siton bug

configs/yolov5/_base_/yolov5p2_cspdarknet.yml

+_BASE_: [
+  'yolov5_cspdarknet.yml',
+]
+
+CSPDarkNet:
+  arch: "P5"
+  return_idx: [1, 2, 3, 4]
+
+YOLOv5Head:
+  anchors: [[5, 6], [8, 14], [15, 11],
+            [10, 13], [16, 30], [33, 23],
+            [30, 61], [62, 45], [59, 119],
+            [116, 90], [156, 198], [373, 326]]
+  anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]
+  stride: [4, 8, 16, 32]
+
+YOLOv5Loss:
+  downsample_ratios: [4, 8, 16, 32]
+  balance: [4.0, 1.0, 0.25, 0.06]

configs/yolov5/_base_/yolov5p6_cspdarknet.yml

+_BASE_: [
+  'yolov5_cspdarknet.yml',
+]
+
+CSPDarkNet:
+  arch: "P6"
+  return_idx: [2, 3, 4, 5]
+
+YOLOv5Head:
+  anchors: [[19, 27], [44, 40], [38, 94],
+            [96, 68], [86, 152], [180, 137],
+            [140, 301], [303, 264], [238, 542],
+            [436, 615], [739, 380], [925, 792]]
+  anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]
+  stride: [8, 16, 32, 64]
+
+YOLOv5Loss:
+  downsample_ratios: [8, 16, 32, 64]
+  balance: [4.0, 1.0, 0.25, 0.06]
+  box_weight: 0.05
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5/_base_/yolov5p6_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, scale: 0.9, mixup_prob: 0.1, copy_paste_prob: 0.1}
+    - 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}
+    - 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: 2
+
+
+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/yolov5/yolov5_l_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader_high_aug.yml',
+]
+depth_mult: 1.0
+width_mult: 1.0
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_l_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5/yolov5_l_30e_obj365_finetune_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader_high_aug.yml',
+]
+depth_mult: 1.0
+width_mult: 1.0
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/yolov5_l_30e_obj365_finetune_coco/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/yolov5_l_300e_obj365.pdparams
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3
+
+
+epoch: 30
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 30
+    min_lr_ratio: 0.1
+  - !ExpWarmup
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/yolov5/yolov5_m_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader_high_aug.yml',
+]
+depth_mult: 0.67
+width_mult: 0.75
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_m_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5/yolov5_n_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.25
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_n_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov5/yolov5_s_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_s_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov5/yolov5_s_80e_ssod_finetune_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_s_80e_ssod_finetune_coco/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/yolov5_s_300e_ssod_coco.pdparams
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+  - !YOLOv5LRDecay
+    max_epochs: 80
+    min_lr_ratio: 0.01
+  - !ExpWarmup
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    type: Momentum
+    momentum: 0.937
+    use_nesterov: True
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/yolov5/yolov5_x_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5_cspdarknet.yml',
+  '_base_/yolov5_reader_high_aug.yml',
+]
+depth_mult: 1.33
+width_mult: 1.25
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5_x_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128
+
+
+YOLOv5Loss:
+  obj_weight: 0.7
+  cls_weght: 0.3

configs/yolov5/yolov5p6_l_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5p6_cspdarknet.yml',
+  '_base_/yolov5p6_reader.yml',
+]
+depth_mult: 1.0
+width_mult: 1.0
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5p6_l_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 8 # default 8 gpus, total bs = 64

configs/yolov5/yolov5p6_m_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5p6_cspdarknet.yml',
+  '_base_/yolov5p6_reader.yml',
+]
+depth_mult: 0.67
+width_mult: 0.75
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5p6_m_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov5/yolov5p6_n_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5p6_cspdarknet.yml',
+  '_base_/yolov5p6_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.25
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5p6_n_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov5/yolov5p6_s_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5p6_cspdarknet.yml',
+  '_base_/yolov5p6_reader.yml',
+]
+depth_mult: 0.33
+width_mult: 0.50
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5p6_s_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 16 # default 8 gpus, total bs = 128

configs/yolov5/yolov5p6_x_300e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_300e_high.yml',
+  '_base_/yolov5p6_cspdarknet.yml',
+  '_base_/yolov5p6_reader.yml',
+]
+depth_mult: 1.33
+width_mult: 1.25
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/yolov5p6_x_300e_coco/model_final
+
+
+TrainReader:
+  batch_size: 8 # default 8 gpus, total bs = 64

configs/yolov5_seg/README.md

+# YOLOv5 Instance segmentation
+
+## 模型库
+
+### 实例分割模型
+
+| 网络网络        | 输入尺寸   | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) | box AP | mask AP | Params(M) | FLOPs(G) |    下载链接       | 配置文件 |
+| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
+| *YOLOv5-n        |  640     |    16      |   300e   |     -    |  27.6  | - |  2.0  | 7.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_n_300e_coco.pdparams) | [配置文件](./yolov5_seg_n_300e_coco.yml) |
+| *YOLOv5-s        |  640     |    16      |   300e   |     -    |  37.6  | - |  7.8  | 26.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_s_300e_coco.pdparams) | [配置文件](./yolov5_seg_s_300e_coco.yml) |
+| *YOLOv5-m        |  640     |    16      |   300e   |     -    |  45.0  | - |  22.0  | 70.8 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_m_300e_coco.pdparams) | [配置文件](./yolov5_seg_m_300e_coco.yml) |
+| *YOLOv5-l        |  640     |    16      |   300e   |     -    |  48.9  | - |  47.9  | 147.7 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_l_300e_coco.pdparams) | [配置文件](./yolov5_seg_l_300e_coco.yml) |
+| *YOLOv5-x        |  640     |    16      |   300e   |     -    |  50.6  | - |  88.8  | 265.7 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_seg_x_300e_coco.pdparams) | [配置文件](./yolov5_seg_x_300e_coco.yml) |

configs/yolov5_seg/_base_/optimizer_300e.yml

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

configs/yolov5_seg/_base_/optimizer_300e_high.yml

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

configs/yolov5_seg/_base_/yolov5_seg_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
+
+with_mask: True
+
+depth_mult: 1.0
+width_mult: 1.0
+
+
+YOLOv5:
+  backbone: CSPDarkNet
+  neck: YOLOCSPPAN
+  yolo_head: YOLOv5InsHead
+  with_mask: True
+  post_process: ~
+
+CSPDarkNet:
+  arch: "P5"
+  return_idx: [2, 3, 4]
+  depthwise: false
+
+YOLOCSPPAN:
+  depthwise: false
+
+YOLOv5InsHead:
+  anchors: [[10, 13], [16, 30], [33, 23],
+            [30, 61], [62, 45], [59, 119],
+            [116, 90], [156, 198], [373, 326]]
+  anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+  stride: [8, 16, 32]
+  loss: YOLOv5InsLoss
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 3000
+    keep_top_k: 300
+    score_threshold: 0.001
+    nms_threshold: 0.65
+    return_index: True
+
+YOLOv5InsLoss:
+  downsample_ratios: [8, 16, 32]
+  balance: [4.0, 1.0, 0.4]
+  overlap: True
+  box_weight: 0.05
+  obj_weight: 1.0
+  cls_weght: 0.5

configs/yolov5_seg/_base_/yolov5_seg_reader.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, 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: 16
+  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