Image Classification
Transformers
Safetensors
English
siglip
Human
Non-Human
Detection
SigLIP2
Vision-Encoder
Instructions to use prithivMLmods/Human-vs-NonHuman-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Human-vs-NonHuman-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Human-vs-NonHuman-Detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Human-vs-NonHuman-Detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Human-vs-NonHuman-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-978/optimizer.pt from prithivMLmods/Human-vs-NonHuman-Detection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Human-vs-NonHuman-Detection/resolve/main/checkpoint-978/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Human-vs-NonHuman-Detection/checkpoint-978/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Human-vs-NonHuman-Detection/resolve/main/checkpoint-978/optimizer.pt
687 MB
- Xet hash:
- fbcb0e802289c3b82242ea8882c5b585b540e7fa6bc0a254e7b5e69a40508f43
- Size of remote file:
- 687 MB
- SHA256:
- 6d29a990632438c1845a0bd58b1a08b1e0fc97323c0fbb4f38fb5f9d0f49b6b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.