更换文档检测模型

2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
2072 changed files with 254336 additions and 4967 deletions
--- a/paddle_detection/configs/semi_det/denseteacher/README.md
+++ b/paddle_detection/configs/semi_det/denseteacher/README.md
@@ -0,0 +1,101 @@
+简体中文 | [English](README_en.md)
+
+# Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
+
+## FCOS模型库
+
+|      模型       |  监督数据比例 |        Sup Baseline     |    Sup Epochs (Iters)   |  Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 |  Semi Epochs (Iters)  |  模型下载  |   配置文件   |
+| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
+| DenseTeacher-FCOS     |   5% |   [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup005.yml)    |  24 (8712)  | 21.3 |  **30.6**  | 240 (87120)   | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi005.yml) |
+| DenseTeacher-FCOS     |   10% |   [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup010.yml)    | 24 (17424)  | 26.3 |  **35.1**  | 240 (174240)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010.yml) |
+| DenseTeacher-FCOS(LSJ)|   10% |   [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup010.yml)    | 24 (17424)  | 26.3 |  **37.1(LSJ)**  | 240 (174240)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) |
+| DenseTeacher-FCOS |100%(full)|   [sup_config](../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll) | 24 (175896) | 42.6 | **44.2** | 24 (175896)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_full.yml) |
+
+
+**注意:**
+ - 以上模型训练默认使用8 GPUs，监督数据总batch_size默认为16，无监督数据总batch_size默认也为16，默认初始学习率为0.01。如果改动了总batch_size，请按线性比例相应地调整学习率；
+ - **监督数据比例**是指使用的有标签COCO数据集占 COCO train2017 全量训练集的百分比，使用的无标签COCO数据集一般也是相同比例，但具体图片和有标签数据的图片不重合；
+ - `Semi Epochs (Iters)`表示**半监督训练**的模型的 Epochs (Iters)，如果使用**自定义数据集**，需自行根据Iters换算到对应的Epochs调整，最好保证总Iters 和COCO数据集的设置较为接近；
+ - `Sup mAP`是**只使用有监督数据训练**的模型的精度，请参照**基础检测器的配置文件** 和 [baseline](../baseline)；
+ - `Semi mAP`是**半监督训练**的模型的精度，模型下载和配置文件的链接均为**半监督模型**；
+ - `LSJ`表示 **large-scale jittering**，表示使用更大范围的多尺度训练，可进一步提升精度，但训练速度也会变慢；
+ - 半监督检测的配置讲解，请参照[文档](../README.md/#半监督检测配置)；
+ - `Dense Teacher`原文使用`R50-va-caffe`预训练，PaddleDetection中默认使用`R50-vb`预训练，如果使用`R50-vd`结合[SSLD](../../../docs/feature_models/SSLD_PRETRAINED_MODEL.md)的预训练模型，可进一步显著提升检测精度，同时backbone部分配置也需要做出相应更改，如：
+ ```python
+  pretrain_weights:  https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
+  ResNet:
+    depth: 50
+    variant: d
+    norm_type: bn
+    freeze_at: 0
+    return_idx: [1, 2, 3]
+    num_stages: 4
+    lr_mult_list: [0.05, 0.05, 0.1, 0.15]
+ ```
+
+
+## PPYOLOE+ 模型库
+
+|      模型       |  监督数据比例 |        Sup Baseline     |    Sup Epochs (Iters)   |  Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 |  Semi Epochs (Iters)  |  模型下载  |   配置文件   |
+| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
+| DenseTeacher-PPYOLOE+_s | 5% |   [sup_config](../baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml) | 80 (14480) | 32.8 |  **34.0**  | 200 (36200)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_s_coco_semi005.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_s_coco_semi005.yml) |
+| DenseTeacher-PPYOLOE+_s | 10% |   [sup_config](../baseline/ppyoloe_plus_crn_s_80e_coco_sup010.yml) | 80 (14480) | 35.3 |  **37.5**  | 200 (36200)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_s_coco_semi010.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_s_coco_semi010.yml) |
+| DenseTeacher-PPYOLOE+_l | 5% |   [sup_config](../baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml) | 80 (14480) | 42.9 |  **45.4**  | 200 (36200)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_l_coco_semi005.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_l_coco_semi005.yml) |
+| DenseTeacher-PPYOLOE+_l | 10% |   [sup_config](../baseline/ppyoloe_plus_crn_l_80e_coco_sup010.yml) | 80 (14480) | 45.7 |  **47.4**  | 200 (36200)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_l_coco_semi010.pdparams) | [config](./denseteacher_ppyoloe_plus_crn_l_coco_semi010.yml) |
+
+
+## 使用说明
+
+仅训练时必须使用半监督检测的配置文件去训练，评估、预测、部署也可以按基础检测器的配置文件去执行。
+
+### 训练
+
+```bash
+# 单卡训练 (不推荐，需按线性比例相应地调整学习率)
+CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
+
+# 多卡训练
+python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
+```
+
+### 评估
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams
+```
+
+### 预测
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg
+```
+
+### 部署
+
+部署可以使用半监督检测配置文件，也可以使用基础检测器的配置文件去部署和使用。
+
+```bash
+# 导出模型
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams
+
+# 导出权重预测
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU
+
+# 部署测速
+CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
+
+# 导出ONNX
+paddle2onnx --model_dir output_inference/denseteacher_fcos_r50_fpn_coco_semi010/ --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file denseteacher_fcos_r50_fpn_coco_semi010.onnx
+```
+
+
+## 引用
+
+```
+ @article{denseteacher2022,
+  title={Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection},
+  author={Hongyu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun},
+  journal={arXiv preprint arXiv:2207.02541},
+  year={2022}
+}
+```
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_full.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_full.yml
@@ -0,0 +1,166 @@
+_BASE_: [
+  'denseteacher_fcos_r50_fpn_coco_semi010.yml',
+  '../_base_/coco_detection_full.yml',
+]
+log_iter: 100
+snapshot_epoch: 2
+epochs: &epochs 24
+weights: output/denseteacher_fcos_r50_fpn_coco_full/model_final
+
+
+### pretrain and warmup config, choose one and comment another
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/fcos_r50_fpn_iou_multiscale_2x_coco.pdparams # mAP=42.6
+# semi_start_iters: 0
+# ema_start_iters: 0
+# use_warmup: &use_warmup False
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
+semi_start_iters: 5000
+ema_start_iters: 3000
+use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 2.0, distill_loss_box: 1.0, distill_loss_quality: 1.0}
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+    gamma: 2.0
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
+    - RandomFlip: {}
+  weak_aug:
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  sup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+    - Gt2FCOSTarget:
+        object_sizes_boundary: [64, 128, 256, 512]
+        center_sampling_radius: 1.5
+        downsample_ratios: [8, 16, 32, 64, 128]
+        num_shift: 0.5
+        norm_reg_targets: True
+  unsup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+  sup_batch_size: 2
+  unsup_batch_size: 2
+  shuffle: True
+  drop_last: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+
+TestReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  fuse_normalize: True
+
+
+### model config
+architecture: FCOS
+FCOS:
+  backbone: ResNet
+  neck: FPN
+  fcos_head: FCOSHead
+
+ResNet:
+  depth: 50
+  variant: 'b'
+  norm_type: bn
+  freeze_at: 0 # res2
+  return_idx: [1, 2, 3]
+  num_stages: 4
+
+FPN:
+  out_channel: 256
+  spatial_scales: [0.125, 0.0625, 0.03125]
+  extra_stage: 2
+  has_extra_convs: True
+  use_c5: False
+
+FCOSHead:
+  fcos_feat:
+    name: FCOSFeat
+    feat_in: 256
+    feat_out: 256
+    num_convs: 4
+    norm_type: "gn"
+    use_dcn: False
+  fpn_stride: [8, 16, 32, 64, 128]
+  prior_prob: 0.01
+  norm_reg_targets: True
+  centerness_on_reg: True
+  num_shift: 0.5
+  fcos_loss:
+    name: FCOSLoss
+    loss_alpha: 0.25
+    loss_gamma: 2.0
+    iou_loss_type: "giou"
+    reg_weights: 1.0
+    quality: "iou"
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [*epochs]
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+  clip_grad_by_value: 1.0
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi005.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi005.yml
@@ -0,0 +1,164 @@
+_BASE_: [
+  '../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
+  '../_base_/coco_detection_percent_5.yml',
+]
+log_iter: 20
+snapshot_epoch: 5
+epochs: &epochs 240 # 480 will be better
+weights: output/denseteacher_fcos_r50_fpn_coco_semi005/model_final
+
+
+### pretrain and warmup config, choose one and comment another
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/fcos_r50_fpn_2x_coco_sup005.pdparams # mAP=21.3
+# semi_start_iters: 0
+# ema_start_iters: 0
+# use_warmup: &use_warmup False
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
+semi_start_iters: 5000
+ema_start_iters: 3000
+use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 4.0, distill_loss_box: 1.0, distill_loss_quality: 1.0}
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+    gamma: 2.0
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
+    - RandomFlip: {}
+  weak_aug:
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  sup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+    - Gt2FCOSTarget:
+        object_sizes_boundary: [64, 128, 256, 512]
+        center_sampling_radius: 1.5
+        downsample_ratios: [8, 16, 32, 64, 128]
+        norm_reg_targets: True
+  unsup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+  sup_batch_size: 2
+  unsup_batch_size: 2
+  shuffle: True
+  drop_last: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+
+TestReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  fuse_normalize: True
+
+
+### model config
+architecture: FCOS
+FCOS:
+  backbone: ResNet
+  neck: FPN
+  fcos_head: FCOSHead
+
+ResNet:
+  depth: 50
+  variant: 'b'
+  norm_type: bn
+  freeze_at: 0 # res2
+  return_idx: [1, 2, 3]
+  num_stages: 4
+
+FPN:
+  out_channel: 256
+  spatial_scales: [0.125, 0.0625, 0.03125]
+  extra_stage: 2
+  has_extra_convs: True
+  use_c5: False
+
+FCOSHead:
+  fcos_feat:
+    name: FCOSFeat
+    feat_in: 256
+    feat_out: 256
+    num_convs: 4
+    norm_type: "gn"
+    use_dcn: False
+  fpn_stride: [8, 16, 32, 64, 128]
+  prior_prob: 0.01
+  norm_reg_targets: True
+  centerness_on_reg: True
+  fcos_loss:
+    name: FCOSLoss
+    loss_alpha: 0.25
+    loss_gamma: 2.0
+    iou_loss_type: "giou"
+    reg_weights: 1.0
+    quality: "iou"
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [*epochs]
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+  clip_grad_by_value: 1.0
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml
@@ -0,0 +1,169 @@
+_BASE_: [
+  '../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
+  '../_base_/coco_detection_percent_10.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+epochs: &epochs 240
+weights: output/denseteacher_fcos_r50_fpn_coco_semi010/model_final
+
+
+### pretrain and warmup config, choose one and comment another
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/fcos_r50_fpn_2x_coco_sup010.pdparams # mAP=26.3
+# semi_start_iters: 0
+# ema_start_iters: 0
+# use_warmup: &use_warmup False
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
+semi_start_iters: 5000
+ema_start_iters: 3000
+use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 4.0, distill_loss_box: 1.0, distill_loss_quality: 1.0}
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+    gamma: 2.0
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
+    - RandomFlip: {}
+  weak_aug:
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  sup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+    - Gt2FCOSTarget:
+        object_sizes_boundary: [64, 128, 256, 512]
+        center_sampling_radius: 1.5
+        downsample_ratios: [8, 16, 32, 64, 128]
+        num_shift: 0. # default 0.5
+        multiply_strides_reg_targets: False
+        norm_reg_targets: True
+  unsup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+  sup_batch_size: 2
+  unsup_batch_size: 2
+  shuffle: True
+  drop_last: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+
+TestReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  fuse_normalize: True
+
+
+### model config
+architecture: FCOS
+FCOS:
+  backbone: ResNet
+  neck: FPN
+  fcos_head: FCOSHead
+
+ResNet:
+  depth: 50
+  variant: 'b'
+  norm_type: bn
+  freeze_at: 0 # res2
+  return_idx: [1, 2, 3]
+  num_stages: 4
+
+FPN:
+  out_channel: 256
+  spatial_scales: [0.125, 0.0625, 0.03125]
+  extra_stage: 2
+  has_extra_convs: True
+  use_c5: False
+
+FCOSHead:
+  fcos_feat:
+    name: FCOSFeat
+    feat_in: 256
+    feat_out: 256
+    num_convs: 4
+    norm_type: "gn"
+    use_dcn: False
+  fpn_stride: [8, 16, 32, 64, 128]
+  prior_prob: 0.01
+  norm_reg_targets: True
+  centerness_on_reg: True
+  num_shift: 0. # default 0.5
+  multiply_strides_reg_targets: False
+  sqrt_score: False
+  fcos_loss:
+    name: FCOSLoss
+    loss_alpha: 0.25
+    loss_gamma: 2.0
+    iou_loss_type: "giou"
+    reg_weights: 1.0
+    quality: "iou"
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [*epochs]
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+  clip_grad_by_value: 1.0
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml
@@ -0,0 +1,44 @@
+_BASE_: [
+  'denseteacher_fcos_r50_fpn_coco_semi010.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+epochs: &epochs 240
+weights: output/denseteacher_fcos_r50_fpn_coco_semi010_lsj/model_final
+
+
+### reader config
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    # large-scale jittering
+    - RandomResize: {target_size: [[400, 1333], [1200, 1333]], keep_ratio: True, interp: 1, random_range: True}
+    - RandomFlip: {}
+  weak_aug:
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  sup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+    - Gt2FCOSTarget:
+        object_sizes_boundary: [64, 128, 256, 512]
+        center_sampling_radius: 1.5
+        downsample_ratios: [8, 16, 32, 64, 128]
+        num_shift: 0. # default 0.5
+        multiply_strides_reg_targets: False
+        norm_reg_targets: True
+  unsup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+  sup_batch_size: 2
+  unsup_batch_size: 2
+  shuffle: True
+  drop_last: True
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_l_coco_semi005.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_l_coco_semi005.yml
@@ -0,0 +1,151 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml',
+  '../_base_/coco_detection_percent_5.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/denseteacher_ppyoloe_plus_crn_l_coco_semi005/model_final
+
+epochs: &epochs 200
+cosine_epochs: &cosine_epochs 240
+
+
+### pretrain and warmup config, choose one and comment another
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_l_80e_coco_sup005.pdparams # mAP=42.9
+semi_start_iters: 0
+ema_start_iters: 0
+use_warmup: &use_warmup False
+
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
+# semi_start_iters: 5000
+# ema_start_iters: 3000
+# use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 1.0, distill_loss_iou: 2.5, distill_loss_dfl: 0., distill_loss_contrast: 0.1}
+    contrast_loss:
+      temperature: 0.2
+      alpha: 0.9
+      smooth_iter: 100
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+batch_size: &batch_size 8
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomFlip: {}
+    - RandomCrop: {} # unsup will be fake gt_boxes
+  weak_aug:
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  sup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+    - PadGT: {}
+  unsup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+  sup_batch_size: *batch_size
+  unsup_batch_size: *batch_size
+  shuffle: True
+  drop_last: True
+  collate_batch: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 2
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+### model config
+architecture: PPYOLOE
+norm_type: sync_bn
+ema_black_list: ['proj_conv.weight']
+custom_black_list: ['reduce_mean']
+PPYOLOE:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+eval_size: ~ # means None, but not str 'None'
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: -1 #
+  use_varifocal_loss: True
+  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !CosineDecay
+    max_epochs: *cosine_epochs
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005 # dt-fcos 0.0001
+    type: L2
+  clip_grad_by_norm: 1.0 # dt-fcos clip_grad_by_value
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_l_coco_semi010.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_l_coco_semi010.yml
@@ -0,0 +1,151 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml',
+  '../_base_/coco_detection_percent_10.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/denseteacher_ppyoloe_plus_crn_l_coco_semi010/model_final
+
+epochs: &epochs 200
+cosine_epochs: &cosine_epochs 240
+
+
+### pretrain and warmup config, choose one and comment another
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_l_80e_coco_sup010.pdparams # mAP=45.7
+semi_start_iters: 0
+ema_start_iters: 0
+use_warmup: &use_warmup False
+
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
+# semi_start_iters: 5000
+# ema_start_iters: 3000
+# use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 1.0, distill_loss_iou: 2.5, distill_loss_dfl: 0., distill_loss_contrast: 0.1}
+    contrast_loss:
+      temperature: 0.2
+      alpha: 0.9
+      smooth_iter: 100
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+batch_size: &batch_size 8
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomFlip: {}
+    - RandomCrop: {} # unsup will be fake gt_boxes
+  weak_aug:
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  sup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+    - PadGT: {}
+  unsup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+  sup_batch_size: *batch_size
+  unsup_batch_size: *batch_size
+  shuffle: True
+  drop_last: True
+  collate_batch: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 2
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+### model config
+architecture: PPYOLOE
+norm_type: sync_bn
+ema_black_list: ['proj_conv.weight']
+custom_black_list: ['reduce_mean']
+PPYOLOE:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+eval_size: ~ # means None, but not str 'None'
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: -1 #
+  use_varifocal_loss: True
+  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !CosineDecay
+    max_epochs: *cosine_epochs
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005 # dt-fcos 0.0001
+    type: L2
+  clip_grad_by_norm: 1.0 # dt-fcos clip_grad_by_value
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_s_coco_semi005.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_s_coco_semi005.yml
@@ -0,0 +1,151 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml',
+  '../_base_/coco_detection_percent_5.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/denseteacher_ppyoloe_plus_crn_s_coco_semi005/model_final
+
+epochs: &epochs 200
+cosine_epochs: &cosine_epochs 240
+
+
+### pretrain and warmup config, choose one and comment another
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_s_80e_coco_sup005.pdparams # mAP=32.8
+semi_start_iters: 0
+ema_start_iters: 0
+use_warmup: &use_warmup False
+
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
+# semi_start_iters: 5000
+# ema_start_iters: 3000
+# use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 1.0, distill_loss_iou: 2.5, distill_loss_dfl: 0., distill_loss_contrast: 0.1}
+    contrast_loss:
+      temperature: 0.2
+      alpha: 0.9
+      smooth_iter: 100
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+batch_size: &batch_size 8
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomFlip: {}
+    - RandomCrop: {} # unsup will be fake gt_boxes
+  weak_aug:
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  sup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+    - PadGT: {}
+  unsup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+  sup_batch_size: *batch_size
+  unsup_batch_size: *batch_size
+  shuffle: True
+  drop_last: True
+  collate_batch: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 2
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+### model config
+architecture: PPYOLOE
+norm_type: sync_bn
+ema_black_list: ['proj_conv.weight']
+custom_black_list: ['reduce_mean']
+PPYOLOE:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+eval_size: ~ # means None, but not str 'None'
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: -1 #
+  use_varifocal_loss: True
+  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !CosineDecay
+    max_epochs: *cosine_epochs
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005 # dt-fcos 0.0001
+    type: L2
+  clip_grad_by_norm: 1.0 # dt-fcos clip_grad_by_value
--- a/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_s_coco_semi010.yml
+++ b/paddle_detection/configs/semi_det/denseteacher/denseteacher_ppyoloe_plus_crn_s_coco_semi010.yml
@@ -0,0 +1,151 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml',
+  '../_base_/coco_detection_percent_10.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/denseteacher_ppyoloe_plus_crn_s_coco_semi010/model_final
+
+epochs: &epochs 200
+cosine_epochs: &cosine_epochs 240
+
+
+### pretrain and warmup config, choose one and comment another
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_s_80e_coco_sup010.pdparams # mAP=35.3
+semi_start_iters: 0
+ema_start_iters: 0
+use_warmup: &use_warmup False
+
+# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
+# semi_start_iters: 5000
+# ema_start_iters: 3000
+# use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 1.0, distill_loss_iou: 2.5, distill_loss_dfl: 0., distill_loss_contrast: 0.1}
+    contrast_loss:
+      temperature: 0.2
+      alpha: 0.9
+      smooth_iter: 100
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+batch_size: &batch_size 8
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomFlip: {}
+    - RandomCrop: {} # unsup will be fake gt_boxes
+  weak_aug:
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], is_scale: true, norm_type: none}
+  sup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+    - PadGT: {}
+  unsup_batch_transforms:
+    - BatchRandomResize: {target_size: [640], random_size: True, random_interp: True, keep_ratio: False}
+    - Permute: {}
+  sup_batch_size: *batch_size
+  unsup_batch_size: *batch_size
+  shuffle: True
+  drop_last: True
+  collate_batch: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 2
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 1
+
+
+### model config
+architecture: PPYOLOE
+norm_type: sync_bn
+ema_black_list: ['proj_conv.weight']
+custom_black_list: ['reduce_mean']
+PPYOLOE:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+eval_size: ~ # means None, but not str 'None'
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: -1 #
+  use_varifocal_loss: True
+  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !CosineDecay
+    max_epochs: *cosine_epochs
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 3
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005 # dt-fcos 0.0001
+    type: L2
+  clip_grad_by_norm: 1.0 # dt-fcos clip_grad_by_value