# yolact_pytorch **Repository Path**: qiaodl/yolact_pytorch ## Basic Information - **Project Name**: yolact_pytorch - **Description**: :fire: :fire: :fire:Train Your Own DataSet for YOLACT and YOLACT++ Instance Segmentation Model!!! - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-15 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## YOLACT++训练自己的数据集 ## **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s paper: [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218) **Xu Jing** ### 1.将labelme标注的数据转化为coco数据样式 通过[labelme](https://github.com/wkentaro/labelme)我们标注了自己的训练集，我们将标注的训练集经过如下处理后即可得到coco样式的数据 ``` $ cd labelme-master/examples/instance_segmentation $ python3 labelme2coco.py water water_coco --labels labels.txt ``` 其中`labels.txt`文件为labelme标注的label名称列表，如下： ``` __ignore__ _background_ water ``` 会在当前文件夹下的`water_coco`文件下生成`JPEGImages`文件夹内存放了所有的image及`annotations.json`为coco类型的标注文件。 ### 2. YOLACT环境安装 + clone yolact项目 ``` $ git clone https://github.com/dbolya/yolact.git $ cd yolact ``` + 安装相关环境 ``` # anaconda $ conda env create -f environment.yml ``` ``` # Pytorch 1.0.1 (or higher) 和 TorchVision建议手动安装 # Cython needs to be installed before pycocotools $ pip3 install cython $ pip3 install opencv-python pillow pycocotools matplotlib ``` 如果想使用YOLACT++,请编译DCNv2 ``` $ cd external/DCNv2 $ python3 setup.py build develop ``` ### 3.修改yolact代码 + 打开`yolact-master/data/config.py` ``` # 修改dataset_base # ----------------------- DATASETS ----------------------- # dataset_base = Config({ 'name': 'water', # 起个名字 # Training images and annotations 'train_images': './myData/coco/JPEGImages/train', # 训练集的路径 'train_info': './myData/coco/annotations/train/annotations_train.json', # 标签的路径 # Validation images and annotations. 'valid_images': './myData/coco/JPEGImages/val', # 验证集路径 'valid_info': './myData/coco/annotations/train/annotations_val.json', #验证集标签的路径 # Whether or not to load GT. If this is False, eval.py quantitative evaluation won't work. 'has_gt': True, # A list of names for each of you classes. 'class_names': ('water'), # 修改成自己的class name 是一个truple # COCO class ids aren't sequential, so this is a bandage fix. If your ids aren't sequential, # provide a map from category_id -> index in class_names + 1 (the +1 is there because it's 1-indexed). # If not specified, this just assumes category ids start at 1 and increase sequentially. 'label_map': {1:1} # 修改成自己的label map }) ``` + 修改数据名称和`num_classes`。（`num_classes=数据集标签类数+1`） ``` # ----------------------- YOLACT v1.0 CONFIGS ----------------------- # yolact_base_config = coco_base_config.copy({ 'name': 'yolact_base', # Dataset stuff 'dataset': dataset_base, # 数据源 'num_classes': 1 + 1, # 类别数量 = label + 1 # Image Size 'max_size': 550, # Training params 'lr_steps': (280000, 600000, 700000, 750000), 'max_iter': 800000, ``` ### 4.训练 + 下载预训练权重放至根目录里的`weights`文件夹下 - For Resnet101, download resnet101_reducedfc.pth from [here](https://drive.google.com/file/d/1tvqFPd4bJtakOlmn-uIA492g2qurRChj/view?usp=sharing). - For Resnet50, download resnet50-19c8e357.pth from [here](https://drive.google.com/file/d/1Jy3yCdbatgXa5YYIdTCRrSV0S9V5g1rn/view?usp=sharing). - For Darknet53, download darknet53.pth from [here](https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing). + 在训练过程中按`ctrl+c`能够保存当前训练权重并保存在weight文件夹里。命名为：`__.pth` + 训练 使用默认为8的batch size训练： ``` $ python3 train.py --config=yolact_base_config ``` 根据根据实际情况可以适当调节batch size的大小，如果显卡性能低可以减小batch size大小： ``` $ python3 train.py --config=yolact_base_config --batch_size=5 ``` 从中断的权重继续训练： ``` $ python3 train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1 ``` ``` $ python3 train.py --config=yolact_plus_resnet50_config ``` 帮助： ``` $ python3 train.py --help ``` ### 5.推断 + image ``` # Display qualitative results on the specified image. $ python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=my_image.png # Process an image and save it to another file. $ python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --image=input_image.png:output_image.png # Process a whole folder of images. $ python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder $ python3 eval.py --trained_model=weights/yolact_plus_resnet50_1086_100000.pth --score_threshold=0.15 --top_k=15 --images=./test:./result ``` + video ``` # Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance. # If you want, use "--display_fps" to draw the FPS directly on the frame. $ python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4 # Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0. $ python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0 # Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast! $ python3 eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=input_video.mp4:output_video.mp4 $ python3 eval.py --trained_model=weights/yolact_plus_resnet50_1086_100000.pth --score_threshold=0.50 --top_k=15 --video_multiframe=4 --display_fps --video=test.mp4:test_res.mp4 python3 eval.py --trained_model=weights/yolact_plus_resnet50_1086_100000.pth --score_threshold=0.50 --top_k=15 --display_fps --video=test.mp4:test_res.mp4 ``` + help ``` python3 eval.py --help ``` ![](./data/test.png) ### 6.Referecne ``` @inproceedings{yolact-iccv2019, author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee}, title = {YOLACT: {Real-time} Instance Segmentation}, booktitle = {ICCV}, year = {2019}, } ``` For YOLACT++, please cite ``` @misc{yolact-plus-arxiv2019, title = {YOLACT++: Better Real-time Instance Segmentation}, author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee}, year = {2019}, eprint = {1912.06218}, archivePrefix = {arXiv}, primaryClass = {cs.CV} } ```