Add ppyoloe+ voc (#7319)

* remove duplicate backbone variant, test=document_fix * add ppyoloe+ voc * fix doc

Add ppyoloe+ voc (#7319)
* remove duplicate backbone variant, test=document_fix * add ppyoloe+ voc * fix doc
e443404e · Feng Ni · GitHub · c8678c3b · e443404e · e443404e
4 changed file
--- a/configs/ppyoloe/README.md
+++ b/configs/ppyoloe/README.md
@@ -33,6 +33,9 @@ PP-YOLOE is composed of following methods:
 - [SiLU(Swish) activation function](https://arxiv.org/abs/1710.05941)

 ## Model Zoo
+
+### Model Zoo on COCO
+
 |     Model      | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |                                       download                                       |                   config                    |
 |:--------------:|:-----:|:-------:|:----------:|:----------:| :-------:|:--------------------------:|:---------------------------:|:---------:|:--------:|:---------------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:|
 |  PP-YOLOE+_s   |  80   |     8      |     8      | cspresnet-s |     640     |            43.7            |            43.9             |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_s_80e_coco.yml) |
@@ -68,6 +71,15 @@ PP-YOLOE is composed of following methods:
 - If you set `--run_benchmark=True`，you should install these dependencies at first, `pip install pynvml psutil GPUtil`.
 - End-to-end speed test includes pre-processing + inference + post-processing and NMS time, using **Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz**, **single Tesla V100**, **CUDA 11.2**, **CUDNN 8.2.0**, **TensorRT 8.0.1.6**.

+
+### Model Zoo on VOC
+
+|     Model      | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>0.5 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |  download  |  config  |
+|:---------------:|:-----:|:-----------:|:-----------:|:---------:|:----------:|:--------------:|:---------:|:---------:|:-------------:|:-----------------------:| :-------: |:--------:|
+|   PP-YOLOE+_s   |  30   |     8     |    8     | cspresnet-s |    640     |   86.7  |  7.93  |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [config](./ppyoloe_plus_crn_s_30e_voc.yml) |
+|   PP-YOLOE+_l   |  30   |     8     |    8     | cspresnet-l |    640     |   89.0  |  52.20 |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_30e_voc.pdparams) | [config](./ppyoloe_plus_crn_l_30e_voc.yml) |
+
+
 ### Feature Models

 The PaddleDetection team provides configs and weights of various feature detection models based on PP-YOLOE, which users can download for use:
@@ -76,11 +88,35 @@ The PaddleDetection team provides configs and weights of various feature detecti
 | :--------: | :---------: | :------: |
 |Pedestrian Detection | CrowdHuman | [pphuman](../pphuman) |
 |Vehicle Detection | BDD100K, UA-DETRAC | [ppvehicle](../ppvehicle) |
-|Small Object Detection | VisDrone | [visdrone](../visdrone) |
+|Small Object Detection | VisDrone、DOTA、xView |  [smalldet](../smalldet) |


 ## Getting Start

+### Datasets and Metrics
+
+PaddleDetection team provides **COCO and VOC dataset** , decompress and place it under `PaddleDetection/dataset/`:
+
+```
+wget https://bj.bcebos.com/v1/paddledet/data/coco.tar
+# tar -xvf coco.tar
+
+wget https://bj.bcebos.com/v1/paddledet/data/voc.zip
+# unzip voc.zip
+```
+
+**Note:**
+  - For the format of COCO style dataset, please refer to [format-data](https://cocodataset.org/#format-data) and [format-results](https://cocodataset.org/#format-results).
+  - For the evaluation metric of COCO, please refer to [detection-eval](https://cocodataset.org/#detection-eval), and install  [cocoapi](https://github.com/cocodataset/cocoapi) at first.
+  - For the evaluation metric of VOC, please refer to [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html).
+
+### Custom dataset
+
+1.For the annotation of custom dataset, please refer to [DetAnnoTools](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/DetAnnoTools_en.md);
+
+2.For training preparation of custom dataset，please refer to [PrepareDataSet](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/PrepareDetDataSet_en.md).
+
+
 ### Training

 Training PP-YOLOE+ on 8 GPUs with following command

--- a/configs/ppyoloe/README_cn.md
+++ b/configs/ppyoloe/README_cn.md
@@ -33,6 +33,9 @@ PP-YOLOE由以下方法组成
 - [SiLU(Swish)激活函数](https://arxiv.org/abs/1710.05941)

 ## 模型库
+
+### COCO数据集模型库
+
 |       模型        | Epoch |   GPU个数   | 每GPU图片个数 |  骨干网络  |    输入尺寸    | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |                                         模型下载                                         |                    配置文件                     |
 |:---------------:|:-----:|:---------:|:--------:|:----------:|:----------:|:--------------------------:|:---------------------------:|:---------:|:--------:|:---------------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:|
 |   PP-YOLOE+_s   |  80   |     8     |    8     | cspresnet-s |    640     |            43.7            |            43.9             |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_s_80e_coco.yml) |
@@ -69,6 +72,13 @@ PP-YOLOE由以下方法组成
 - 端到端速度测试包含模型前处理 + 模型推理 + 模型后处理及NMS的时间，测试使用**Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz**, **单卡V100**, **CUDA 11.2**, **CUDNN 8.2.0**, **TensorRT 8.0.1.6**。


+### VOC数据集模型库
+|       模型       | Epoch |   GPU个数   | 每GPU图片个数 |  骨干网络  |   输入尺寸   | Box AP<sup>0.5 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |  模型下载  |  配置文件 |
+|:---------------:|:-----:|:-----------:|:-----------:|:---------:|:----------:|:--------------:|:---------:|:---------:|:-------------:|:-----------------------:| :-------: |:--------:|
+|   PP-YOLOE+_s   |  30   |     8     |    8     | cspresnet-s |    640     |   86.7  |  7.93  |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [config](./ppyoloe_plus_crn_s_30e_voc.yml) |
+|   PP-YOLOE+_l   |  30   |     8     |    8     | cspresnet-l |    640     |   89.0  |  52.20 |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_30e_voc.pdparams) | [config](./ppyoloe_plus_crn_l_30e_voc.yml) |
+
+
 ### 垂类应用模型

 PaddleDetection团队提供了基于PP-YOLOE的各种垂类检测模型的配置文件和权重，用户可以下载进行使用：
@@ -77,11 +87,34 @@ PaddleDetection团队提供了基于PP-YOLOE的各种垂类检测模型的配置
 | :--------: | :---------: | :------: |
 |  行人检测   | CrowdHuman  |   [pphuman](../pphuman)  |
 |  车辆检测   | BDD100K、UA-DETRAC  |  [ppvehicle](../ppvehicle)   |
-|  小目标检测 | VisDrone    |  [visdrone](../visdrone)   |
+|  小目标检测 | VisDrone、DOTA、xView |  [smalldet](../smalldet)   |


 ## 使用说明

+### 数据集和评价指标
+
+下载PaddleDetection团队提供的**COCO和VOC数据**，并解压放置于`PaddleDetection/dataset/`下：
+
+```
+wget https://bj.bcebos.com/v1/paddledet/data/coco.tar
+# tar -xvf coco.tar
+
+wget https://bj.bcebos.com/v1/paddledet/data/voc.zip
+# unzip voc.zip
+```
+
+**注意:**
+ - COCO风格格式，请参考 [format-data](https://cocodataset.org/#format-data) 和 [format-results](https://cocodataset.org/#format-results)。
+ - COCO风格评测指标，请参考 [detection-eval](https://cocodataset.org/#detection-eval) ，并首先安装 [cocoapi](https://github.com/cocodataset/cocoapi)。
+ - VOC风格格式和评测指标，请参考 [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html)。
+
+### 自定义数据集
+
+1.自定义数据集的标注制作，请参考 [DetAnnoTools](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/DetAnnoTools.md);
+2.自定义数据集的训练准备，请参考 [PrepareDataSet](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/PrepareDetDataSet.md).
+
+
 ### 训练

 请执行以下指令训练PP-YOLOE+

--- a/configs/ppyoloe/ppyoloe_plus_crn_l_30e_voc.yml
+++ b/configs/ppyoloe/ppyoloe_plus_crn_l_30e_voc.yml
+_BASE_: [
+  '../datasets/voc.yml',
+  '../runtime.yml',
+  './_base_/optimizer_80e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_l_30e_voc/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_l_80e_coco.pdparams
+depth_mult: 1.0
+width_mult: 1.0
+
+
+TrainReader:
+  batch_size: 8 # default 8 gpus, total bs = 64
+
+EvalReader:
+  batch_size: 4
+
+
+epoch: 30
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 36
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
--- a/configs/ppyoloe/ppyoloe_plus_crn_s_30e_voc.yml
+++ b/configs/ppyoloe/ppyoloe_plus_crn_s_30e_voc.yml
+_BASE_: [
+  '../datasets/voc.yml',
+  '../runtime.yml',
+  './_base_/optimizer_80e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_s_30e_voc/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/ppyoloe_plus_crn_s_80e_coco.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+
+TrainReader:
+  batch_size: 8 # default 8 gpus, total bs = 64
+
+EvalReader:
+  batch_size: 4
+
+
+epoch: 30
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 36
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7