Instructions to use Donglin90/sapiens-normal-2b-torchscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sapiens
How to use Donglin90/sapiens-normal-2b-torchscript with sapiens:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: cc-by-nc-4.0
tags:
- sapiens
Normal-Sapiens-2B-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-2B 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: normal
- Format: torchscript
- File: sapiens_2b_normal_render_people_epoch_70_torchscript.pt2
Model Card
- Image Size: 1024 x 768 (H x W)
- Num Parameters: 2.163 B
- FLOPs: 8.709 TFLOPs
- Patch Size: 16 x 16
- Embedding Dimensions: 1920
- Num Layers: 48
- Num Heads: 32
- Feedforward Channels: 7680
More Resources
- Repository: https://github.com/facebookresearch/sapiens
- Paper: https://arxiv.org/abs/2408.12569
- Demo: https://huggingface.co/spaces/facebook/sapiens-normal
- Project Page: https://about.meta.com/realitylabs/codecavatars/sapiens
- Additional Results: https://rawalkhirodkar.github.io/sapiens
- HuggingFace Collection: https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc
Uses
Normal 2B model can be used to estimate surface normal (XYZ) on human images.