Tokenize Anything via Prompting

Ting Pan^1,2*, Lulu Tang^2*, Xinlong Wang^2¶, Shiguang Shan¹

¹ICT-CAS, ²BAAI
^* Equal Contribution, ^¶Project Lead

[Paper] [🤗 Demo]

We present Tokenize Anything via Prompting, a unified and promptable model capable of simultaneously segmenting, recognizing, and captioning arbitrary regions, with flexible visual prompts (point, box and sketch). The model is trained with exhaustive segmentation masks sourced from SA-1B, coupled with semantic priors from a pre-trained EVA-CLIP with 5 billion parameters.

Installation

See Github Page.

Models

Model weights

V1.1 Release Notes

Three versions of the model are available with different image encoders.
Use a longer pre-training and fine-tuning schedule (improved segmentation and caption performance).
Apply weight decay for all bias parameters (avoid FP16 overflow in QK matmul).
Sample point prompts from predicted mask instead of GT box during VG training.

Model	Description	Schedule	MD5	Weights
tap_vit_h	ViT-H TAP v1.1 model	(100% SA-1B, 180k), (VG, 50ep)	4bdfb9	🤗 HF link
tap_vit_l	ViT-L TAP v1.1 model	(100% SA-1B, 180k), (VG, 50ep)	c1d41f	🤗 HF link
tap_vit_b	ViT-B TAP v1.1 model	(100% SA-1B, 180k), (VG, 50ep)	707f80	🤗 HF link

V1.0 Release Notes

Two versions of the model are available with different image encoders.
Original paper results.

Model	Description	Schedule	MD5	Weights
tap_vit_l	ViT-L TAP v1.0 model	(50% SA-1B, 90k), (VG, 25ep)	03f8ec	🤗 HF link
tap_vit_b	ViT-B TAP v1.0 model	(50% SA-1B, 90k), (VG, 25ep)	b45cbf	🤗 HF link

Concept weights

Note: You can generate these weights following the Concept Guide.

Concept	Description	Weights
Merged-2560	Merged concepts	🤗 HF link
LVIS-1203	LVIS concepts	🤗 HF link
COCO-80	COCO concepts	🤗 HF link

License

Apache License 2.0

Citation

@article{pan2023tap,
  title={Tokenize Anything via Prompting},
  author={Pan, Ting and Tang, Lulu and Wang, Xinlong and Shan, Shiguang},
  journal={arXiv preprint arXiv:2312.09128},
  year={2023}
}

Acknowledgement

We thank the repositories: SAM, EVA, LLaMA, FlashAttention, Gradio, Detectron2 and CodeWithGPU.