https://github.com/dataxujing/yolact_pytorch
:fire: :fire: :fire:Train Your Own DataSet for YOLACT and YOLACT++ Instance Segmentation Model!!!
https://github.com/dataxujing/yolact_pytorch
computer-vision convolutional-neural-networks deep-learning instance-segmentation pytorch yolact
Last synced: 11 months ago
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:fire: :fire: :fire:Train Your Own DataSet for YOLACT and YOLACT++ Instance Segmentation Model!!!
- Host: GitHub
- URL: https://github.com/dataxujing/yolact_pytorch
- Owner: DataXujing
- License: mit
- Created: 2020-02-18T21:08:55.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-02-20T15:36:08.000Z (over 6 years ago)
- Last Synced: 2025-04-04T13:23:27.618Z (about 1 year ago)
- Topics: computer-vision, convolutional-neural-networks, deep-learning, instance-segmentation, pytorch, yolact
- Language: Python
- Homepage:
- Size: 10.1 MB
- Stars: 68
- Watchers: 2
- Forks: 14
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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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
```
### 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}
}
```