DETRs with Collaborative Hybrid Assignments Training

[📖 Paper] [🤗 Hugging Face Model]

News

• [07/21/2024] Check out our Co-DETR detection and segmentation checkpoints, fine-tuned on COCO and LVIS, now available on Hugging Face. We've achieved new state-of-the-art performance in instance segmentation!
• [04/22/2024] We release a new MLLM framework MoVA, which adopts Co-DETR as the vision and achieves state-of-the-art performance on multimodal benchmarks.
• [10/19/2023] Our SOTA model Co-DETR w/ ViT-L is released now. Please refer to our huggingface page for more details.
• [09/10/2023] We release LVIS inference configs and a stronger LVIS detector that achieves 64.5 box AP.
• [08/21/2023] Our O365 pre-trained Co-DETR with Swin-L achieves 64.8 AP on COCO test-dev. The config and weights are released.
• [07/20/2023] Code for Co-DINO is released: 55.4 AP with ResNet-50 and 60.7 AP with Swin-L.
• [07/14/2023] Co-DETR is accepted to ICCV 2023!
• [07/12/2023] We finetune Co-DETR on LVIS and achieve the best results without TTA: 72.0 box AP and 59.7 mask AP on LVIS minival, 68.0 box AP and 56.0 mask AP on LVIS val. For instance segmentation, we report the performance of the auxiliary mask branch.
• [07/03/2023] Co-DETR with ViT-L (304M parameters) sets a new record of 65.6 66.0 AP on COCO test-dev, surpassing the previous best model InternImage-G (~3000M parameters). It is the first model to exceed 66.0 AP on COCO test-dev.
• [07/03/2023] Code for Co-Deformable-DETR is released.
• [04/05/2023] HoP leverages Co-DETR as the backbone and achieves new SOTA performance on nuScenes 3D detection leaderboard.
• [11/19/2022] We achieved 64.4 AP on COCO minival and 64.5 AP on COCO test-dev with only ImageNet-1K as pre-training data. Codes will be available soon.

Introduction

In this paper, we present a novel collaborative hybrid assignments training scheme, namely Co-DETR, to learn more efficient and effective DETR-based detectors from versatile label assignment manners.

1. Encoder optimization: The proposed training scheme can easily enhance the encoder's learning ability in end-to-end detectors by training multiple parallel auxiliary heads supervised by one-to-many label assignments.
2. Decoder optimization: We conduct extra customized positive queries by extracting the positive coordinates from these auxiliary heads to improve attention learning of the decoder.
3. State-of-the-art performance: Co-DETR with ViT-L (304M parameters) is the first model to achieve 66.0 AP on COCO test-dev.

Model Zoo

Objects365 pre-trained Co-DETR

Model	Backbone	Aug	Dataset	box AP (val)	mask AP (val)	box AP (test)	mask AP (test)	Config	Download
Co-DINO	Swin-L	DETR	COCO	64.1	-	-	-	config	model
Co-DINO	Swin-L	LSJ	LVIS	64.5	-	-	-	config (test)	model
Co-DINO	ViT-L	DETR	Objects365	-	-	-	-	config	model
Co-DINO	ViT-L	DETR	COCO	65.9	-	66.0	-	config	model
Co-DINO	ViT-L	LSJ	LVIS	68.0	-	-	-	config (test)	model
Co-DINO-Inst	ViT-L	LSJ	LVIS	67.3	60.7	-	-	config (test)	model

Co-DETR with ResNet-50

Model	Backbone	Epochs	Aug	Dataset	box AP	Config	Download
Co-DINO	R50	12	DETR	COCO	52.1	config	model
Co-DINO	R50	12	LSJ	COCO	52.1	config	model
Co-DINO-9enc	R50	12	LSJ	COCO	52.6	config	model
Co-DINO	R50	36	LSJ	COCO	54.8	config	model
Co-DINO-9enc	R50	36	LSJ	COCO	55.4	config	model

Co-DETR with Swin-L

Model	Backbone	Epochs	Aug	Dataset	box AP	Config	Download
Co-DINO	Swin-L	12	DETR	COCO	58.9	config	model
Co-DINO	Swin-L	24	DETR	COCO	59.8	config	model
Co-DINO	Swin-L	36	DETR	COCO	60.0	config	model
Co-DINO	Swin-L	12	LSJ	COCO	59.3	config	model
Co-DINO	Swin-L	24	LSJ	COCO	60.4	config	model
Co-DINO	Swin-L	36	LSJ	COCO	60.7	config	model
Co-DINO	Swin-L	36	LSJ	LVIS	56.9	config (test)	model

Co-Deformable-DETR

Model	Backbone	Epochs	Queries	box AP	Config	Download
Co-Deformable-DETR	R50	12	300	49.5	config	model | log
Co-Deformable-DETR	Swin-T	12	300	51.7	config	model | log
Co-Deformable-DETR	Swin-T	36	300	54.1	config	model | log
Co-Deformable-DETR	Swin-S	12	300	53.4	config	model | log
Co-Deformable-DETR	Swin-S	36	300	55.3	config	model | log
Co-Deformable-DETR	Swin-B	12	300	55.5	config	model | log
Co-Deformable-DETR	Swin-B	36	300	57.5	config	model | log
Co-Deformable-DETR	Swin-L	12	300	56.9	config	model | log
Co-Deformable-DETR	Swin-L	36	900	58.5	config	model | log

Running

Install

We implement Co-DETR using MMDetection V2.25.3 and MMCV V1.5.0. The source code of MMdetection has been included in this repo and you only need to build MMCV following official instructions. We test our models under python=3.7.11,pytorch=1.11.0,cuda=11.3. Other versions may not be compatible.

Data

The COCO dataset and LVIS dataset should be organized as:

Co-DETR
└── data
    ├── coco
    │   ├── annotations
    │   │      ├── instances_train2017.json
    │   │      └── instances_val2017.json
    │   ├── train2017
    │   └── val2017
    │
    └── lvis_v1
        ├── annotations
        │      ├── lvis_v1_train.json
        │      └── lvis_v1_val.json
        ├── train2017
        └── val2017        

Training

Train Co-Deformable-DETR + ResNet-50 with 8 GPUs:

sh tools/dist_train.sh projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py 8 path_to_exp

Train using slurm:

sh tools/slurm_train.sh partition job_name projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py path_to_exp

Testing

Test Co-Deformable-DETR + ResNet-50 with 8 GPUs, and evaluate:

sh tools/dist_test.sh  projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py path_to_checkpoint 8 --eval bbox

Test using slurm:

sh tools/slurm_test.sh partition job_name projects/configs/co_deformable_detr/co_deformable_detr_r50_1x_coco.py path_to_checkpoint --eval bbox

Cite Co-DETR

If you find this repository useful, please use the following BibTeX entry for citation.

@inproceedings{zong2023detrs,
  title={Detrs with collaborative hybrid assignments training},
  author={Zong, Zhuofan and Song, Guanglu and Liu, Yu},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={6748--6758},
  year={2023}
}

License

This project is released under the MIT license. Please see the LICENSE file for more information.