|
--- |
|
language: en |
|
license: cc-by-nc-4.0 |
|
pipeline_tag: keypoint-detection |
|
tags: |
|
- sapiens |
|
--- |
|
|
|
# Pose-Sapiens-0.3B-Torchscript |
|
|
|
### Model Details |
|
Sapiens is a family of vision transformers pretrained on 300 million human images at 1024 x 1024 image resolution. The pretrained models, when finetuned for human-centric vision tasks, generalize to in-the-wild conditions. |
|
Sapiens-0.3B natively support 1K high-resolution inference. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. |
|
|
|
- **Developed by:** Meta |
|
- **Model type:** Vision Transformer |
|
- **License:** Creative Commons Attribution-NonCommercial 4.0 |
|
- **Task:** pose |
|
- **Format:** torchscript |
|
- **File:** sapiens_0.3b_goliath_best_goliath_AP_573_torchscript.pt2 |
|
|
|
### Model Card |
|
- **Image Size:** 1024 x 768 (H x W) |
|
- **Num Parameters:** 0.336 B |
|
- **FLOPs:** 1.242 TFLOPs |
|
- **Patch Size:** 16 x 16 |
|
- **Embedding Dimensions:** 1024 |
|
- **Num Layers:** 24 |
|
- **Num Heads:** 16 |
|
- **Feedforward Channels:** 4096 |
|
|
|
### More Resources |
|
- **Repository:** [https://github.com/facebookresearch/sapiens](https://github.com/facebookresearch/sapiens) |
|
- **Paper:** [https://arxiv.org/abs/2408.12569](https://arxiv.org/abs/2408.12569) |
|
- **Demo:** [https://huggingface.co/spaces/facebook/sapiens-pose](https://huggingface.co/spaces/facebook/sapiens-pose) |
|
- **Project Page:** [https://about.meta.com/realitylabs/codecavatars/sapiens](https://about.meta.com/realitylabs/codecavatars/sapiens/) |
|
- **Additional Results:** [https://rawalkhirodkar.github.io/sapiens](https://rawalkhirodkar.github.io/sapiens/) |
|
- **HuggingFace Collection:** [https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc](https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc) |
|
|
|
## Uses |
|
Pose 0.3B model can be used for estimate 308 keypoints (body + face + hands + feet) on a single image. |