| language: en | |
| license: cc-by-nc-4.0 | |
| pipeline_tag: image-segmentation | |
| tags: | |
| - sapiens | |
| # Seg-Sapiens-0.6B | |
| ### 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-0.6B 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:** seg | |
| - **Format:** original | |
| - **File:** sapiens_0.6b_goliath_best_goliath_mIoU_7777_epoch_178.pth | |
| ### Model Card | |
| - **Image Size:** 1024 x 768 (H x W) | |
| - **Num Parameters:** 0.664 B | |
| - **FLOPs:** 2.583 TFLOPs | |
| - **Patch Size:** 16 x 16 | |
| - **Embedding Dimensions:** 1280 | |
| - **Num Layers:** 32 | |
| - **Num Heads:** 16 | |
| - **Feedforward Channels:** 5120 | |
| ### 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-seg](https://huggingface.co/spaces/facebook/sapiens-seg) | |
| - **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 | |
| Seg 0.6B model can be used to perform 28 class body part segmentation on human images. | |