MegaKPT: A Large-Scale High-Quality GKD Dataset

Project

• GitHub: https://github.com/AlanLuSun/General-Keypoint-Detection

1. Introduction

MegaKPT dataset unifies 29 public keypoint datasets into the same annotation format, namely, COCO format, resulting over 1.3 million object instances. Moreover, we correct noisy annotations, supplement accurate keypoint texts, and give clear super-categories and indexes, rendering a high-quality and convenient-to-use dataset. To our best knowledge, MegaKPT is the largest unified dataset in the field. A glance of MegaKPT is shown in below image: All images have both keypoint and text annotations. (top) Visualize keypoints only; (bottom) Visualize both keypoints and texts. The dataset covers large diversity of objects from various scenes.

2. Statistics & Download

We credit each dataset source. The statistics of MegaKPT are as follows:

Super-category	Dataset	Category	Keypoint	Image	Instance
Human pose	COCO	1	17	66,808	273,469
	Human-Art	1	21	50,000	123,131
Human face	300W	1	68	600	600
	HELLEN	1	68	2,330	2,330
	AFW	1	68	337	337
	IBUG	1	68	135	135
	LFPW	1	68	1,035	1,035
	AFLW	1	21	25,993	25,993
Human limbs	OneHand10K	1	21	11,703	11,289
	HInt	1	21	17,281	17,281
Animal pose	Animal	5	20	4,666	6,117
	AwA pose	35	39	10,064	10,064
	CUB	200	15	11,788	11,788
	NABird	555	11	48,562	48,562
	AP-10K	54	17	10,015	13,028
	APT-36K	30	17	35,708	48,704
	MacaquePose	1	17	13,083	16,393
	ATRW (tiger)	1	15	2,830	2,830
	AcinoSet (cheetah)	1	20	5,795	5,795
	Animal Kingdom	850	23	33,000	99,267
	TopViewMouse-5K	1	27	5,000	5,000
Insect pose	Vinegar Fly	1	32	1,500	1,500
	Desert Locust	1	35	700	700
Animal face	AnimalWeb	350	9	22,451	21,921
Furniture	Keypoint-5	5	8–14	8,649	8,649
Vehicle	CarFusion	3	14	53,000	100,000
Clothes	DeepFashion2	13	8–39	491,000	491,000
Medical SC	Cephalometric	1	19	400	400
	Hand X-ray	1	37	910	910
Accumulated	29	1587	740	935,343	1,348,228

Download notes:

• We release the MegaKPT unified annotations of all component datasets in HuggingFace. For the datasets that are without hyperlinks in the above table, their image data is included as well since they may be not easily downloaded or have some corrections.
• For the datasets that are with hyperlinks, you can download their image data stably via the links shown in the above table, and then place the image data into the corresponding folder in MegaKPT. You can double check /your/path/to/General-Keypoint-Detection/datasets/dataset_meta_info.py which registers relative paths of each dataset.
• We provide your/path/to/MegaKPT/vehicle/carfusion/download_via_code.py to download the CarFusion dataset for your convenience
• Please use our provided unified annotation files in HuggingFace (instead of the original ones)
• We group 300W, HELLEN, AFW, IBUG, LFPW into one folder with the name 300w in our released MegaKPT as they share the same number of annotated facial keypoints.

MegaKPT folder layout:

|-- /your/path/to/MegaKPT/
|   |-- animal_face/
|   |   |-- animalweb/
|   |-- animal_pose/
|   |   |-- acinoset_cheetah/
|   |   |-- animal_kingdom/
|   |   |-- animal_pose_dataset/
|   |   |-- ap10k/
|   |   |-- apt36k/
|   |   |-- atrw_tiger/
|   |   |-- awa_pose/
|   |   |-- cub/
|   |   |-- macaque_pose/
|   |   |-- nabird/
|   |   |-- topviewmouse5k/
|   |-- clothes/
|   |   |-- deepfashion2/
|   |-- furniture/
|   |   |-- keypoint-5/
|   |-- human_face/
|   |   |-- 300w/
|   |   |-- aflw/
|   |-- human_limbs/
|   |   |-- hint/
|   |   |-- onehand10k/
|   |-- human_pose/
|   |   |-- coco/
|   |   |-- human_art/
|   |-- insect_pose/
|   |   |-- desert_locust/
|   |   |-- vinegar_fly/
|   |-- medical/
|   |   |-- cephalometric_landmark/
|   |   |-- hand_xray/
|   |-- vehicle/
|   |   |-- carfusion/

3. License

We do not own the copyrights to these images. Their use is restricted to non-commercial research and educational purposes.

4. Citation

If you find our work helpful, please give us a star and cite our paper, thank you!

@inproceedings{lu2026gkdt,
  title={GKDT: General Keypoint Detection Transformer},
  author={Lu, Changsheng and Chen, Yuxin and Gui, Haokun and Wang, Rong and Yang, Jie and Yang, Harry and Hengel, Anton van den and Jia, Jiaya},
  booktitle={European Conference on Computer Vision},
  year={2026},
  organization={Springer}
}
@inproceedings{lu2024openkd,
  title={Openkd: Opening prompt diversity for zero-and few-shot keypoint detection},
  author={Lu, Changsheng and Liu, Zheyuan and Koniusz, Piotr},
  booktitle={European Conference on Computer Vision},
  pages={148--165},
  year={2024},
  organization={Springer}
}
@inproceedings{lu2022few,
  title={Few-shot keypoint detection with uncertainty learning for unseen species},
  author={Lu, Changsheng and Koniusz, Piotr},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={19394--19404},
  year={2022},
  organization={IEEE}
}