siton bug

configs/yolov5_seg/_base_/yolov5_seg_reader_high_aug.yml

+input_height: &input_height 640
+input_width: &input_width 640
+input_size: &input_size [*input_height, *input_width]
+mosaic_epoch: &mosaic_epoch 300
+
+worker_num: 4
+TrainReader:
+  sample_transforms:
+    - DecodeNormResizeMask: {target_size: *input_size, mosaic: True}
+    - MosaicPerspective: {mosaic_prob: 0.0, target_size: *input_size, scale: 0.9, mixup_prob: 0.1, copy_paste_prob: 0.1, with_mask: True}
+    - Poly2Mask: {del_poly: True}
+    - RandomHSV: {hgain: 0.015, sgain: 0.7, vgain: 0.4}
+    - RandomFlip: {}
+    - BboxXYXY2XYWH: {}
+    - NormalizeBox: {}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - RGBReverse: {} # bgr->rgb
+    - Permute: {}
+  batch_size: 8
+  shuffle: True
+  drop_last: False
+  use_shared_memory: False
+  collate_batch: False
+  mosaic_epoch: *mosaic_epoch
+
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
+    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: *input_size, keep_ratio: True, interp: 1}
+    - Pad: {size: *input_size, fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+  fuse_normalize: False

configs/yolov5_seg/yolov5_seg_l_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_seg_cspdarknet.yml',
+  '_base_/yolov5_seg_reader_high_aug.yml',
+]
+depth_mult: 1.0
+width_mult: 1.0
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_seg_l_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5_seg/yolov5_seg_m_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_seg_cspdarknet.yml',
+  '_base_/yolov5_seg_reader_high_aug.yml',
+]
+depth_mult: 0.67
+width_mult: 0.75
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_seg_m_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5_seg/yolov5_seg_n_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/yolov5_seg_cspdarknet.yml',
+  '_base_/yolov5_seg_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.25
+
+log_iter: 50
+snapshot_epoch: 10
+weights: output/yolov5_seg_n_300e_coco/model_final

configs/yolov5_seg/yolov5_seg_s_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/yolov5_seg_cspdarknet.yml',
+  '_base_/yolov5_seg_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_seg_s_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1

configs/yolov5_seg/yolov5_seg_x_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_seg_cspdarknet.yml',
+  '_base_/yolov5_seg_reader_high_aug.yml',
+]
+depth_mult: 1.33
+width_mult: 1.25
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_seg_x_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+EvalReader:
+  batch_size: 1
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5u/README.md

+# YOLOv5u
+
+## 模型库
+
+### 基础模型
+
+| 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 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
+| 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) |
+| 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) |
+| 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) |
+| 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) |
+| 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) |
+
+### P6大尺度模型
+
+| 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 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
+| 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) |
+| 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) |
+| 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) |
+| 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) |
+| 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) |
+
+
+**注意:**
+  - YOLOv5u 模型表示YOLOv5使用YOLOv8的head和loss，是Anchor Free的检测方案；
+  - 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；
+  - 使用教程可参照[YOLOv5](../yolov5)；

configs/yolov5u/_base_/optimizer_300e.yml

+epoch: 300
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 300
+    min_lr_ratio: 0.01
+  - !ExpWarmup
+    epochs: 5 #3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.001 # 0.0005 in yolov8
+    type: L2

configs/yolov5u/_base_/optimizer_300e_high.yml

+epoch: 300
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 300
+    min_lr_ratio: 0.1 #
+  - !ExpWarmup
+    epochs: 5 #3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.001 # 0.0005 in yolov8
+    type: L2

configs/yolov5u/_base_/yolov5u_cspdarknet.yml

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

configs/yolov5u/_base_/yolov5u_reader.yml

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

configs/yolov5u/_base_/yolov5u_reader_high_aug.yml

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

configs/yolov5u/_base_/yolov5up6_cspdarknet.yml

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

configs/yolov5u/_base_/yolov5up6_reader.yml

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

configs/yolov5u/yolov5u_l_300e_coco.yml

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

configs/yolov5u/yolov5u_m_300e_coco.yml

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

configs/yolov5u/yolov5u_n_300e_coco.yml

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

configs/yolov5u/yolov5u_s_300e_coco.yml

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

configs/yolov5u/yolov5u_x_300e_coco.yml

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

configs/yolov5u/yolov5up6_l_300e_coco.yml

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