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https://github.com/facebookresearch/detic
Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".
https://github.com/facebookresearch/detic
Last synced: about 1 month ago
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Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".
- Host: GitHub
- URL: https://github.com/facebookresearch/detic
- Owner: facebookresearch
- License: apache-2.0
- Created: 2021-12-11T18:38:12.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-21T20:23:43.000Z (8 months ago)
- Last Synced: 2024-10-09T13:23:56.090Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 3.44 MB
- Stars: 1,866
- Watchers: 21
- Forks: 211
- Open Issues: 57
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Detecting Twenty-thousand Classes using Image-level Supervision
**Detic**: A **Det**ector with **i**mage **c**lasses that can use image-level labels to easily train detectors.
> [**Detecting Twenty-thousand Classes using Image-level Supervision**](http://arxiv.org/abs/2201.02605),
> Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra,
> *ECCV 2022 ([arXiv 2201.02605](http://arxiv.org/abs/2201.02605))*
## Features
- Detects **any** class given class names (using [CLIP](https://github.com/openai/CLIP)).
- We train the detector on ImageNet-21K dataset with 21K classes.
- Cross-dataset generalization to OpenImages and Objects365 **without finetuning**.
- State-of-the-art results on Open-vocabulary LVIS and Open-vocabulary COCO.
- Works for DETR-style detectors.
## Installation
See [installation instructions](docs/INSTALL.md).
## Demo
**Update April 2022**: we released more real-time models [here](docs/MODEL_ZOO.md#real-time-models).
Replicate web demo and docker image: [](https://replicate.com/facebookresearch/detic)
Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the web demo: [](https://huggingface.co/spaces/akhaliq/Detic)
Run our demo using Colab (no GPU needed): [](https://colab.research.google.com/drive/1QtTW9-ukX2HKZGvt0QvVGqjuqEykoZKI)
We use the default detectron2 [demo interface](https://github.com/facebookresearch/detectron2/blob/main/GETTING_STARTED.md).
For example, to run our [21K model](docs/MODEL_ZOO.md#cross-dataset-evaluation) on a [messy desk image](https://web.eecs.umich.edu/~fouhey/fun/desk/desk.jpg) (image credit [David Fouhey](https://web.eecs.umich.edu/~fouhey)) with the lvis vocabulary, run
~~~
mkdir models
wget https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth -O models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth
wget https://eecs.engin.umich.edu/~fouhey/fun/desk/desk.jpg
python demo.py --config-file configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml --input desk.jpg --output out.jpg --vocabulary lvis --opts MODEL.WEIGHTS models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth
~~~
If setup correctly, the output should look like:
The same model can run with other vocabularies (COCO, OpenImages, or Objects365), or a **custom vocabulary**. For example:
~~~
python demo.py --config-file configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml --input desk.jpg --output out2.jpg --vocabulary custom --custom_vocabulary headphone,webcam,paper,coffe --confidence-threshold 0.3 --opts MODEL.WEIGHTS models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth
~~~
The output should look like:
Note that `headphone`, `paper` and `coffe` (typo intended) are **not** LVIS classes. Despite the misspelled class name, our detector can produce a reasonable detection for `coffe`.
## Benchmark evaluation and training
Please first [prepare datasets](datasets/README.md), then check our [MODEL ZOO](docs/MODEL_ZOO.md) to reproduce results in our paper. We highlight key results below:
- Open-vocabulary LVIS
| | mask mAP | mask mAP_novel |
|-----------------------|-----------|-----------------|
|Box-Supervised | 30.2 | 16.4 |
|Detic | 32.4 | 24.9 |
- Standard LVIS
| | Detector/ Backbone | mask mAP | mask mAP_rare |
|-----------------------|----------|-----------|-----------------|
|Box-Supervised | CenterNet2-ResNet50 | 31.5 | 25.6 |
|Detic | CenterNet2-ResNet50 | 33.2 | 29.7 |
|Box-Supervised | CenterNet2-SwinB | 40.7 | 35.9 |
|Detic | CenterNet2-SwinB | 41.7 | 41.7 |
| | Detector/ Backbone | box mAP | box mAP_rare |
|-----------------------|----------|-----------|-----------------|
|Box-Supervised | DeformableDETR-ResNet50 | 31.7 | 21.4 |
|Detic | DeformableDETR-ResNet50 | 32.5 | 26.2 |
- Cross-dataset generalization
| | Backbone | Objects365 box mAP | OpenImages box mAP50 |
|-----------------------|----------|-----------|-----------------|
|Box-Supervised | SwinB | 19.1 | 46.2 |
|Detic | SwinB | 21.4 | 55.2 |
## License
The majority of Detic is licensed under the [Apache 2.0 license](LICENSE), however portions of the project are available under separate license terms: SWIN-Transformer, CLIP, and TensorFlow Object Detection API are licensed under the MIT license; UniDet is licensed under the Apache 2.0 license; and the LVIS API is licensed under a [custom license](https://github.com/lvis-dataset/lvis-api/blob/master/LICENSE). If you later add other third party code, please keep this license info updated, and please let us know if that component is licensed under something other than CC-BY-NC, MIT, or CC0
## Ethical Considerations
Detic's wide range of detection capabilities may introduce similar challenges to many other visual recognition and open-set recognition methods.
As the user can define arbitrary detection classes, class design and semantics may impact the model output.
## Citation
If you find this project useful for your research, please use the following BibTeX entry.
@inproceedings{zhou2022detecting,
title={Detecting Twenty-thousand Classes using Image-level Supervision},
author={Zhou, Xingyi and Girdhar, Rohit and Joulin, Armand and Kr{\"a}henb{\"u}hl, Philipp and Misra, Ishan},
booktitle={ECCV},
year={2022}
}