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[T-PAMI 2022] Meta-DETR for Few-Shot Object Detection: Official PyTorch Implementation
| Date | Stars |
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| 2026-07-24 | 434 |
| 2026-07-25 | 434 |
| 2026-07-28 | 434 |
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| 2026-08-06 | 434 |
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# __[T-PAMI' 2022] Meta-DETR__ <br> (Official PyTorch Implementation) [](https://arxiv.org/abs/2208.00219) [](https://github.com/dk-liang/Awesome-Visual-Transformer) [](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) [](http://makeapullrequest.com) [](https://github.com/ZhangGongjie/Meta-DETR/blob/master/LICENSE) ------- This repository is the official PyTorch implementation of the T-PAMI 2022 paper "[Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation](https://doi.org/10.1109/TPAMI.2022.3195735)" by _Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, Shijian Lu, and Eric P. Xing_. <b> [ Important Notice ] </b> Meta-DETR first appeared as a tech report on arXiv.org (https://arxiv.org/abs/2103.11731v2) in 2021. Since its release, we have made substantial improvements to the original version. This repository corresponds to [the final published version accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) in 2022](https://doi.org/10.1109/TPAMI.2022.3195735). Please kindly be advised to refer to the latest version of the paper. ------- ## Brief Introduction Meta-DETR is a state-of-the-art few-shot object detector that performs image-level meta-learning-based prediction and effectively exploits the inter-class correlation to enhance generalization from old knowledge to new classes. Meta-DETR entirely bypasses the proposal quality gap between base and novel classes, thus achieving superior performance than R-CNN-based few-shot object detectors. In addition, Meta-DETR performs meta-learning on a set of support classes at one go, thus effectively leveraging the inter-class correlation for better generalization. <div align=center> <img src='.assets/motivation.jpg' width="60%"> </div> <div align=center> <img src='.assets/MetaDETR_architecture.jpg' width="95%"> </div> Please check [our T-PAMI paper](https://doi.org/10.1109/TPAMI.2022.3195735) or [its preprint version](https://arxiv.org/abs/2208.00219) for more details. ------- ## Installation ### Pre-Requisites You must have NVIDIA GPUs to run the codes. The implementation codes are developed and tested with the following environment setups: - Ubuntu LTS 18.04 - 8x NVIDIA V100 GPUs (32GB) - CUDA 10.2 - Python == 3.7 - PyTorch == 1.7.1+cu102, TorchVision == 0.8.2+cu102 - GCC == 7.5.0 - cython, pycocotools, tqdm, scipy We recommend using the exact setups above. However, other environments (Linux, Python>=3.7, CUDA>=9.2, GCC>=5.4, PyTorch>=1.5.1, TorchVision>=0.6.1) should also work properly. ### Code Installation First, clone the repository locally: ```shell git clone https://github.com/ZhangGongjie/Meta-DETR.git ``` We recommend you to use [Anaconda](https://www.anaconda.com/) to create a conda environment: ```bash conda create -n meta_detr python=3.7 pip ``` Then, activate the environment: ```bash conda activate meta_detr ``` Then, install PyTorch and TorchVision: (preferably using our recommended setups; CUDA version should match your own local environment) ```bash conda install pytorch=1.7.1 torchvision=0.8.2 cudatoolkit=10.2 -c pytorch ``` After that, install other requirements: ```bash conda install cython scipy tqdm pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' ``` As Meta-DETR is developed upon Deformable DETR, you need to compile [*Deformable Attention*](https://github.com/fundamentalvision/Deformable-DETR). ```bash # compile CUDA operators of Deformable Attention cd Meta-DETR cd ./models/ops sh ./make.sh python test.py # unit test
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:739b9385a8d0adda, topic:computer-vision, topic:object-detection, desc:object detection