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/picodet/legacy_model/README.md
+++ b/paddle_detection/configs/picodet/legacy_model/README.md
@@ -0,0 +1,60 @@
+# PP-PicoDet Legacy Model-ZOO (2021.10)
+
+| Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  Download  | Config |
+| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- |
+| PicoDet-S |  320*320   |          27.1           |        41.4        |        0.99        |       0.73        |              8.13               |            **6.65**             | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_320_coco.yml) |
+| PicoDet-S |  416*416   |          30.7           |        45.8        |        0.99        |       1.24        |              12.37              |            **9.82**             | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco.yml) |
+| PicoDet-M |  320*320   |          30.9           |        45.7        |        2.15        |       1.48        |              11.27              |            **9.61**             | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_320_coco.yml) |
+| PicoDet-M |  416*416   |          34.8           |        50.5        |        2.15        |       2.50        |              17.39              |            **15.88**            | [model](https://paddledet.bj.bcebos.com/models/picodet_m_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_416_coco.yml) |
+| PicoDet-L |  320*320   |          32.9           |        48.2        |        3.30        |       2.23        |              15.26              |            **13.42**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_320_coco.yml) |
+| PicoDet-L |  416*416   |          36.6           |        52.5        |        3.30        |       3.76        |              23.36              |            **21.85**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_416_coco.yml) |
+| PicoDet-L |  640*640   |          40.9           |        57.6        |        3.30        |       8.91        |              54.11              |            **50.55**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_640_coco.yml) |
+
+#### More Configs
+
+| Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  Download  | Config |
+| :--------------------------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- |
+| PicoDet-Shufflenetv2 1x      |  416*416   |          30.0           |        44.6        |        1.17        |       1.53        |              15.06              |            **10.63**            |      [model](https://paddledet.bj.bcebos.com/models/picodet_shufflenetv2_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_shufflenetv2_1x_416_coco.log)      | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_shufflenetv2_1x_416_coco.yml)      |
+| PicoDet-MobileNetv3-large 1x |  416*416   |          35.6           |        52.0        |        3.55        |       2.80        |              20.71              |            **17.88**            | [model](https://paddledet.bj.bcebos.com/models/picodet_mobilenetv3_large_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_mobilenetv3_large_1x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_mobilenetv3_large_1x_416_coco.yml) |
+| PicoDet-LCNet 1.5x           |  416*416   |          36.3           |        52.2        |        3.10        |       3.85        |              21.29              |            **20.8**             |           [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_416_coco.log)           | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_lcnet_1_5x_416_coco.yml)           |
+| PicoDet-LCNet 1.5x           |  640*640   |          40.6           |        57.4        |        3.10        |       -        |              -              |            -             |           [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_640_coco.log)           | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_lcnet_1_5x_640_coco.yml)           |
+| PicoDet-R18           |  640*640   |          40.7           |        57.2        |        11.10        |       -        |              -              |            -             |           [model](https://paddledet.bj.bcebos.com/models/picodet_r18_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_r18_640_coco.log)           | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/more_config/picodet_r18_640_coco.yml)           |
+
+<details open>
+<summary><b>Table Notes:</b></summary>
+
+- <a name="latency">Latency:</a> All our models test on `Qualcomm Snapdragon 865(4xA77+4xA55)` with 4 threads by arm8 and with FP16. In the above table, test latency on [NCNN](https://github.com/Tencent/ncnn) and `Lite`->[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite).  And testing latency with code: [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark).
+- PicoDet is trained on COCO train2017 dataset and evaluated on COCO val2017.
+- PicoDet used 4 or 8 GPUs for training and all checkpoints are trained with default settings and hyperparameters.
+
+</details>
+
+- Deploy models
+
+| Model     | Input size | ONNX  | Paddle Lite(fp32) | Paddle Lite(fp16) |
+| :-------- | :--------: | :---------------------: | :----------------: | :----------------: |
+| PicoDet-S |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_320.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_320_fp16.tar) |
+| PicoDet-S |  416*416   |  [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_416_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_416.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_416_fp16.tar) |
+| PicoDet-M |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_320_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_320.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_320_fp16.tar) |
+| PicoDet-M |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_416.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_416_fp16.tar) |
+| PicoDet-L |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_320_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_320.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_320_fp16.tar) |
+| PicoDet-L |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_416_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_416.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_416_fp16.tar) |
+| PicoDet-L |  640*640   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_640_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_640.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_640_fp16.tar) |
+| PicoDet-Shufflenetv2 1x      |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_shufflenetv2_1x_416_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_shufflenetv2_1x.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_shufflenetv2_1x_fp16.tar) |
+| PicoDet-MobileNetv3-large 1x |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_mobilenetv3_large_1x_416_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_mobilenetv3_large_1x.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_mobilenetv3_large_1x_fp16.tar) |
+| PicoDet-LCNet 1.5x           |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_lcnet_1_5x_416_coco.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_lcnet_1_5x.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_lcnet_1_5x_fp16.tar) |
+
+
+
+## Cite PP-PicoDet
+```
+@misc{yu2021pppicodet,
+      title={PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices},
+      author={Guanghua Yu and Qinyao Chang and Wenyu Lv and Chang Xu and Cheng Cui and Wei Ji and Qingqing Dang and Kaipeng Deng and Guanzhong Wang and Yuning Du and Baohua Lai and Qiwen Liu and Xiaoguang Hu and Dianhai Yu and Yanjun Ma},
+      year={2021},
+      eprint={2111.00902},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+```
--- a/paddle_detection/configs/picodet/legacy_model/_base_/optimizer_100e.yml
+++ b/paddle_detection/configs/picodet/legacy_model/_base_/optimizer_100e.yml
@@ -0,0 +1,18 @@
+epoch: 100
+
+LearningRate:
+  base_lr: 0.4
+  schedulers:
+  - name: CosineDecay
+    max_epochs: 100
+  - name: LinearWarmup
+    start_factor: 0.1
+    steps: 300
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
--- a/paddle_detection/configs/picodet/legacy_model/_base_/optimizer_300e.yml
+++ b/paddle_detection/configs/picodet/legacy_model/_base_/optimizer_300e.yml
@@ -0,0 +1,18 @@
+epoch: 300
+
+LearningRate:
+  base_lr: 0.4
+  schedulers:
+  - name: CosineDecay
+    max_epochs: 300
+  - name: LinearWarmup
+    start_factor: 0.1
+    steps: 300
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
--- a/paddle_detection/configs/picodet/legacy_model/_base_/picodet_320_reader.yml
+++ b/paddle_detection/configs/picodet/legacy_model/_base_/picodet_320_reader.yml
@@ -0,0 +1,42 @@
+worker_num: 6
+eval_height: &eval_height 320
+eval_width: &eval_width 320
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [256, 288, 320, 352, 384], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 128
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, *eval_height, *eval_width]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 1
--- a/paddle_detection/configs/picodet/legacy_model/_base_/picodet_416_reader.yml
+++ b/paddle_detection/configs/picodet/legacy_model/_base_/picodet_416_reader.yml
@@ -0,0 +1,42 @@
+worker_num: 6
+eval_height: &eval_height 416
+eval_width: &eval_width 416
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [352, 384, 416, 448, 480], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 80
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, *eval_height, *eval_width]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 1
--- a/paddle_detection/configs/picodet/legacy_model/_base_/picodet_640_reader.yml
+++ b/paddle_detection/configs/picodet/legacy_model/_base_/picodet_640_reader.yml
@@ -0,0 +1,42 @@
+worker_num: 6
+eval_height: &eval_height 640
+eval_width: &eval_width 640
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [576, 608, 640, 672, 704], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 56
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, *eval_height, *eval_width]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 1
--- a/paddle_detection/configs/picodet/legacy_model/_base_/picodet_esnet.yml
+++ b/paddle_detection/configs/picodet/legacy_model/_base_/picodet_esnet.yml
@@ -0,0 +1,55 @@
+architecture: PicoDet
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_0_pretrained.pdparams
+
+PicoDet:
+  backbone: ESNet
+  neck: CSPPAN
+  head: PicoHead
+
+ESNet:
+  scale: 1.0
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]
+
+CSPPAN:
+  out_channels: 128
+  use_depthwise: True
+  num_csp_blocks: 1
+  num_features: 4
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 128
+    feat_out: 128
+    num_convs: 4
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  fpn_stride: [8, 16, 32, 64]
+  feat_in_chan: 128
+  prior_prob: 0.01
+  reg_max: 7
+  cell_offset: 0.5
+  loss_class:
+    name: VarifocalLoss
+    use_sigmoid: True
+    iou_weighted: True
+    loss_weight: 1.0
+  loss_dfl:
+    name: DistributionFocalLoss
+    loss_weight: 0.25
+  loss_bbox:
+    name: GIoULoss
+    loss_weight: 2.0
+  assigner:
+    name: SimOTAAssigner
+    candidate_topk: 10
+    iou_weight: 6
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
--- a/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/README.md
+++ b/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/README.md
@@ -0,0 +1,56 @@
+# 更多应用
+
+
+## 1. 版面分析任务
+
+版面分析指的是对图片形式的文档进行区域划分，定位其中的关键区域，如文字、标题、表格、图片等。版面分析示意图如下图所示。
+
+<div align="center">
+    <img src="images/layout_demo.png" width="800">
+</div>
+
+### 1.1 数据集
+
+使用[PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet)训练英文文档版面分析模型，该数据面向英文文献类（论文）场景，分别训练集(333,703张标注图片)、验证集(11,245张标注图片)和测试集(11,405张图片)，包含5类：Table、Figure、Title、Text、List，更多[版面分析数据集](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/layout/README.md#32)
+
+### 1.2 模型库
+
+使用PicoDet模型在PubLayNet数据集进行训练，同时采用FGD蒸馏，预训练模型如下:
+
+| 模型     | 图像输入尺寸 | mAP<sup>val<br/>0.5 |  下载地址  |  配置文件  |
+| :-------- | :--------: |  :----------------: | :---------------: | ----------------- |
+| PicoDet-LCNet_x1_0 |  800*608   |   93.5% | [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout.pdparams) &#124; [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_layout_infer.tar) | [config](./picodet_lcnet_x1_0_layout.yml) |
+| PicoDet-LCNet_x1_0 + FGD |  800*608   |   94.0%     | [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) &#124; [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) | [teacher config](./picodet_lcnet_x2_5_layout.yml)&#124;[student config](./picodet_lcnet_x1_0_layout.yml) |
+
+ [FGD蒸馏介绍](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/distill/README.md)
+
+### 1.3 模型推理
+
+了解版面分析整个流程(数据准备、模型训练、评估等)，请参考[版面分析](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/layout/README.md)，这里仅展示模型推理过程。首先下载模型库中的inference_model模型。
+
+```
+mkdir inference_model
+cd inference_model
+# 下载并解压PubLayNet推理模型
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar && tar xf picodet_lcnet_x1_0_fgd_layout_infer.tar
+cd ..
+```
+
+版面恢复任务进行推理，可以执行如下命令：
+
+```bash
+python3 deploy/python/infer.py \
+    --model_dir=inference_model/picodet_lcnet_x1_0_fgd_layout_infer/ \
+    --image_file=docs/images/layout.jpg \
+    --device=CPU
+```
+
+可视化版面结果如下图所示：
+
+<div align="center">
+    <img src="images/layout_res.jpg" width="800">
+</div>
+
+## 2 Reference
+
+[1] Zhong X, Tang J, Yepes A J. Publaynet: largest dataset ever for document layout analysis[C]//2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019: 1015-1022.
--- a/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/images/layout_demo.png
+++ b/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/images/layout_demo.png
--- a/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/images/layout_res.jpg
+++ b/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/images/layout_res.jpg
--- a/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml
+++ b/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml
@@ -0,0 +1,90 @@
+_BASE_: [
+  '../../../../runtime.yml',
+  '../../_base_/picodet_esnet.yml',
+  '../../_base_/optimizer_100e.yml',
+  '../../_base_/picodet_640_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_0_pretrained.pdparams
+weights: output/picodet_lcnet_x1_0_layout/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 10
+snapshot_epoch: 1
+epoch: 100
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+  nms_cpu: True
+
+LCNet:
+  scale: 1.0
+  feature_maps: [3, 4, 5]
+
+metric: COCO
+num_classes: 5
+
+TrainDataset:
+    name: COCODataSet
+    image_dir: train
+    anno_path: train.json
+    dataset_dir: ./dataset/publaynet/
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+    name: COCODataSet
+    image_dir: val
+    anno_path: val.json
+    dataset_dir: ./dataset/publaynet/
+
+TestDataset:
+  !ImageFolder
+    anno_path: ./dataset/publaynet/val.json
+
+
+worker_num: 8
+eval_height: &eval_height 800
+eval_width: &eval_width 608
+eval_size: &eval_size [*eval_height, *eval_width]
+
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [[768, 576], [800, 608], [832, 640]], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 24
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [800, 608], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, 800, 608]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [800, 608], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  shuffle: false
--- a/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x2_5_layout.yml
+++ b/paddle_detection/configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x2_5_layout.yml
@@ -0,0 +1,34 @@
+_BASE_: [
+  '../../_base_/picodet_esnet.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x2_5_ssld_pretrained.pdparams
+weights: output/picodet_lcnet_x2_5_layout/model_final
+find_unused_parameters: True
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+  nms_cpu: True
+
+LCNet:
+  scale: 2.5
+  feature_maps: [3, 4, 5]
+
+CSPPAN:
+  spatial_scales: [0.125, 0.0625, 0.03125]
+
+slim: Distill
+slim_method: FGD
+distill_loss: FGDFeatureLoss
+distill_loss_name: ['neck_f_3', 'neck_f_2', 'neck_f_1', 'neck_f_0']
+
+FGDFeatureLoss:
+  student_channels: 128
+  teacher_channels: 128
+  temp: 0.5
+  alpha_fgd: 0.001
+  beta_fgd: 0.0005
+  gamma_fgd: 0.0005
+  lambda_fgd: 0.000005
--- a/paddle_detection/configs/picodet/legacy_model/application/mainbody_detection/README.md
+++ b/paddle_detection/configs/picodet/legacy_model/application/mainbody_detection/README.md
@@ -0,0 +1,30 @@
+# 更多应用
+
+
+## 1. 主体检测任务
+
+主体检测技术是目前应用非常广泛的一种检测技术，它指的是检测出图片中一个或者多个主体的坐标位置，然后将图像中的对应区域裁剪下来，进行识别，从而完成整个识别过程。主体检测是识别任务的前序步骤，可以有效提升识别精度。
+
+主体检测是图像识别的前序步骤，被用于PaddleClas的PP-ShiTu图像识别系统中。PP-ShiTu中使用的主体检测模型基于PP-PicoDet。更多关于PP-ShiTu的介绍与使用可以参考：[PP-ShiTu](https://github.com/PaddlePaddle/PaddleClas)。
+
+
+### 1.1 数据集
+
+PP-ShiTu图像识别任务中，训练主体检测模型时主要用到了以下几个数据集。
+
+| 数据集       | 数据量   | 主体检测任务中使用的数据量   | 场景  | 数据集地址 |
+| :------------:  | :-------------: | :-------: | :-------: | :--------: |
+| Objects365 | 1700K | 173k | 通用场景 | [地址](https://www.objects365.org/overview.html) |
+| COCO2017 | 118K | 118k  | 通用场景 | [地址](https://cocodataset.org/) |
+| iCartoonFace | 48k | 48k | 动漫人脸检测 | [地址](https://github.com/luxiangju-PersonAI/iCartoonFace) |
+| LogoDet-3k | 155k | 155k | Logo检测 | [地址](https://github.com/Wangjing1551/LogoDet-3K-Dataset) |
+| RPC | 54k | 54k  | 商品检测 | [地址](https://rpc-dataset.github.io/) |
+
+在实际训练的过程中，将所有数据集混合在一起。由于是主体检测，这里将所有标注出的检测框对应的类别都修改为 `前景` 的类别，最终融合的数据集中只包含 1 个类别，即前景，数据集定义配置可以参考[picodet_lcnet_x2_5_640_mainbody.yml](./picodet_lcnet_x2_5_640_mainbody.yml)。
+
+
+### 1.2 模型库
+
+| 模型     | 图像输入尺寸 | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 |  下载地址  | config |
+| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: |
+| PicoDet-LCNet_x2_5 |  640*640   |          41.5   |    62.0     | [trained model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_x2_5_640_mainbody.pdparams) &#124; [inference model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_x2_5_640_mainbody_infer.tar) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_x2_5_640_mainbody.log) | [config](./picodet_lcnet_x2_5_640_mainbody.yml) |
--- a/paddle_detection/configs/picodet/legacy_model/application/mainbody_detection/picodet_lcnet_x2_5_640_mainbody.yml
+++ b/paddle_detection/configs/picodet/legacy_model/application/mainbody_detection/picodet_lcnet_x2_5_640_mainbody.yml
@@ -0,0 +1,23 @@
+_BASE_: [
+  '../../../../datasets/coco_detection.yml',
+  '../../../../runtime.yml',
+  '../../_base_/picodet_esnet.yml',
+  '../../_base_/optimizer_100e.yml',
+  '../../_base_/picodet_640_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x2_5_ssld_pretrained.pdparams
+weights: output/picodet_lcnet_x2_5_640_mainbody/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 20
+snapshot_epoch: 2
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+
+LCNet:
+  scale: 2.5
+  feature_maps: [3, 4, 5]
--- a/paddle_detection/configs/picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml
+++ b/paddle_detection/configs/picodet/legacy_model/application/pedestrian_detection/picodet_s_192_pedestrian.yml
@@ -0,0 +1,149 @@
+use_gpu: true
+log_iter: 20
+save_dir: output
+snapshot_epoch: 1
+print_flops: false
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_192_pedestrian/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 300
+metric: COCO
+num_classes: 1
+# Exporting the model
+export:
+  post_process: False  # Whether post-processing is included in the network when export model.
+  nms: False           # Whether NMS is included in the network when export model.
+  benchmark: False    # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
+
+architecture: PicoDet
+
+PicoDet:
+  backbone: ESNet
+  neck: CSPPAN
+  head: PicoHead
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+CSPPAN:
+  out_channels: 96
+  use_depthwise: True
+  num_csp_blocks: 1
+  num_features: 4
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  fpn_stride: [8, 16, 32, 64]
+  feat_in_chan: 96
+  prior_prob: 0.01
+  reg_max: 7
+  cell_offset: 0.5
+  loss_class:
+    name: VarifocalLoss
+    use_sigmoid: True
+    iou_weighted: True
+    loss_weight: 1.0
+  loss_dfl:
+    name: DistributionFocalLoss
+    loss_weight: 0.25
+  loss_bbox:
+    name: GIoULoss
+    loss_weight: 2.0
+  assigner:
+    name: SimOTAAssigner
+    candidate_topk: 10
+    iou_weight: 6
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
+
+LearningRate:
+  base_lr: 0.4
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: aic_coco_train_cocoformat.json
+    dataset_dir: dataset
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/instances_val2017.json
+
+worker_num: 8
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [128, 160, 192, 224, 256], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 128
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [192, 192], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, 192, 192]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [192, 192], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  shuffle: false
+  fuse_normalize: true
--- a/paddle_detection/configs/picodet/legacy_model/application/pedestrian_detection/picodet_s_320_pedestrian.yml
+++ b/paddle_detection/configs/picodet/legacy_model/application/pedestrian_detection/picodet_s_320_pedestrian.yml
@@ -0,0 +1,148 @@
+use_gpu: true
+log_iter: 20
+save_dir: output
+snapshot_epoch: 1
+print_flops: false
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_320_pedestrian/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 300
+metric: COCO
+num_classes: 1
+# Exporting the model
+export:
+  post_process: False  # Whether post-processing is included in the network when export model.
+  nms: False           # Whether NMS is included in the network when export model.
+  benchmark: False    # It is used to testing model performance, if set `True`, post-process and NMS will not be exported.
+
+architecture: PicoDet
+
+PicoDet:
+  backbone: ESNet
+  neck: CSPPAN
+  head: PicoHead
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+CSPPAN:
+  out_channels: 96
+  use_depthwise: True
+  num_csp_blocks: 1
+  num_features: 4
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  fpn_stride: [8, 16, 32, 64]
+  feat_in_chan: 96
+  prior_prob: 0.01
+  reg_max: 7
+  cell_offset: 0.5
+  loss_class:
+    name: VarifocalLoss
+    use_sigmoid: True
+    iou_weighted: True
+    loss_weight: 1.0
+  loss_dfl:
+    name: DistributionFocalLoss
+    loss_weight: 0.25
+  loss_bbox:
+    name: GIoULoss
+    loss_weight: 2.0
+  assigner:
+    name: SimOTAAssigner
+    candidate_topk: 10
+    iou_weight: 6
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
+
+LearningRate:
+  base_lr: 0.4
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: aic_coco_train_cocoformat.json
+    dataset_dir: dataset
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/instances_val2017.json
+
+worker_num: 8
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [256, 288, 320, 352, 384], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 128
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [320, 320], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, 320, 320]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [320, 320], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  shuffle: false
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_1_0x_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_1_0x_416_coco.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_0_pretrained.pdparams
+weights: output/picodet_lcnet_1_5x_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+
+LCNet:
+  scale: 1.0
+  feature_maps: [3, 4, 5]
+
+TrainReader:
+  batch_size: 90
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_1_5x_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_1_5x_416_coco.yml
@@ -0,0 +1,23 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_5_pretrained.pdparams
+weights: output/picodet_lcnet_1_5x_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+
+LCNet:
+  scale: 1.5
+  feature_maps: [3, 4, 5]
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_1_5x_640_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_1_5x_640_coco.yml
@@ -0,0 +1,49 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_640_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_5_pretrained.pdparams
+weights: output/picodet_lcnet_1_5x_640_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+
+LCNet:
+  scale: 1.5
+  feature_maps: [3, 4, 5]
+
+CSPPAN:
+  out_channels: 160
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 160
+    feat_out: 160
+    num_convs: 4
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 160
+
+TrainReader:
+  batch_size: 24
+
+LearningRate:
+  base_lr: 0.2
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_2_5x_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_lcnet_2_5x_416_coco.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x2_5_ssld_pretrained.pdparams
+weights: output/picodet_lcnet_1_5x_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: LCNet
+  neck: CSPPAN
+  head: PicoHead
+
+LCNet:
+  scale: 2.5
+  feature_maps: [3, 4, 5]
+
+TrainReader:
+  batch_size: 48
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_mobilenetv3_large_1x_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_mobilenetv3_large_1x_416_coco.yml
@@ -0,0 +1,39 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
+weights: output/picodet_mobilenetv3_large_1x_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 180
+
+PicoDet:
+  backbone: MobileNetV3
+  neck: CSPPAN
+  head: PicoHead
+
+MobileNetV3:
+  model_name: large
+  scale: 1.0
+  with_extra_blocks: false
+  extra_block_filters: []
+  feature_maps: [7, 13, 16]
+
+TrainReader:
+  batch_size: 56
+
+LearningRate:
+  base_lr: 0.3
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_r18_640_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_r18_640_coco.yml
@@ -0,0 +1,39 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_640_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet18_vd_pretrained.pdparams
+weights: output/picodet_r18_640_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: ResNet
+  neck: CSPPAN
+  head: PicoHead
+
+ResNet:
+  depth: 18
+  variant: d
+  return_idx: [1, 2, 3]
+  freeze_at: -1
+  freeze_norm: false
+  norm_decay: 0.
+
+TrainReader:
+  batch_size: 56
+
+LearningRate:
+  base_lr: 0.3
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
--- a/paddle_detection/configs/picodet/legacy_model/more_config/picodet_shufflenetv2_1x_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/more_config/picodet_shufflenetv2_1x_416_coco.yml
@@ -0,0 +1,38 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ShuffleNetV2_x1_0_pretrained.pdparams
+weights: output/picodet_shufflenetv2_1x_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: ShuffleNetV2
+  neck: CSPPAN
+  head: PicoHead
+
+ShuffleNetV2:
+  scale: 1.0
+  feature_maps: [5, 13, 17]
+  act: leaky_relu
+
+CSPPAN:
+  out_channels: 96
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 96
--- a/paddle_detection/configs/picodet/legacy_model/picodet_l_320_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_l_320_coco.yml
@@ -0,0 +1,47 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_320_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_25_pretrained.pdparams
+weights: output/picodet_l_320_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 250
+
+ESNet:
+  scale: 1.25
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]
+
+CSPPAN:
+  out_channels: 160
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 160
+    feat_out: 160
+    num_convs: 4
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 160
+
+TrainReader:
+  batch_size: 56
+
+LearningRate:
+  base_lr: 0.3
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
--- a/paddle_detection/configs/picodet/legacy_model/picodet_l_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_l_416_coco.yml
@@ -0,0 +1,47 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_25_pretrained.pdparams
+weights: output/picodet_l_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 250
+
+ESNet:
+  scale: 1.25
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]
+
+CSPPAN:
+  out_channels: 160
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 160
+    feat_out: 160
+    num_convs: 4
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 160
+
+TrainReader:
+  batch_size: 48
+
+LearningRate:
+  base_lr: 0.3
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
--- a/paddle_detection/configs/picodet/legacy_model/picodet_l_640_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_l_640_coco.yml
@@ -0,0 +1,47 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_640_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_25_pretrained.pdparams
+weights: output/picodet_l_640_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 250
+
+ESNet:
+  scale: 1.25
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]
+
+CSPPAN:
+  out_channels: 160
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 160
+    feat_out: 160
+    num_convs: 4
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 160
+
+TrainReader:
+  batch_size: 32
+
+LearningRate:
+  base_lr: 0.3
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
--- a/paddle_detection/configs/picodet/legacy_model/picodet_m_320_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_m_320_coco.yml
@@ -0,0 +1,13 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_320_reader.yml',
+]
+
+weights: output/picodet_m_320_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
--- a/paddle_detection/configs/picodet/legacy_model/picodet_m_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_m_416_coco.yml
@@ -0,0 +1,13 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_416_reader.yml',
+]
+
+weights: output/picodet_m_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
--- a/paddle_detection/configs/picodet/legacy_model/picodet_s_320_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_s_320_coco.yml
@@ -0,0 +1,34 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_320_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_320_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+CSPPAN:
+  out_channels: 96
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 96
--- a/paddle_detection/configs/picodet/legacy_model/picodet_s_320_voc.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_s_320_voc.yml
@@ -0,0 +1,37 @@
+_BASE_: [
+  '../../datasets/voc.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_320_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_320_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+CSPPAN:
+  out_channels: 96
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 96
+
+EvalReader:
+  collate_batch: false
--- a/paddle_detection/configs/picodet/legacy_model/picodet_s_416_coco.yml
+++ b/paddle_detection/configs/picodet/legacy_model/picodet_s_416_coco.yml
@@ -0,0 +1,34 @@
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '_base_/picodet_esnet.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+CSPPAN:
+  out_channels: 96
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  feat_in_chan: 96
--- a/paddle_detection/configs/picodet/legacy_model/pruner/README.md
+++ b/paddle_detection/configs/picodet/legacy_model/pruner/README.md
@@ -0,0 +1,135 @@
+# 非结构化稀疏在 PicoDet 上的应用教程
+
+## 1. 介绍
+在模型压缩中，常见的稀疏方式为结构化稀疏和非结构化稀疏，前者在某个特定维度（特征通道、卷积核等等）上对卷积、矩阵乘法进行剪枝操作，然后生成一个更小的模型结构，这样可以复用已有的卷积、矩阵乘计算，无需特殊实现推理算子；后者以每一个参数为单元进行稀疏化，然而并不会改变参数矩阵的形状，所以更依赖于推理库、硬件对于稀疏后矩阵运算的加速能力。我们在 PP-PicoDet （以下简称PicoDet） 模型上运用了非结构化稀疏技术，在精度损失较小时，获得了在 ARM CPU 端推理的显著性能提升。本文档会介绍如何非结构化稀疏训练 PicoDet，关于非结构化稀疏的更多介绍请参照[这里](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/demo/dygraph/unstructured_pruning)。
+
+## 2. 版本要求
+```bash
+PaddlePaddle >= 2.1.2
+PaddleSlim develop分支 （pip install paddleslim -i https://pypi.tuna.tsinghua.edu.cn/simple）
+```
+
+## 3. 数据准备
+同 PicoDet
+
+## 4. 预训练模型
+在非结构化稀疏训练中，我们规定预训练模型是已经收敛完成的模型参数，所以需要额外在相关配置文件中声明。
+
+声明预训练模型地址的配置文件：./configs/picodet/pruner/picodet_m_320_coco_pruner.yml
+预训练模型地址请参照 PicoDet 文档：./configs/picodet/README.md
+
+## 5. 自定义稀疏化的作用范围
+为达到最佳推理加速效果，我们建议只对 1x1 卷积层进行稀疏化，其他层参数保持稠密。另外，有些层对于精度影响较大（例如head的最后几层，se-block的若干层），我们同样不建议对他们进行稀疏化，我们支持开发者通过传入自定义函数的形式，方便的指定哪些层不参与稀疏。例如，基于picodet_m_320这个模型，我们稀疏时跳过了后4层卷积以及6层se-block中的卷积，自定义函数如下：
+
+```python
+NORMS_ALL = [ 'BatchNorm', 'GroupNorm', 'LayerNorm', 'SpectralNorm', 'BatchNorm1D',
+    'BatchNorm2D', 'BatchNorm3D', 'InstanceNorm1D', 'InstanceNorm2D',
+    'InstanceNorm3D', 'SyncBatchNorm', 'LocalResponseNorm' ]
+
+def skip_params_self(model):
+    skip_params = set()
+    for _, sub_layer in model.named_sublayers():
+        if type(sub_layer).__name__.split('.')[-1] in NORMS_ALL:
+            skip_params.add(sub_layer.full_name())
+        for param in sub_layer.parameters(include_sublayers=False):
+            cond_is_conv1x1 = len(param.shape) == 4 and param.shape[2] == 1 and param.shape[3] == 1
+            cond_is_head_m = cond_is_conv1x1 and param.shape[0] == 112 and param.shape[1] == 128
+            cond_is_se_block_m = param.name.split('.')[0] in ['conv2d_17', 'conv2d_18', 'conv2d_56', 'conv2d_57', 'conv2d_75', 'conv2d_76']
+            if not cond_is_conv1x1 or cond_is_head_m or cond_is_se_block_m:
+                skip_params.add(param.name)
+    return skip_params
+```
+
+## 6. 训练
+我们已经将非结构化稀疏的核心功能通过 API 调用的方式嵌入到了训练中，所以如果您没有更细节的需求，直接运行 6.1 的命令启动训练即可。同时，为帮助您根据自己的需求更改、适配代码，我们也提供了更为详细的使用介绍，请参照 6.2。
+
+### 6.1 直接使用
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python3.7 -m paddle.distributed.launch --log_dir=log_test --gpus 0,1,2,3 tools/train.py -c configs/picodet/pruner/picodet_m_320_coco_pruner.yml --slim_config configs/slim/prune/picodet_m_unstructured_prune_75.yml --eval
+```
+
+### 6.2 详细介绍
+- 自定义稀疏化的作用范围：可以参照本教程的第 5 节
+- 如何添加稀疏化训练所需的 4 行代码
+
+```python
+# after constructing model and before training
+
+# Pruner Step1: configs
+configs = {
+    'pruning_strategy': 'gmp',
+    'stable_iterations': self.stable_epochs * steps_per_epoch,
+    'pruning_iterations': self.pruning_epochs * steps_per_epoch,
+    'tunning_iterations': self.tunning_epochs * steps_per_epoch,
+    'resume_iteration': 0,
+    'pruning_steps': self.pruning_steps,
+    'initial_ratio': self.initial_ratio,
+}
+
+# Pruner Step2: construct a pruner object
+self.pruner = GMPUnstructuredPruner(
+    model,
+    ratio=self.cfg.ratio,
+    skip_params_func=skip_params_self, # Only pass in this value when you design your own skip_params function. And the following argument (skip_params_type) will be ignored.
+    skip_params_type=self.cfg.skip_params_type,
+    local_sparsity=True,
+    configs=configs)
+
+# training
+for epoch_id in range(self.start_epoch, self.cfg.epoch):
+    model.train()
+    for step_id, data in enumerate(self.loader):
+        # model forward
+        outputs = model(data)
+        loss = outputs['loss']
+        # model backward
+        loss.backward()
+        self.optimizer.step()
+
+        # Pruner Step3: step during training
+        self.pruner.step()
+
+    # Pruner Step4: save the sparse model
+    self.pruner.update_params()
+    # model-saving API
+```
+
+## 7. 模型评估与推理部署
+这部分与 PicoDet 文档中基本一致，只是在转换到 PaddleLite 模型时，需要添加一个输入参数（sparse_model）：
+
+```bash
+paddle_lite_opt --model_dir=inference_model/picodet_m_320_coco --valid_targets=arm --optimize_out=picodet_m_320_coco_fp32_sparse --sparse_model=True
+```
+
+**注意：** 目前稀疏化推理适用于 PaddleLite的 FP32 和 INT8 模型，所以执行上述命令时，请不要打开 FP16 开关。
+
+## 8. 稀疏化结果
+我们在75%和85%稀疏度下，训练得到了 FP32 PicoDet-m模型，并在 SnapDragon-835设备上实测推理速度，效果如下表。其中：
+- 对于 m 模型，mAP损失1.5，获得了 34\%-58\% 的加速性能
+- 同样对于 m 模型，除4线程推理速度基本持平外，单线程推理速度、mAP、模型体积均优于 s 模型。
+
+
+| Model     | Input size | Sparsity | mAP<sup>val<br>0.5:0.95 | Size<br><sup>(MB) | Latency single-thread<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  speed-up single-thread |  Latency 4-thread<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  speed-up 4-thread |  Download  | SlimConfig |
+| :-------- | :--------: |:--------: | :---------------------: | :----------------: | :----------------: |:----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: |
+| PicoDet-m-1.0 |  320*320   |   0      |          30.9         | 8.9 |  127     | 0    |  43     |    0       | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams)&#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_m_320_coco.yml)|
+| PicoDet-m-1.0 |  320*320   |   75%    |          29.4         | 5.6 |  **80**  | 58%  | **32**  |   34%      | [model](https://paddledet.bj.bcebos.com/models/slim/picodet_m_320__coco_sparse_75.pdparams)&#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320__coco_sparse_75.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/prune/picodet_m_unstructured_prune_75.yml)|
+| PicoDet-s-1.0 |  320*320   |   0      |          27.1         | 4.6 |    68    |  0   |    26   |    0       | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_s_320_coco.yml)|
+| PicoDet-m-1.0 |  320*320   |   85%    |          27.6         | 4.1 |  **65**  | 96%  |  **27** |   59%      | [model](https://paddledet.bj.bcebos.com/models/slim/picodet_m_320__coco_sparse_85.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320__coco_sparse_85.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/slim/prune/picodet_m_unstructured_prune_85.yml)|
+
+**注意：**
+- 上述模型体积是**部署模型体积**，即 PaddleLite 转换得到的 *.nb 文件的体积。
+- 加速一栏我们按照 FPS 增加百分比计算，即：$(dense\_latency - sparse\_latency) / sparse\_latency$
+- 上述稀疏化训练时，我们额外添加了一种数据增强方式到 _base_/picodet_320_reader.yml，代码如下。但是不添加的话，预期mAP也不会有明显下降（<0.1），且对速度和模型体积没有影响。
+```yaml
+worker_num: 6
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+  - RandomDistort: {}
+  batch_transforms:
+etc.
+```
--- a/paddle_detection/configs/picodet/legacy_model/pruner/optimizer_300e_pruner.yml
+++ b/paddle_detection/configs/picodet/legacy_model/pruner/optimizer_300e_pruner.yml
@@ -0,0 +1,18 @@
+epoch: 300
+
+LearningRate:
+  base_lr: 0.15
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 1.0
+    steps: 34350
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
--- a/paddle_detection/configs/picodet/legacy_model/pruner/picodet_m_320_coco_pruner.yml
+++ b/paddle_detection/configs/picodet/legacy_model/pruner/picodet_m_320_coco_pruner.yml
@@ -0,0 +1,13 @@
+_BASE_: [
+  '../../../datasets/coco_detection.yml',
+  '../../../runtime.yml',
+  '../_base_/picodet_esnet.yml',
+  './optimizer_300e_pruner.yml',
+  '../_base_/picodet_320_reader.yml',
+]
+
+weights: output/picodet_m_320_coco/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10