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fix issues: #1 ,yolov6 infer update
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‎configs/yolov5u/README.md

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# YOLOv5u
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## 模型库
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### 基础模型
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| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
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| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
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| YOLOv5u-n | 640 | 16 | 300e | 1.61 | 34.5 | 49.7 | 2.65 | 7.79 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_n_300e_coco.pdparams) | [配置文件](./yolov5u_n_300e_coco.yml) |
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| YOLOv5u-s | 640 | 16 | 300e | 2.66 | 43.0 | 59.7 | 9.15 | 24.12 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_s_300e_coco.pdparams) | [配置文件](./yolov5u_s_300e_coco.yml) |
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| YOLOv5u-m | 640 | 16 | 300e | 5.50 | 49.0 | 65.7 | 25.11 | 64.42 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_m_300e_coco.pdparams) | [配置文件](./yolov5u_m_300e_coco.yml) |
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| YOLOv5u-l | 640 | 16 | 300e | 8.73 | 52.2 | 69.0 | 53.23 | 135.34 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_l_300e_coco.pdparams) | [配置文件](./yolov5u_l_300e_coco.yml) |
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| YOLOv5u-x | 640 | 16 | 300e | 15.49 | 53.1 | 69.9 | 97.28 | 246.89 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5u_x_300e_coco.pdparams) | [配置文件](./yolov5u_x_300e_coco.yml) |
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### P6大尺度模型
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| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
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| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
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| YOLOv5u_p6-n | 1280 | 16 | 300e | - | 42.2 | 58.6 | 4.33 | 15.79 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5up6_n_300e_coco.pdparams) | [配置文件](./yolov5up6_n_300e_coco.yml) |
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| YOLOv5u_p6-s | 1280 | 16 | 300e | - | 48.6 | 65.8 | 15.31 | 49.02 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5up6_s_300e_coco.pdparams) | [配置文件](./yolov5up6_s_300e_coco.yml) |
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| YOLOv5u_p6-m | 1280 | 16 | 300e | - | 53.3 | 70.3 | 41.22 | 131.06 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5up6_m_300e_coco.pdparams) | [配置文件](./yolov5up6_m_300e_coco.yml) |
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| YOLOv5u_p6-l | 1280 | 8 | 300e | - | 55.4 | 72.2 | 86.10 | 275.38 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5up6_l_300e_coco.pdparams) | [配置文件](./yolov5up6_l_300e_coco.yml) |
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| YOLOv5u_p6-x | 1280 | 8 | 300e | - | 56.4 | 73.1 | 155.54 | 502.38 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5up6_x_300e_coco.pdparams) | [配置文件](./yolov5p6_x_3yolov5up6_x_300e_coco00e_coco.yml) |
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**注意:**
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- YOLOv5u 模型表示YOLOv5使用YOLOv8的head和loss，是Anchor Free的检测方案；
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- YOLOv5u 模型训练使用COCO train2017作为训练集，Box AP为在COCO val2017上的`mAP(IoU=0.5:0.95)`结果；
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- 使用教程可参照[YOLOv5](../yolov5)；

‎configs/yolov5u/_base_/optimizer_300e.yml

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epoch: 300
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LearningRate:
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base_lr: 0.01
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schedulers:
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- !YOLOv5LRDecay
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max_epochs: 300
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min_lr_ratio: 0.01
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- !ExpWarmup
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epochs: 3
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OptimizerBuilder:
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optimizer:
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type: Momentum
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momentum: 0.937
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use_nesterov: True
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regularizer:
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factor: 0.001
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type: L2

‎configs/yolov5u/_base_/optimizer_300e_high.yml

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epoch: 300
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LearningRate:
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base_lr: 0.01
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schedulers:
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- !YOLOv5LRDecay
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max_epochs: 300
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min_lr_ratio: 0.1
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- !ExpWarmup
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epochs: 3
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OptimizerBuilder:
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optimizer:
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type: Momentum
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momentum: 0.937
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use_nesterov: True
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regularizer:
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factor: 0.001
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type: L2

‎configs/yolov5u/_base_/yolov5u_cspdarknet.yml

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architecture: YOLOv5
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norm_type: sync_bn
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use_ema: True
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ema_decay: 0.9999
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ema_decay_type: "exponential"
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act: silu
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find_unused_parameters: True
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depth_mult: 1.0
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width_mult: 1.0
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YOLOv5:
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backbone: CSPDarkNet
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neck: YOLOCSPPAN
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yolo_head: YOLOv8Head
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post_process: ~
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CSPDarkNet:
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arch: "P5"
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return_idx: [2, 3, 4]
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depthwise: false
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YOLOCSPPAN:
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depthwise: false
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YOLOv8Head:
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fpn_strides: [8, 16, 32]
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loss_weight: {class: 0.5, iou: 7.5, dfl: 1.5}
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assigner:
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name: TaskAlignedAssigner
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topk: 10
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alpha: 0.5
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beta: 6.0
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nms:
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name: MultiClassNMS
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nms_top_k: 3000
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keep_top_k: 300
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score_threshold: 0.001
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nms_threshold: 0.7

‎configs/yolov5u/_base_/yolov5u_reader.yml

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input_height: &input_height 640
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input_width: &input_width 640
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input_size: &input_size [*input_height, *input_width]
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mosaic_epoch: &mosaic_epoch 290 # last 10 epochs close mosaic, totally 300 epochs as default
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worker_num: 4
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TrainReader:
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sample_transforms:
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- Decode: {}
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- MosaicPerspective: {mosaic_prob: 1.0, target_size: *input_size}
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- RandomHSV: {hgain: 0.015, sgain: 0.7, vgain: 0.4}
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- RandomFlip: {}
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batch_transforms:
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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- PadGT: {}
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batch_size: 8
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shuffle: True
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drop_last: False
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use_shared_memory: True
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collate_batch: True
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mosaic_epoch: *mosaic_epoch
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EvalReader:
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
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- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 8
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TestReader:
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inputs_def:
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image_shape: [3, 640, 640]
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
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- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1
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fuse_normalize: False

‎configs/yolov5u/_base_/yolov5u_reader_high_aug.yml

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input_height: &input_height 640
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input_width: &input_width 640
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input_size: &input_size [*input_height, *input_width]
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mosaic_epoch: &mosaic_epoch 290 # last 10 epochs close mosaic, totally 300 epochs as default
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worker_num: 4
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TrainReader:
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sample_transforms:
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- Decode: {}
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- MosaicPerspective: {mosaic_prob: 1.0, target_size: *input_size, scale: 0.9, mixup_prob: 0.1, copy_paste_prob: 0.1}
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- RandomHSV: {hgain: 0.015, sgain: 0.7, vgain: 0.4}
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- RandomFlip: {}
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batch_transforms:
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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- PadGT: {}
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batch_size: 8
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shuffle: True
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drop_last: False
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use_shared_memory: True
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collate_batch: True
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mosaic_epoch: *mosaic_epoch
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EvalReader:
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
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- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 8
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TestReader:
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inputs_def:
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image_shape: [3, 640, 640]
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
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- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1
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fuse_normalize: False

‎configs/yolov5u/_base_/yolov5up6_cspdarknet.yml

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_BASE_: [
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'yolov5u_cspdarknet.yml',
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]
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CSPDarkNet:
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arch: "P6"
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return_idx: [2, 3, 4, 5]
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YOLOCSPPAN:
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depthwise: false
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YOLOv8Head:
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fpn_strides: [8, 16, 32, 64]

‎configs/yolov5u/_base_/yolov5up6_reader.yml

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input_height: &input_height 1280
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input_width: &input_width 1280
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input_size: &input_size [*input_height, *input_width]
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mosaic_epoch: &mosaic_epoch 290 # last 10 epochs close mosaic, totally 300 epochs as default
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worker_num: 4
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TrainReader:
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sample_transforms:
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- Decode: {}
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- MosaicPerspective: {mosaic_prob: 1.0, target_size: *input_size}
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- RandomHSV: {hgain: 0.015, sgain: 0.7, vgain: 0.4}
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- RandomFlip: {}
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batch_transforms:
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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- PadGT: {}
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batch_size: 8
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shuffle: True
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drop_last: False
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use_shared_memory: True
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collate_batch: True
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mosaic_epoch: *mosaic_epoch
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EvalReader:
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
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- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1
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TestReader:
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inputs_def:
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image_shape: [3, 1280, 1280]
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sample_transforms:
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- Decode: {}
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- Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
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- Pad: {size: *input_size, fill_value: [114., 114., 114.]}
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- NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
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- Permute: {}
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batch_size: 1
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fuse_normalize: False

‎configs/yolov5u/yolov5u_l_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e_high.yml',
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'_base_/yolov5u_cspdarknet.yml',
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'_base_/yolov5u_reader_high_aug.yml',
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]
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depth_mult: 1.0
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width_mult: 1.0
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5u_l_300e_coco/model_final
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TrainReader:
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batch_size: 16 # default 8 gpus, total bs = 128

‎configs/yolov5u/yolov5u_m_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e_high.yml',
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'_base_/yolov5u_cspdarknet.yml',
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'_base_/yolov5u_reader_high_aug.yml',
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]
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depth_mult: 0.67
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width_mult: 0.75
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5u_m_300e_coco/model_final
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TrainReader:
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batch_size: 16 # default 8 gpus, total bs = 128

‎configs/yolov5u/yolov5u_n_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e.yml',
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'_base_/yolov5u_cspdarknet.yml',
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'_base_/yolov5u_reader.yml',
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]
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depth_mult: 0.33
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width_mult: 0.25
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5u_n_300e_coco/model_final
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TrainReader:
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batch_size: 16 # default 8 gpus, total bs = 128

‎configs/yolov5u/yolov5u_s_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e.yml',
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'_base_/yolov5u_cspdarknet.yml',
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'_base_/yolov5u_reader.yml',
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]
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depth_mult: 0.33
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width_mult: 0.50
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5u_s_300e_coco/model_final
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TrainReader:
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batch_size: 16 # default 8 gpus, total bs = 128

‎configs/yolov5u/yolov5u_x_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e_high.yml',
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'_base_/yolov5u_cspdarknet.yml',
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'_base_/yolov5u_reader_high_aug.yml',
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]
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depth_mult: 1.33
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width_mult: 1.25
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5u_x_300e_coco/model_final
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TrainReader:
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batch_size: 16 # default 8 gpus, total bs = 128

‎configs/yolov5u/yolov5up6_l_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e_high.yml',
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'_base_/yolov5up6_cspdarknet.yml',
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'_base_/yolov5up6_reader.yml',
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]
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depth_mult: 1.0
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width_mult: 1.0
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5up6_l_300e_coco/model_final
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TrainReader:
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batch_size: 8 # default 8 gpus, total bs = 64

‎configs/yolov5u/yolov5up6_m_300e_coco.yml

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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'_base_/optimizer_300e_high.yml',
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'_base_/yolov5up6_cspdarknet.yml',
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'_base_/yolov5up6_reader.yml',
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]
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depth_mult: 0.67
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width_mult: 0.75
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log_iter: 100
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snapshot_epoch: 10
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weights: output/yolov5up6_m_300e_coco/model_final
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TrainReader:
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batch_size: 16 # default 8 gpus, total bs = 128

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