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- Overview
- Features
- Method Overview
- Semi-supervised and Fully-supervised Learning
- License
- Ethical Considerations
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- Citation
Cut-and-LEaRn (CutLER) is a simple approach for training object detection and instance segmentation models without human annotations. It outperforms previous SOTA by 2.7 times for AP50 and 2.6 times for AR on 11 benchmarks.
[project page] [arxiv] [colab] [bibtex]
Unsupervised video instance segmentation (VideoCutLER) is also supported. We demonstrate that video instance segmentation models can be learned without using any human annotations, without relying on natural videos (ImageNet data alone is sufficient), and even without motion estimations! The code is available here.
[code] [PDF] [arxiv] [bibtex]
•We propose MaskCut approach to generate pseudo-masks for multiple objects in an image.
•CutLER can learn unsupervised object detectors and instance segmentors solely on ImageNet-1K.
•CutLER exhibits strong robustness to domain shifts when evaluated on 11 different benchmarks across domains like natural images, video frames, paintings, sketches, etc.
•CutLER can serve as a pretrained model for fully/semi-supervised detection and segmentation tasks.
1. MaskCut
MaskCut can be used to provide segmentation masks for multiple instances of each image.
MaskCut Demo
Try out the MaskCut demo using Colab (no GPU needed): Try out the web demo: (thanks to @hysts!) If you want to run MaskCut locally, we provide demo.py that is able to visualize the pseudo-masks produced by MaskCut. Run it with: We give a few demo images in maskcut/imgs/. If you want to run demo.py with cpu, simply add "--cpu" when running the demo script. For imgs/demo4.jpg, you need to use "--N 6" to segment all six instances in the image. Following, we give some visualizations of the pseudo-masks on the demo images.
Generating Annotations for ImageNet-1K with MaskCut
To generate pseudo-masks for ImageNet-1K using MaskCut, first set up the ImageNet-1K dataset according to the instructions in datasets/README.md, then execute the following command: As the process of generating pseudo-masks for all 1.3 million images in 1,000 folders takes a significant amount of time, it is recommended to use multiple runs. Each run should process the pseudo-mask generation for a smaller number of image folders by setting "--num-folder-per-job" and "--job-index". Once all runs are completed, you can merge all the resulting json files by using the following command: The "--num-folder-per-job", "--fixed-size", "--tau" and "--N" of merge_jsons.py should match the ones used to run maskcut.py. We also provide a submitit script to launch the pseudo-mask generation process with multiple nodes. After that, you can use "merge_jsons.py" to merge all these json files as described above.
Training & Evaluation in Command Line
You can find all the semi-supervised and fully-supervised learning configs provided in CutLER under model_zoo/configs/COCO-Semisupervised. To train a model using K% labels with train_net.py, first set up the COCO dataset according to datasets/README.md and specify K value in the config file, then run: You can find all config files used to train supervised models under model_zoo/configs/COCO-Semisupervised. The configs are made for 8-GPU training. To train on 1 GPU, you may need to change some parameters, e.g. number of GPUs (num-gpus your_num_gpus), learning rates (SOLVER.BASE_LR your_base_lr) and batch size (SOLVER.IMS_PER_BATCH your_batch_size).
Evaluation
To evaluate a model's performance, use For more options, see python train_net.py -h.
Model Zoo
We fine-tune a Cascade R-CNN model initialized with CutLER or MoCo-v2 on varying amounts of labeled COCO data, and show results (Box | Mask AP) on the val2017 split below: Both MoCo-v2 and our CutLER are trained for the 1x schedule using Detectron2, except for extremely low-shot settings with 1% or 2% labels. When training with 1% or 2% labels, we train both MoCo-v2 and our model for 3,600 iterations with a batch size of 16.
The majority of CutLER, Detectron2 and DINO are licensed under the CC-BY-NC license, however portions of the project are available under separate license terms: TokenCut, Bilateral Solver and CRF are licensed under the MIT license; If you later add other third party code, please keep this license info updated, and please let us know if that compone...
CutLER's wide range of detection capabilities may introduce similar challenges to many other visual recognition methods. As the image can contain arbitrary instances, it may impact the model output.
If you have any general questions, feel free to email us at Xudong Wang, Ishan Misra and Rohit Girdhar. If you have code or implementation-related questions, please feel free to send emails to us or open an issue in this codebase (We recommend that you open an issue in this codebase, because your questions may help others).
If you find our work inspiring or use our codebase in your research, please consider giving a star ⭐ and a citation.
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Cutler Park (Visalia, California), municipal park. The Cutler River (New Hampshire), on Mount Washington. Cutler Majestic Theatre, an opera house, Boston, Massachusetts. Cutler Memorial Library, an historic library in Farmington, Maine. Cutler Reservoir, in Utah.
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