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-489/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-489/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Human-vs-NonHuman-Detection/checkpoint-489/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Human-vs-NonHuman-Detection/resolve/main/checkpoint-489/optimizer.pt
687 MB
- Xet hash:
- 706c228eb06bab78d8da0b8881beaab100cbf3ea9f3f21274737cbc713835bc5
- Size of remote file:
- 687 MB
- SHA256:
- abc371ff3ea4a8360dca1ed024f81270e423d94a4cdfb50ab5c1b9c2e8db1c0a
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