# yolact **Repository Path**: nnyy123/yolact ## Basic Information - **Project Name**: yolact - **Description**: A simple, fully convolutional model for real-time instance segmentation. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-06-12 - **Last Updated**: 2021-11-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s ``` ██╗ ██╗ ██████╗ ██╗ █████╗ ██████╗████████╗ ╚██╗ ██╔╝██╔═══██╗██║ ██╔══██╗██╔════╝╚══██╔══╝ ╚████╔╝ ██║ ██║██║ ███████║██║ ██║ ╚██╔╝ ██║ ██║██║ ██╔══██║██║ ██║ ██║ ╚██████╔╝███████╗██║ ██║╚██████╗ ██║ ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ ``` A simple, fully convolutional model for real-time instance segmentation. This is the code for our papers: - [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) - [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218) #### YOLACT++ (v1.2) released! ([Changelog](CHANGELOG.md)) YOLACT++'s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO's `test-dev` (check out our journal paper [here](https://arxiv.org/abs/1912.06218)). In order to use YOLACT++, make sure you compile the DCNv2 code. (See [Installation](https://github.com/dbolya/yolact#installation)) #### For a real-time demo, check out our ICCV video: [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg)](https://www.youtube.com/watch?v=0pMfmo8qfpQ) Some examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO's `test-dev`): ![Example 0](data/yolact_example_0.png) ![Example 1](data/yolact_example_1.png) ![Example 2](data/yolact_example_2.png) # Installation - Clone this repository and enter it: ```Shell git clone https://github.com/dbolya/yolact.git cd yolact ``` - Set up the environment using one of the following methods: - Using [Anaconda](https://www.anaconda.com/distribution/) - Run `conda env create -f environment.yml` - Manually with pip - Set up a Python3 environment (e.g., using virtenv). - Install [Pytorch](http://pytorch.org/) 1.0.1 (or higher) and TorchVision. - Install some other packages: ```Shell # Cython needs to be installed before pycocotools pip install cython pip install opencv-python pillow pycocotools matplotlib ``` - If you'd like to train YOLACT, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into `./data/coco`. ```Shell sh data/scripts/COCO.sh ``` - If you'd like to evaluate YOLACT on `test-dev`, download `test-dev` with this script. ```Shell sh data/scripts/COCO_test.sh ``` - If you want to use YOLACT++, compile deformable convolutional layers (from [DCNv2](https://github.com/CharlesShang/DCNv2/tree/pytorch_1.0)). Make sure you have the latest CUDA toolkit installed from [NVidia's Website](https://developer.nvidia.com/cuda-toolkit). ```Shell cd external/DCNv2 python setup.py build develop ``` # Evaluation Here are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on `test-dev`: | Image Size | Backbone | FPS | mAP | Weights | | |:----------:|:-------------:|:----:|:----:|----------------------------------------------------------------------------------------------------------------------|--------| | 550 | Resnet50-FPN | 42.5 | 28.2 | [yolact_resnet50_54_800000.pth](https://drive.google.com/file/d/1yp7ZbbDwvMiFJEq4ptVKTYTI2VeRDXl0/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EUVpxoSXaqNIlssoLKOEoCcB1m0RpzGq_Khp5n1VX3zcUw) | | 550 | Darknet53-FPN | 40.0 | 28.7 | [yolact_darknet53_54_800000.pth](https://drive.google.com/file/d/1dukLrTzZQEuhzitGkHaGjphlmRJOjVnP/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/ERrao26c8llJn25dIyZPhwMBxUp2GdZTKIMUQA3t0djHLw) | 550 | Resnet101-FPN | 33.5 | 29.8 | [yolact_base_54_800000.pth](https://drive.google.com/file/d/1UYy3dMapbH1BnmtZU4WH1zbYgOzzHHf_/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EYRWxBEoKU9DiblrWx2M89MBGFkVVB_drlRd_v5sdT3Hgg) | 700 | Resnet101-FPN | 23.6 | 31.2 | [yolact_im700_54_800000.pth](https://drive.google.com/file/d/1lE4Lz5p25teiXV-6HdTiOJSnS7u7GBzg/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/Eagg5RSc5hFEhp7sPtvLNyoBjhlf2feog7t8OQzHKKphjw) YOLACT++ models (released on December 16th, 2019): | Image Size | Backbone | FPS | mAP | Weights | | |:----------:|:-------------:|:----:|:----:|----------------------------------------------------------------------------------------------------------------------|--------| | 550 | Resnet50-FPN | 33.5 | 34.1 | [yolact_plus_resnet50_54_800000.pth](https://drive.google.com/file/d/1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EcJAtMiEFlhAnVsDf00yWRIBUC4m8iE9NEEiV05XwtEoGw) | | 550 | Resnet101-FPN | 27.3 | 34.6 | [yolact_plus_base_54_800000.pth](https://drive.google.com/file/d/15id0Qq5eqRbkD-N3ZjDZXdCvRyIaHpFB/view?usp=sharing) | [Mirror](https://ucdavis365-my.sharepoint.com/:u:/g/personal/yongjaelee_ucdavis_edu/EVQ62sF0SrJPrl_68onyHF8BpG7c05A8PavV4a849sZgEA) To evalute the model, put the corresponding weights file in the `./weights` directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., `yolact_base` for `yolact_base_54_800000.pth`). ## Quantitative Results on COCO ```Shell # Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above. # This should get 29.92 validation mask mAP last time I checked. python eval.py --trained_model=weights/yolact_base_54_800000.pth # Output a COCOEval json to submit to the website or to use the run_coco_eval.py script. # This command will create './results/bbox_detections.json' and './results/mask_detections.json' for detection and instance segmentation respectively. python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json # You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py. python run_coco_eval.py # To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset ``` ## Qualitative Results on COCO ```Shell # Display qualitative results on COCO. From here on I'll use a confidence threshold of 0.15. python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --display ``` ## Benchmarking on COCO ```Shell # Run just the raw model on the first 1k images of the validation set python eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000 ``` ## Images ```Shell # Display qualitative results on the specified image. python 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. python 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. python 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 ``` ## Video ```Shell # 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. python 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. python 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! python 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 ``` As you can tell, `eval.py` can do a ton of stuff. Run the `--help` command to see everything it can do. ```Shell python eval.py --help ``` # Training By default, we train on COCO. Make sure to download the entire dataset using the commands above. - To train, grab an imagenet-pretrained model and put it in `./weights`. - 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). - Run one of the training commands below. - Note that you can press ctrl+c while training and it will save an `*_interrupt.pth` file at the current iteration. - All weights are saved in the `./weights` directory by default with the file name `__.pth`. ```Shell # Trains using the base config with a batch size of 8 (the default). python train.py --config=yolact_base_config # Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly. python train.py --config=yolact_base_config --batch_size=5 # Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file's name. python train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1 # Use the help option to see a description of all available command line arguments python train.py --help ``` ## Multi-GPU Support YOLACT now supports multiple GPUs seamlessly during training: - Before running any of the scripts, run: `export CUDA_VISIBLE_DEVICES=[gpus]` - Where you should replace [gpus] with a comma separated list of the index of each GPU you want to use (e.g., 0,1,2,3). - You should still do this if only using 1 GPU. - You can check the indices of your GPUs with `nvidia-smi`. - Then, simply set the batch size to `8*num_gpus` with the training commands above. The training script will automatically scale the hyperparameters to the right values. - If you have memory to spare you can increase the batch size further, but keep it a multiple of the number of GPUs you're using. - If you want to allocate the images per GPU specific for different GPUs, you can use `--batch_alloc=[alloc]` where [alloc] is a comma seprated list containing the number of images on each GPU. This must sum to `batch_size`. ## Logging YOLACT now logs training and validation information by default. You can disable this with `--no_log`. A guide on how to visualize these logs is coming soon, but now you can look at `LogVizualizer` in `utils/logger.py` for help. ## Pascal SBD We also include a config for training on Pascal SBD annotations (for rapid experimentation or comparing with other methods). To train on Pascal SBD, proceed with the following steps: 1. Download the dataset from [here](http://home.bharathh.info/pubs/codes/SBD/download.html). It's the first link in the top "Overview" section (and the file is called `benchmark.tgz`). 2. Extract the dataset somewhere. In the dataset there should be a folder called `dataset/img`. Create the directory `./data/sbd` (where `.` is YOLACT's root) and copy `dataset/img` to `./data/sbd/img`. 4. Download the COCO-style annotations from [here](https://drive.google.com/open?id=1ExrRSPVctHW8Nxrn0SofU1lVhK5Wn0_S). 5. Extract the annotations into `./data/sbd/`. 6. Now you can train using `--config=yolact_resnet50_pascal_config`. Check that config to see how to extend it to other models. I will automate this all with a script soon, don't worry. Also, if you want the script I used to convert the annotations, I put it in `./scripts/convert_sbd.py`, but you'll have to check how it works to be able to use it because I don't actually remember at this point. If you want to verify our results, you can download our `yolact_resnet50_pascal_config` weights from [here](https://drive.google.com/open?id=1yLVwtkRtNxyl0kxeMCtPXJsXFFyc_FHe). This model should get 72.3 mask AP_50 and 56.2 mask AP_70. Note that the "all" AP isn't the same as the "vol" AP reported in others papers for pascal (they use an averages of the thresholds from `0.1 - 0.9` in increments of `0.1` instead of what COCO uses). ## Custom Datasets You can also train on your own dataset by following these steps: - Create a COCO-style Object Detection JSON annotation file for your dataset. The specification for this can be found [here](http://cocodataset.org/#format-data). Note that we don't use some fields, so the following may be omitted: - `info` - `liscense` - Under `image`: `license, flickr_url, coco_url, date_captured` - `categories` (we use our own format for categories, see below) - Create a definition for your dataset under `dataset_base` in `data/config.py` (see the comments in `dataset_base` for an explanation of each field): ```Python my_custom_dataset = dataset_base.copy({ 'name': 'My Dataset', 'train_images': 'path_to_training_images', 'train_info': 'path_to_training_annotation', 'valid_images': 'path_to_validation_images', 'valid_info': 'path_to_validation_annotation', 'has_gt': True, 'class_names': ('my_class_id_1', 'my_class_id_2', 'my_class_id_3', ...) }) ``` - A couple things to note: - Class IDs in the annotation file should start at 1 and increase sequentially on the order of `class_names`. If this isn't the case for your annotation file (like in COCO), see the field `label_map` in `dataset_base`. - If you do not want to create a validation split, use the same image path and annotations file for validation. By default (see `python train.py --help`), `train.py` will output validation mAP for the first 5000 images in the dataset every 2 epochs. - Finally, in `yolact_base_config` in the same file, change the value for `'dataset'` to `'my_custom_dataset'` or whatever you named the config object above. Then you can use any of the training commands in the previous section. #### Creating a Custom Dataset from Scratch See [this nice post by @Amit12690](https://github.com/dbolya/yolact/issues/70#issuecomment-504283008) for tips on how to annotate a custom dataset and prepare it for use with YOLACT. # Citation If you use YOLACT or this code base in your work, please cite ``` @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} } ``` # Contact For questions about our paper or code, please contact [Daniel Bolya](mailto:dbolya@ucdavis.edu).