add ppyoloe plus obj365 (#7564)

configs/datasets/objects365_detection.yml

+metric: COCO
+num_classes: 365
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train
+    anno_path: annotations/zhiyuan_objv2_train.json
+    dataset_dir: dataset/objects365
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: val
+    anno_path: annotations/zhiyuan_objv2_val.json
+    dataset_dir: dataset/objects365
+    allow_empty: true
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/zhiyuan_objv2_val.json
+    dataset_dir: dataset/objects365/

configs/ppyoloe/README.md

@@ -71,6 +71,18 @@ 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 Objects365
+|     Model      | Epoch | Machine number | 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   |  60   |  3 |  8     |    8     | cspresnet-s |    640     |   18.1  |  7.93  |  17.36   |   208.3   |  333.3   | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_s_60e_objects365.yml) |
+|   PP-YOLOE+_m   |  60   |   4 |  8     |    8     | cspresnet-m |    640     |   25.0  |  23.43  |  49.91   |   123.4       |  208.3  | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_m_60e_objects365.yml) |
+|   PP-YOLOE+_l   |  60   |   3 |  8     |    8     | cspresnet-l |    640     |   30.8  |  52.20 |  110.07  |   78.1    |  149.2   | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_l_60e_objects365.yml) |
+|   PP-YOLOE+_x   |  60   |  4 |   8     |    8     | cspresnet-x |    640     |   32.7  |  98.42 |  206.59      |   45.0        |  95.2  | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_x_60e_objects365.yml) |
+
+
+**Notes:**
+- The Details for multiple machine and multi-gpu training, see [DistributedTraining](../../docs/tutorials/DistributedTraining_en.md)
+
 
 ### Model Zoo on VOC
 

configs/ppyoloe/README_cn.md

@@ -71,6 +71,18 @@ PP-YOLOE由以下方法组成
 - 如果你设置了`--run_benchmark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
 - 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**。
 
+### Objects365数据集模型库
+|       模型       | Epoch |  机器个数 | 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   |  60   |  3 |  8     |    8     | cspresnet-s |    640     |   18.1  |  7.93  |  17.36   |   208.3   |  333.3   | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_s_60e_objects365.yml) |
+|   PP-YOLOE+_m   |  60   |   4 |  8     |    8     | cspresnet-m |    640     |   25.0  |  23.43  |  49.91   |   123.4       |  208.3  | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_m_60e_objects365.yml) |
+|   PP-YOLOE+_l   |  60   |   3 |  8     |    8     | cspresnet-l |    640     |   30.8  |  52.20 |  110.07  |   78.1    |  149.2   | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_l_60e_objects365.yml) |
+|   PP-YOLOE+_x   |  60   |  4 |   8     |    8     | cspresnet-x |    640     |   32.7  |  98.42 |  206.59      |   45.0        |  95.2  | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_x_60e_objects365.yml) |
+
+
+**注意:**
+- 多机训练细节见[文档](../../docs/tutorials/DistributedTraining_cn.md)
+
 
 ### 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 |

configs/ppyoloe/_base_/optimizer_60e.yml

+epoch: 60
+
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 72
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

configs/ppyoloe/ppyoloe_plus_crn_l_60e_objects365.yml

+_BASE_: [
+  '../datasets/objects365_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_60e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_l_60e_objects365/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_l_pretrained.pdparams
+
+CSPResNet:
+  use_alpha: False
+
+PPYOLOEHead:
+  static_assigner_epoch: 20
+
+depth_mult: 1.0
+width_mult: 1.0

configs/ppyoloe/ppyoloe_plus_crn_m_60e_objects365.yml

+_BASE_: [
+  '../datasets/objects365_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_60e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_m_60e_objects365/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_m_pretrained.pdparams
+
+CSPResNet:
+  use_alpha: False
+
+PPYOLOEHead:
+  static_assigner_epoch: 20
+
+depth_mult: 0.67
+width_mult: 0.75

configs/ppyoloe/ppyoloe_plus_crn_s_60e_objects365.yml

+_BASE_: [
+  '../datasets/objects365_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_60e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_s_60e_objects365/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_s_pretrained.pdparams
+
+CSPResNet:
+  use_alpha: False
+
+PPYOLOEHead:
+  static_assigner_epoch: 20
+
+depth_mult: 0.33
+width_mult: 0.50

configs/ppyoloe/ppyoloe_plus_crn_x_60e_objects365.yml

+_BASE_: [
+  '../datasets/objects365_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_60e.yml',
+  './_base_/ppyoloe_plus_crn.yml',
+  './_base_/ppyoloe_plus_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_x_60e_objects365/model_final
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_x_pretrained.pdparams
+
+CSPResNet:
+  use_alpha: False
+
+PPYOLOEHead:
+  static_assigner_epoch: 20
+
+depth_mult: 1.33
+width_mult: 1.25