Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

Related tags

Deep LearningDetic
Overview

Detecting Twenty-thousand Classes using Image-level Supervision

Detic: A Detector with image classes that can use image-level labels to easily train detectors.

Detecting Twenty-thousand Classes using Image-level Supervision,
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra,
arXiv technical report (arXiv 2201.02605)

Features

  • Detects any class given class names (using 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.

Demo

Integrated into Huggingface Spaces 🤗 using Gradio. Try out the web demo: Hugging Face Spaces

Run our demo using Colab (no GPU needed): Open In Colab

We use the default detectron2 demo interface. For example, to run our 21K model on a messy desk image (image credit David 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://web.eecs.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, then check our MODEL ZOO 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, 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{zhou2021detecting,
  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={arXiv preprint arXiv:2201.02605},
  year={2021}
}
Owner
Meta Research
Meta Research
Keyword spotting on Arm Cortex-M Microcontrollers

Keyword spotting for Microcontrollers This repository consists of the tensorflow models and training scripts used in the paper: Hello Edge: Keyword sp

Arm Software 1k Dec 30, 2022
COIN the currently largest dataset for comprehensive instruction video analysis.

COIN Dataset COIN is the currently largest dataset for comprehensive instruction video analysis. It contains 11,827 videos of 180 different tasks (i.e

86 Dec 28, 2022
A PaddlePaddle version image model zoo.

Paddle-Image-Models English | 简体中文 A PaddlePaddle version image model zoo. Install Package Install by pip: $ pip install ppim Install by wheel package

AgentMaker 131 Dec 07, 2022
Code for "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection", ICRA 2021

FGR This repository contains the python implementation for paper "FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection"(I

Yi Wei 31 Dec 08, 2022
You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

Huiyiqianli 42 Dec 06, 2022
MonoRCNN is a monocular 3D object detection method for automonous driving

MonoRCNN MonoRCNN is a monocular 3D object detection method for automonous driving, published at ICCV 2021. This project is an implementation of MonoR

87 Dec 27, 2022
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade

Sanghyun Son 2.1k Dec 27, 2022
Differentiable Surface Triangulation

Differentiable Surface Triangulation This is our implementation of the paper Differentiable Surface Triangulation that enables optimization for any pe

61 Dec 07, 2022
PSGAN running with ncnn⚡妆容迁移/仿妆⚡Imitation Makeup/Makeup Transfer⚡

PSGAN running with ncnn⚡妆容迁移/仿妆⚡Imitation Makeup/Makeup Transfer⚡

WuJinxuan 144 Dec 26, 2022
Synthetic structured data generators

Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati

YData 850 Jan 07, 2023
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer

BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer Project Page | Paper | Video State-of-the-art image-to-image translatio

47 Dec 06, 2022
A cross-lingual COVID-19 fake news dataset

CrossFake An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. Jiangshu Du,

Yingtong Dou 11 Dec 01, 2022
Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

Jooyoung Choi 88 Dec 01, 2022
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [BCNet, CVPR 2021] This is the official pytorch implementation of BCNet built on

Lei Ke 434 Dec 01, 2022
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation Ported from https://github.com/hzwer/arXiv2020-RIFE Dependencies NumPy

49 Jan 07, 2023
This project aims to be a handler for input creation and running of multiple RICEWQ simulations.

What is autoRICEWQ? This project aims to be a handler for input creation and running of multiple RICEWQ simulations. What is RICEWQ? From the descript

Yass Fuentes 1 Feb 01, 2022
Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore

[AI6122] Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instructor of this course

HT. Li 5 Sep 12, 2022
Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.

Video Representation Learning by Recognizing Temporal Transformations [Project Page] Simon Jenni, Givi Meishvili, and Paolo Favaro. In ECCV, 2020. Thi

Simon Jenni 46 Nov 14, 2022
Official PyTorch implementation of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" (AAAI 2021)

Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning Official PyTorch implementation of "Proxy Synthesis: Learning with Synthetic

NAVER/LINE Vision 30 Dec 06, 2022
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0

32 Nov 27, 2022