https://github.com/nemonameless/draw_keypoint_gt_pred
https://github.com/nemonameless/draw_keypoint_gt_pred
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/nemonameless/draw_keypoint_gt_pred
- Owner: nemonameless
- License: mit
- Created: 2019-10-14T09:43:58.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-14T09:50:57.000Z (over 6 years ago)
- Last Synced: 2025-02-17T06:30:26.835Z (over 1 year ago)
- Language: Python
- Size: 2.13 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Pytorch Keypoint Detection
#### 介绍
2019.05月pytorch发布了torchvision0.3, 里面实现了Mask_RCNN, Keypoint_RCNN和DeepLabV3，可以直接用于语义分割，目标检测，实例分割和人体关键点检测4个任务。
在github上torch/vision/reference里面有classification, detection和segmentation三个文件夹，分别对应不同任务。直接运行detection的代码是MaskRCNN的实现，用于目标检测和实例分割任务。官网也有对应的[教程](https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html])。可以轻松通过COCO2017数据集进行训练和测试。
但是如果要实现人体关键点检测的话需要在detection的文件修改一些参数，这里我将修改后的文件和可视化程序附上。
#### Dataset
下载COCO2017数据集，下载train2017,val2017和annotations三个文件后解压，最终文件目录结构如下
COCO2017/
train2017/
val2017/
annotations/
#### Train
对`train.py`进行如下操作
1 修改函数`get_dataset`中的`paths`
2 修改文件中列出了的各种参数
3 假设路径都修改完毕，使用预训练模型进行训练: `python train.py --pretrained`
#### Predict and Visualize
对COCO val集的图片进行预测并可视化。
对predict_visualize.py进行如下操作
1 修改代码中的路径
2 修改参数`detect_threshold`和`keypoint_score_threshold`, 分别过滤得分低的个体和得分低的关键点
3 在根目录下建立文件夹result, 可视化后的图片存在在此文件夹下
4 运行`python predict_visualize.py`
#### Evaluate
执行代码`python train.py --test-only`
程序在COCO2017 val集上的结果如下，同[官网介绍](https://pytorch.org/blog/torchvision03/)一致
```
Averaged stats: model_time: 0.1371 (0.1627) evaluator_time: 0.0043 (0.0105)
Accumulating evaluation results...
DONE (t=1.31s).
Accumulating evaluation results...
DONE (t=0.41s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.502
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.796
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.545
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.341
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.591
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.648
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.176
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.519
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.603
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.460
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.669
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.738
IoU metric: keypoints
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.599
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.834
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.650
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.553
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.675
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.672
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.889
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.721
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.623
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.741
```