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/baseline/README.md
+++ b/paddle_detection/configs/semi_det/baseline/README.md
@@ -0,0 +1,93 @@
+# Supervised Baseline 纯监督模型基线
+
+## COCO数据集模型库
+
+### [FCOS](../../fcos)
+
+|  基础模型          |    监督数据比例   |    Epochs (Iters)  | mAP<sup>val<br>0.5:0.95 |  模型下载  |   配置文件   |
+| :---------------: | :-------------: | :---------------: |:---------------------: |:--------: | :---------: |
+| FCOS ResNet50-FPN |        5%       |    24 (8712)      |       21.3        | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_2x_coco_sup005.pdparams) | [config](fcos_r50_fpn_2x_coco_sup005.yml) |
+| FCOS ResNet50-FPN |        10%      |    24 (17424)     |       26.3        | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_2x_coco_sup010.pdparams) | [config](fcos_r50_fpn_2x_coco_sup010.yml) |
+| FCOS ResNet50-FPN |        full     |    24 (175896)    |       42.6        | [download](https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_iou_multiscale_2x_coco.pdparams) | [config](../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml) |
+
+**注意:**
+  - 以上模型训练默认使用8 GPUs，总batch_size默认为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。
+
+
+### [PP-YOLOE+](../../ppyoloe)
+
+|  基础模型          |    监督数据比例   |    Epochs (Iters)  |   mAP<sup>val<br>0.5:0.95 |  模型下载  |   配置文件   |
+| :---------------: | :-------------: | :---------------: | :---------------------: |:--------: | :---------: |
+| PP-YOLOE+_s       |        5%       |    80 (7200)      |       32.8       | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco_sup005.pdparams) | [config](ppyoloe_plus_crn_s_80e_coco_sup005.yml) |
+| PP-YOLOE+_s       |        10%      |    80 (14480)     |       35.3       | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco_sup010.pdparams) | [config](ppyoloe_plus_crn_s_80e_coco_sup010.yml) |
+| PP-YOLOE+_s       |        full     |    80 (146560)    |       43.7       | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml) |
+| PP-YOLOE+_l       |        5%       |    80 (7200)      |       42.9       | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco_sup005.pdparams) | [config](ppyoloe_plus_crn_l_80e_coco_sup005.yml) |
+| PP-YOLOE+_l       |        10%      |    80 (14480)     |       45.7       | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco_sup010.pdparams) | [config](ppyoloe_plus_crn_l_80e_coco_sup010.yml) |
+| PP-YOLOE+_l       |        full     |    80 (146560)    |       49.8       | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams) | [config](../../ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml) |
+
+**注意:**
+  - 以上模型训练默认使用8 GPUs，总batch_size默认为64，默认初始学习率为0.001。如果改动了总batch_size，请按线性比例相应地调整学习率。
+
+
+### [Faster R-CNN](../../faster_rcnn)
+
+|  基础模型          |    监督数据比例   |    Epochs (Iters)  |  mAP<sup>val<br>0.5:0.95 |  模型下载  |   配置文件   |
+| :---------------: | :-------------: | :---------------: | :---------------------: |:--------: | :---------: |
+| Faster R-CNN ResNet50-FPN |    5%   |    24 (8712)      |       20.7      | [download](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_2x_coco_sup005.pdparams) | [config](faster_rcnn_r50_fpn_2x_coco_sup005.yml) |
+| Faster R-CNN ResNet50-FPN |    10%  |    24 (17424)     |       25.6      | [download](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_2x_coco_sup010.pdparams) | [config](faster_rcnn_r50_fpn_2x_coco_sup010.yml) |
+| Faster R-CNN ResNet50-FPN |    full |    24 (175896)    |       40.0      | [download](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_2x_coco.pdparams) | [config](../../configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.yml) |
+
+**注意:**
+  - 以上模型训练默认使用8 GPUs，总batch_size默认为16，默认初始学习率为0.02。如果改动了总batch_size，请按线性比例相应地调整学习率。
+
+
+### [RetinaNet](../../retinanet)
+
+|  基础模型          |    监督数据比例   |    Epochs (Iters)  |  mAP<sup>val<br>0.5:0.95 |  模型下载  |   配置文件   |
+| :---------------: | :-------------: | :---------------: | :---------------------: |:--------: | :---------: |
+| RetinaNet ResNet50-FPN |     5%    |    24 (8712)      |       13.9       | [download](https://paddledet.bj.bcebos.com/models/retinanet_r50_fpn_2x_coco_sup005.pdparams) | [config](retinanet_r50_fpn_2x_coco_sup005.yml) |
+| RetinaNet ResNet50-FPN |    10%    |    24 (17424)     |       23.6       | [download](https://paddledet.bj.bcebos.com/models/retinanet_r50_fpn_2x_coco_sup010.pdparams) | [config](retinanet_r50_fpn_2x_coco_sup010.yml) |
+| RetinaNet ResNet50-FPN |    full   |    24 (175896)    |       39.1       | [download](https://paddledet.bj.bcebos.com/models/retinanet_r50_fpn_2x_coco.pdparams) | [config](../../configs/retinanet/retinanet_r50_fpn_2x_coco.yml) |
+
+**注意:**
+  - 以上模型训练默认使用8 GPUs，总batch_size默认为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。
+
+
+
+### [RT-DETR](../../rtdetr)
+
+|  基础模型          |    监督数据比例     |  mAP<sup>val<br>0.5:0.95 |  模型下载  |   配置文件   |
+| :---------------: | :-------------: | :---------------------: |:--------: | :---------: |
+| RT-DETR ResNet5vd |     5%    |     39.1       | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup005.pdparams) | [config](rtdetr_r50vd_6x_coco_sup005.yml) |
+| RT-DETR ResNet5vd |    10%    |     42.3       | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_coco_sup010.pdparams) | [config](rtdetr_r50vd_6x_coco_sup010.yml) |
+| RT-DETR ResNet5vd |    VOC2007 |     62.7      | [download](https://bj.bcebos.com/v1/paddledet/data/semidet/rtdetr_ssod/baseline/rtdetr_r50vd_6x_voc2007.pdparams) | [config](rtdetr_r50vd_6x_voc2007.yml) |
+
+**注意:**
+  - RT-DETR模型训练默认使用4 GPUs，总batch_size默认为16，默认初始学习率为0.0001。如果改动了总batch_size，请按线性比例相应地调整学习率。
+
+### 注意事项
+ - COCO部分监督数据集请参照 [数据集准备](../README.md) 去下载和准备，各个比例的训练集均为**从train2017中抽取部分百分比的子集**，默认使用`fold`号为1的划分子集，`sup010`表示抽取10%的监督数据训练，`sup005`表示抽取5%，`full`表示全部train2017，验证集均为val2017全量；
+ - 抽取部分百分比的监督数据的抽法不同，或使用的`fold`号不同，精度都会因此而有约0.5 mAP之多的差异；
+ - PP-YOLOE+ 使用Objects365预训练，其余模型均使用ImageNet预训练；
+ - 线型比例相应调整学习率，参照公式: **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**。
+
+
+## 使用教程
+
+将以下命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行：
+
+```bash
+model_type=semi_det/baseline
+job_name=ppyoloe_plus_crn_s_80e_coco_sup010 # 可修改，如 fcos_r50_fpn_2x_coco_sup010
+
+config=configs/${model_type}/${job_name}.yml
+log_dir=log_dir/${job_name}
+weights=output/${job_name}/model_final.pdparams
+
+# 1.training
+# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config}
+python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
+
+# 2.eval
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights}
+```
--- a/paddle_detection/configs/semi_det/baseline/faster_rcnn_r50_fpn_2x_coco_sup005.yml
+++ b/paddle_detection/configs/semi_det/baseline/faster_rcnn_r50_fpn_2x_coco_sup005.yml
@@ -0,0 +1,42 @@
+_BASE_: [
+  '../../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/faster_rcnn_r50_fpn_2x_coco_sup005/model_final
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+worker_num: 2
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
+    - RandomFlip: {}
+    - 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: 2
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    epochs: 1
--- a/paddle_detection/configs/semi_det/baseline/faster_rcnn_r50_fpn_2x_coco_sup010.yml
+++ b/paddle_detection/configs/semi_det/baseline/faster_rcnn_r50_fpn_2x_coco_sup010.yml
@@ -0,0 +1,42 @@
+_BASE_: [
+  '../../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/faster_rcnn_r50_fpn_2x_coco_sup010/model_final
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@10.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+worker_num: 2
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
+    - RandomFlip: {}
+    - 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: 2
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.02
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    epochs: 1
--- a/paddle_detection/configs/semi_det/baseline/fcos_r50_fpn_2x_coco_sup005.yml
+++ b/paddle_detection/configs/semi_det/baseline/fcos_r50_fpn_2x_coco_sup005.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/fcos_r50_fpn_2x_coco_sup005/model_final
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 1
--- a/paddle_detection/configs/semi_det/baseline/fcos_r50_fpn_2x_coco_sup010.yml
+++ b/paddle_detection/configs/semi_det/baseline/fcos_r50_fpn_2x_coco_sup010.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/fcos_r50_fpn_2x_coco_sup010/model_final
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@10.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 1
--- a/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_l_80e_coco_sup005.yml
+++ b/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_l_80e_coco_sup005.yml
@@ -0,0 +1,29 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_l_80e_coco_sup005/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
+depth_mult: 1.0
+width_mult: 1.0
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
--- a/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_l_80e_coco_sup010.yml
+++ b/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_l_80e_coco_sup010.yml
@@ -0,0 +1,29 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_l_80e_coco_sup010/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
+depth_mult: 1.0
+width_mult: 1.0
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@10.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
--- a/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml
+++ b/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml
@@ -0,0 +1,29 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_s_80e_coco_sup005/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
--- a/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_s_80e_coco_sup010.yml
+++ b/paddle_detection/configs/semi_det/baseline/ppyoloe_plus_crn_s_80e_coco_sup010.yml
@@ -0,0 +1,29 @@
+_BASE_: [
+  '../../ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 5
+weights: output/ppyoloe_plus_crn_s_80e_coco_sup010/model_final
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@10.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
--- a/paddle_detection/configs/semi_det/baseline/retinanet_r50_fpn_2x_coco_sup005.yml
+++ b/paddle_detection/configs/semi_det/baseline/retinanet_r50_fpn_2x_coco_sup005.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../../retinanet/retinanet_r50_fpn_2x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/retinanet_r50_fpn_2x_coco_sup005/model_final
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 1
--- a/paddle_detection/configs/semi_det/baseline/retinanet_r50_fpn_2x_coco_sup010.yml
+++ b/paddle_detection/configs/semi_det/baseline/retinanet_r50_fpn_2x_coco_sup010.yml
@@ -0,0 +1,26 @@
+_BASE_: [
+  '../../retinanet/retinanet_r50_fpn_2x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/retinanet_r50_fpn_2x_coco_sup010/model_final
+
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@10.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.001
+    epochs: 1
--- a/paddle_detection/configs/semi_det/baseline/rtdetr_r50vd_6x_coco_sup005.yml
+++ b/paddle_detection/configs/semi_det/baseline/rtdetr_r50vd_6x_coco_sup005.yml
@@ -0,0 +1,35 @@
+_BASE_: [
+  '../../rtdetr/rtdetr_r50vd_6x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/rtdetr_r50vd_6x_coco/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+worker_num: 4
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {prob: 0.8}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCrop: {prob: 0.8}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - NormalizeBox: {}
+    - BboxXYXY2XYWH: {}
+    - Permute: {}
+  batch_size: 4
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+  use_shared_memory: false
--- a/paddle_detection/configs/semi_det/baseline/rtdetr_r50vd_6x_coco_sup010.yml
+++ b/paddle_detection/configs/semi_det/baseline/rtdetr_r50vd_6x_coco_sup010.yml
@@ -0,0 +1,35 @@
+_BASE_: [
+  '../../rtdetr/rtdetr_r50vd_6x_coco.yml',
+]
+log_iter: 50
+snapshot_epoch: 2
+weights: output/rtdetr_r50vd_6x_coco/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: semi_annotations/instances_train2017.1@5.json
+    dataset_dir: dataset/coco
+    data_fields: ['image', 'gt_bbox', 'gt_class']
+
+
+worker_num: 4
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomDistort: {prob: 0.8}
+    - RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCrop: {prob: 0.8}
+    - RandomFlip: {}
+  batch_transforms:
+    - BatchRandomResize: {target_size: [480, 512, 544, 576, 608, 640, 640, 640, 672, 704, 736, 768, 800], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - NormalizeBox: {}
+    - BboxXYXY2XYWH: {}
+    - Permute: {}
+  batch_size: 4
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+  use_shared_memory: false