dronefreak/co-dino-5scale-vit-l-coco

Co-DINO (ViT-L (304M, EVA-02), Objects365 pretrain, COCO fine-tune, COCO)

Disclaimer

This is not an official release. All credit for the method, the architecture, and the trained weights belongs entirely to the original authors: Zhuofan Zong, Guanglu Song, and Yu Liu (SenseTime X-Lab). I claim no contribution to the underlying research or training. I host the same checkpoint the authors published to Google Drive, with a model card and a copy of its config file.

Mirror rationale

This checkpoint is already on the authors' own Hub (zongzhuofan/co-detr-vit-large-coco), so availability isn't the issue the way it is for the Google-Drive-only checkpoints elsewhere in this zoo. I mirror it anyway for consistency: the authors' repo ships a bare weights file with no config, so it isn't runnable on its own. This mirror bundles the matching config, a demo banner made with this checkpoint, and the same card format as every other checkpoint here.

I mirror only the pretrained weights; I do not redistribute any training or evaluation data. I will transfer or remove this mirror at the original authors' request. Please cite the original paper (see Citation) and use the official repository or the maintained fork dronefreak/Co-DETR for code.

---

Quickstart

This is not a plain-PyTorch / transformers model. Inference needs the Co-DETR project code and the OpenMMLab 1.x stack (MMDetection 2.25.3, MMCV-full 1.5.0, PyTorch 1.11). The maintained fork ships a one-command environment setup for exactly this.

# 1. Get the code + a validated environment
git clone https://github.com/dronefreak/Co-DETR && cd Co-DETR
bash tools/setup_codetr_env.sh          # builds the `codetr` conda env
conda activate codetr

# 2. Pull this checkpoint + its config from the Hub
pip install -U "huggingface_hub[cli]"
hf download dronefreak/co-dino-5scale-vit-l-coco \
    co_dino_5scale_vit_large_coco.pth co_dino_5scale_vit_large_coco.py \
    --local-dir checkpoints/

# 3. Run detection on an image / folder / video / webcam
python tools/inference.py \
    --config checkpoints/co_dino_5scale_vit_large_coco.py \
    --checkpoint checkpoints/co_dino_5scale_vit_large_coco.pth \
    --input demo/demo.jpg --out-dir outputs/ --save-json

The bundled config is identical to projects/configs/co_dino_vit/co_dino_5scale_vit_large_coco.py in the repo.

---

What is Co-DETR / this checkpoint

Co-DETR ("DETRs with Collaborative Hybrid Assignments Training") is a training scheme, not a new architecture. It attaches auxiliary heads that use one-to-many label assignment (an ATSS head and a Faster-R-CNN-style RoI head) alongside the DETR decoder's one-to-one Hungarian matching, and feeds the positive proposals those heads produce back into the decoder as extra queries. This makes the encoder's features more discriminative and speeds up convergence. The auxiliary heads are used only during training: this checkpoint's config sets eval_module='detr', so at inference only the Co-DINO query head runs and the RPN / RoI / ATSS heads add no inference-time computation to the deployed detector.

This checkpoint is Co-DINO (5 feature scales) with a ViT-L (304M, EVA-02) backbone:

Detector	CoDETR meta-model; deployed head is CoDINOHead
Backbone	ViT-L (304M, EVA-02), pretrained on EVA-02 MIM self-supervised pretrain, then Objects365 supervised pretrain
Object queries	1500
Aux heads in state-dict	RPNHead + CoStandardRoIHead, CoATSSHead: present in the weights but not executed at inference (eval_module='detr')
Parameters	348.1 M executed at inference (backbone + neck + query head). The checkpoint additionally stores the training-only RPN / RoI / ATSS heads (365.4 M total), which inference never touches.
Classes	80 (COCO things)
Training schedule	Objects365 pretrain -> COCO train2017 fine-tune, Objects365 pretrain, COCO fine-tune
Test-time input	single scale, no flip / no TTA (the config's test pipeline; see Measured inference footprint for the actual resized size on the sample image)

• Paper: DETRs with Collaborative Hybrid Assignments Training, ICCV 2023
• Config: co_dino_5scale_vit_large_coco.py (bundled; identical to the Co-DETR repo copy)

---

Intended use & limitations

Intended use. Research and engineering work that needs an object detector in the Co-DETR family trained on COCO val2017: as a baseline, a feature/detection backbone, or a starting point for fine-tuning on your own dataset.

Limitations.

• Trained and evaluated on COCO only; predicts the 80 COCO thing categories and nothing else.
• Accuracy can degrade substantially on domain-shifted imagery (aerial, medical, document, non-natural images, etc.).
• The reported 65.9 box AP is the authors' number; I have not reproduced it (see Evaluation).
• No real-time claim. See measured latency for the actual timings I got.
• Runs only on the legacy OpenMMLab 1.x stack (MMDetection 2.25.3 / MMCV-full 1.5.0 / PyTorch 1.11); no transformers / pipeline() support, and inference: false on the Hub.
• The auxiliary training heads in the checkpoint are not exposed as inference outputs; only the DETR query head's detections are returned.

---

What I checked

Integrity and "does it load and run" checks only. This is not an accuracy reproduction:

• It loads in the codetr env (Python 3.8 / torch 1.11.0+cu113 / mmcv-full 1.5.0 / mmdet 2.25.3): not a full state-dict key match: unexpected keys: 892 ema_* (a full exponential-moving-average shadow copy of the weights, saved during training and unused at inference), none affecting inference.
• I ran it end-to-end through tools/inference.py on the two dashcam clips shown in the banner above (CPU); it produced valid annotated outputs.
• I did not re-measure COCO val2017 AP; the 65.9 box AP below is the authors' number.

Measured inference footprint

Method. I built the model with mmdet.apis.init_detector, then ran inference_detector() on a single image, device='cpu', batch size 1, FP32: one warm-up call, then timed calls with time.perf_counter(). No GPU was used for this checkpoint: mine was occupied by another job at the time I mirrored it, so I fell back to CPU for both this table and the demo banner above (same code path, just --device cpu; see tools/inference.py). Peak memory is the process's peak resident set size (resource.getrusage(RUSAGE_SELF).ru_maxrss), a high-water mark since process start, not attributable to a single call the way the CUDA allocator's counters are for the GPU-measured cards in this zoo.

Item	Value
Device	CPU only: Intel(R) Core(TM) i5-14600KF (20 logical cores)
Stack	Python 3.8, torch 1.11.0+cu113 (CPU path), mmcv-full 1.5.0, mmdet 2.25.3
Precision / batch	FP32 / 1
Params executed at inference	348.1 M
Params stored in the checkpoint	365.4 M
Test image	demo/demo.jpg -> 1280 x 1920 fed to backbone
Peak resident memory (process, since start)	17387 MiB (17.80 GB)
Latency / image (CPU, warm)	mean 45820 ms (45549-46091, n=3)

This model has not been timed on a GPU in this repository. Expect CPU latency in the tens of seconds per image for this backbone/decoder size; COCO val2017 inference at scale needs a GPU.

---

Evaluation (as reported by the original authors)

Benchmark	Metric	Value	Source	Reproduced here?
COCO val2017	box AP	65.9	Co-DETR paper (arXiv:2211.12860) / official model zoo	No

Schedule: Objects365 pretrain, COCO fine-tune, 1500 object queries, single-scale test, no TTA. I have not independently re-measured these numbers.

---

Training data

• Objects365 pretraining: ~2M images, 365 categories. Shao et al., Objects365: A Large-Scale, High-Quality Dataset for Object Detection, ICCV 2019 (paper). Objects365's terms restrict use to non-commercial research; see License status.
• COCO 2017 train2017: 118k images, 80 things categories. Lin et al., Microsoft COCO: Common Objects in Context, ECCV 2014 (arXiv:1405.0312).
• Backbone initialization: ViT-L (304M, EVA-02) pretrained on EVA-02 MIM self-supervised pretrain, then Objects365 supervised pretrain.

I do not redistribute COCO here. Get it from cocodataset.org. This checkpoint's backbone/detector was pretrained on Objects365 before the fine-tune above. Unlike the plain-COCO checkpoints in this zoo, Objects365's non-commercial-research terms are a real constraint here, not a hypothetical one.

---

License status

The Co-DETR source code is MIT-licensed (© 2022 SenseTime X-Lab, LICENSE). The upstream repository does not provide a separate, explicit license for this checkpoint's weights. The original authors have published other Co-DETR checkpoints (the ViT-L family) on Hugging Face with MIT metadata, but I do not treat that as definitive evidence that these weights are independently licensed under MIT.

Because the applicable rights are undetermined, I set the Hub metadata for this repo to license: unknown. Verify the applicable rights yourself before redistribution or commercial use.

⚠️ Objects365 pretrain. This checkpoint's backbone/detector was pretrained on the Objects365 dataset before being fine-tuned on COCO. Objects365's own terms restrict it to non-commercial research use (Objects365 terms); that restriction plausibly carries through to weights derived from it. Treat this checkpoint as research-only unless you independently confirm otherwise; this is a stronger caveat than the general license: unknown above, not a substitute for it.

If you are one of the original authors and want this mirror removed or transferred, open an issue on this repo or contact dronefreak and I will action it.

---

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 (ICCV)},
  pages={6748--6758},
  year={2023}
}

The underlying detector and backbone:

@inproceedings{zhu2021deformable,
  title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
  author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2021}
}
@article{fang2023eva02,
  title={EVA-02: A Visual Representation for Neon Genesis},
  author={Fang, Yuxin and Sun, Quan and Wang, Xinggang and Huang, Tiejun and Wang, Xinlong and Cao, Yue},
  journal={arXiv preprint arXiv:2303.11331},
  year={2023}
}
@inproceedings{shao2019objects365,
  title={Objects365: A Large-Scale, High-Quality Dataset for Object Detection},
  author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019}
}

Built with MMDetection / MMCV (OpenMMLab).

---

Acknowledgements

I sincerely thank Zhuofan Zong, Guanglu Song, and Yu Liu for developing Co-DETR and releasing the pretrained weights, and the OpenMMLab team for MMDetection and MMCV. This mirror only makes one of their existing checkpoints easier and more reliable to obtain.

---

Provenance

co_dino_5scale_vit_large_coco.pth, 2,934,763,233 bytes, SHA-256 733d2ccde180a55151a68a6cab7c9f42b117d24d38d6197b37caf3189243256c. I copied it byte-for-byte and unmodified from the authors' own Hugging Face repo zongzhuofan/co-detr-vit-large-coco.

Repo files: co_dino_5scale_vit_large_coco.pth (weights), co_dino_5scale_vit_large_coco.py (the MMDetection config needed to build the model), config.json (metadata summary; also what the Hub uses to count downloads), plus assets/demo_banner.mp4 / assets/demo_banner_poster.jpg for the card. config.json is descriptive only, inference: false, and is not a transformers config.