dict['IoU']])\n",
    "    return avg_ap, avg_recall, avg_iou\n",
    "\n",
    "def plot_histogram(metrics_dict, title, save_path=None):\n",
    "    try:\n",
    "        avg_aps, avg_recalls, avg_ious = zip(*[get_average_metrics(metrics_dict[model]) for model in metrics_dict])\n",
    "    except (TypeError, KeyError) as e:\n",
    "        print(f\"Error calculating average metrics: {e}\")\n",
    "        return\n",
    "\n",
    "    models = list(metrics_dict.keys())\n",
    "\n",
    "    bar_width = 0.15\n",
    "    bar_distance = 0.05  \n",
    "\n",
    "    positions = np.arange(len(models))\n",
    "\n",
    "    plt.bar(positions - bar_width - bar_distance, avg_aps, width=bar_width, label='AP', edgecolor='grey', linewidth=0.5)\n",
    "    plt.bar(positions, avg_recalls, width=bar_width, label='Recall', edgecolor='grey', linewidth=0.5)\n",
    "    plt.bar(positions + bar_width + bar_distance, avg_ious, width=bar_width, label='IoU', edgecolor='grey', linewidth=0.5)\n",
    "\n",
    "    plt.title(title, fontsize=24, style='italic')\n",
    "    #plt.xlabel('Models')\n",
    "    #plt.ylabel('Metric Values')\n",
    "    plt.xticks(positions, models)\n",
    "    legend = plt.legend(fontsize=18, loc='upper right')\n",
    "    frame = legend.get_frame()\n",
    "    frame.set_alpha(0.3)\n",
    "    plt.xticks(positions, models, rotation=0, fontsize=24)\n",
    "    plt.yticks(fontsize=24)\n",
    "    plt.tight_layout()\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    plt.ylim(0, 1)\n",
    "    if save_path:\n",
    "        plt.savefig(save_path, format='png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_dict_train = {\n",
    "    'YOLOv8': {'AP': ap_test_y8, 'Recall': rec_test_y8, 'IoU': iou_test_y8},\n",
    "    'YOLOv5': {'AP': ap_test_y5, 'Recall': rec_test_y5, 'IoU': iou_test_y5},\n",
    "    'SSD': {'AP': ap_test_ssd, 'Recall': rec_test_ssd, 'IoU': iou_test_ssd},\n",
    "    'Faster RCNN': {'AP': ap_test_fasterrcnn, 'Recall': rec_test_fasterrcnn, 'IoU': iou_test_fasterrcnn},\n",
    "    'RetinaNet': {'AP': ap_test_retinanet, 'Recall': rec_test_retinanet, 'IoU': iou_test_retinanet},\n",
    "    'CenterNet': {'AP': ap_test_centernet, 'Recall': rec_test_centernet, 'IoU': iou_test_centernet},\n",
    "    'DETR': {'AP': ap_test_detr, 'Recall': rec_test_detr, 'IoU': iou_test_detr},\n",
    "    'RTMdet': {'AP': ap_test_rtmdet, 'Recall': rec_test_rtmdet, 'IoU': iou_test_rtmdet},\n",
    "}\n",
    "\n",
    "metrics_dict_test = {\n",
    "    'YOLOv8': {'AP': ap_train_y8, 'Recall': rec_train_y8, 'IoU': iou_train_y8},\n",
    "    'YOLOv5': {'AP': ap_train_y5, 'Recall': rec_train_y5, 'IoU': iou_train_y5},\n",
    "    'SSD': {'AP': ap_train_ssd, 'Recall': rec_train_ssd, 'IoU': iou_train_ssd},\n",
    "    'Faster RCNN': {'AP': ap_train_fasterrcnn, 'Recall': rec_train_fasterrcnn, 'IoU': iou_train_fasterrcnn},\n",
    "    'RetinaNet': {'AP': ap_train_retinanet, 'Recall': rec_train_retinanet, 'IoU': iou_train_retinanet},\n",
    "    'CenterNet': {'AP': ap_train_centernet, 'Recall': rec_train_centernet, 'IoU': iou_train_centernet},\n",
    "    'DETR': {'AP': ap_train_detr, 'Recall': rec_train_detr, 'IoU': iou_train_detr},\n",
    "    'RTMdet': {'AP': ap_train_rtmdet, 'Recall': rec_train_rtmdet, 'IoU': iou_train_rtmdet},\n",
    "}\n",
    "\n",
    "metrics_dict_val = {\n",
    "    'YOLOv8': {'AP': ap_val_y8, 'Recall': rec_val_y8, 'IoU': iou_val_y8},\n",
    "    'YOLOv5': {'AP': ap_val_y5, 'Recall': rec_val_y5, 'IoU': iou_val_y5},\n",
    "    'SSD': {'AP': ap_val_ssd, 'Recall': rec_val_ssd, 'IoU': iou_val_ssd},\n",
    "    'Faster RCNN': {'AP': ap_val_fasterrcnn, 'Recall': rec_val_fasterrcnn, 'IoU': iou_val_fasterrcnn},\n",
    "    'RetinaNet': {'AP': ap_val_retinanet, 'Recall': rec_val_retinanet, 'IoU': iou_val_retinanet},\n",
    "    'CenterNet': {'AP': ap_val_centernet, 'Recall': rec_val_centernet, 'IoU': iou_val_centernet},\n",
    "    'DETR': {'AP': ap_val_detr, 'Recall': rec_val_detr, 'IoU': iou_val_detr},\n",
    "    'RTMdet': {'AP': ap_val_rtmdet, 'Recall': rec_val_rtmdet, 'IoU': iou_val_rtmdet},\n",
    "}\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "# Subplot train\n",
    "plt.subplot(3, 1, 1)\n",
    "plot_histogram(metrics_dict_train, title='(a)')\n",
    "\n",
    "# Subplot test\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_histogram(metrics_dict_test, title='(b)')\n",
    "\n",
    "# Subplot val\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_histogram(metrics_dict_val, title='(c)', save_path='output_histogram.png')\n",
    "\n",
    "# Set x-label and y-label for the middle subplot\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.xlabel('Models', fontsize=25)\n",
    "plt.ylabel('Metric Values', fontsize=24)\n",
    "\n",
    "# Adjust space between subplots\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "\n",
    "plt.savefig('output_histogram.pdf', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: \n",
      "\n",
      "{'YOLOv8': {'AP': 0.9188191096085833, 'Recall': 0.686368352054362, 'IoU': 0.7108205871654029}, 'YOLOv5': {'AP': 0.9684080573043611, 'Recall': 0.6607032910873588, 'IoU': 0.7398717050106715}, 'SSD': {'AP': 0.5937471884840306, 'Recall': 0.38636773155485743, 'IoU': 0.4918626628499067}, 'Faster RCNN': {'AP': 0.5728135698723934, 'Recall': 0.42152014144886574, 'IoU': 0.4726056893107611}, 'RetinaNet': {'AP': 0.8185757221440613, 'Recall': 0.5659981933064663, 'IoU': 0.6469294090394961}, 'CenterNet': {'AP': 0.5660561871807547, 'Recall': 0.4337292480944571, 'IoU': 0.4734027753669999}, 'DETR': {'AP': 0.704969573015011, 'Recall': 0.6103047649478457, 'IoU': 0.55316445721604}, 'RTMdet': {'AP': 0.8630754947666712, 'Recall': 0.6702188487940449, 'IoU': 0.6714638404033594}}\n",
      "\n",
      "\n",
      "####################\n",
      "Test: \n",
      "\n",
      "{'YOLOv8': {'AP': 0.9026649088320698, 'Recall': 0.7094760120951376, 'IoU': 0.7044201033646218}, 'YOLOv5': {'AP': 0.9558997359271018, 'Recall': 0.7048267412721908, 'IoU': 0.7523215410661063}, 'SSD': {'AP': 0.652485492765368, 'Recall': 0.43162406067630477, 'IoU': 0.5169296049477569}, 'Faster RCNN': {'AP': 0.7137477965485157, 'Recall': 0.41249415325914124, 'IoU': 0.563956765464449}, 'RetinaNet': {'AP': 0.8296796874271244, 'Recall': 0.6202193840450018, 'IoU': 0.6524494502368909}, 'CenterNet': {'AP': 0.7416398930070481, 'Recall': 0.44139625002965, 'IoU': 0.5926487504578349}, 'DETR': {'AP': 0.7250024201252498, 'Recall': 0.6359473541196733, 'IoU': 0.5764149925560332}, 'RTMdet': {'AP': 0.8502552264057712, 'Recall': 0.685377455417773, 'IoU': 0.6759222809388442}}\n",
      "\n",
      "\n",
      "####################\n",
      "Val: \n",
      "\n",
      "{'YOLOv8': {'AP': 0.8999609277319327, 'Recall': 0.7808670384713652, 'IoU': 0.6959616934904256}, 'YOLOv5': {'AP': 0.9420243518510453, 'Recall': 0.7694814822200962, 'IoU': 0.7417507747375267}, 'SSD': {'AP': 0.6375861983663009, 'Recall': 0.49636365355622436, 'IoU': 0.5158725734338334}, 'Faster RCNN': {'AP': 0.7254902792965076, 'Recall': 0.49083557755935664, 'IoU': 0.5636080939095005}, 'RetinaNet': {'AP': 0.8145293619271216, 'Recall': 0.6444086685630913, 'IoU': 0.6410838873156173}, 'CenterNet': {'AP': 0.7582613600995954, 'Recall': 0.5361366799478486, 'IoU': 0.6030230573829665}, 'DETR': {'AP': 0.7183345520391653, 'Recall': 0.6800956971290661, 'IoU': 0.5580716787868188}, 'RTMdet': {'AP': 0.8354765383504907, 'Recall': 0.7179930308519498, 'IoU': 0.6643856117044729}}\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Train: \\n')\n",
    "print(metrics_dict_train)\n",
    "print('\\n\\n####################')\n",
    "print('Test: \\n')\n",
    "print(metrics_dict_test)\n",
    "print('\\n\\n####################')\n",
    "print('Val: \\n')\n",
    "print(metrics_dict_val)\n",
    "print('\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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