Image Classification
Transformers
Safetensors
English
siglip
Road-Subsigns-Classification
SigLIP2
Traffic
Instructions to use prithivMLmods/Road-Subsigns-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Road-Subsigns-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Road-Subsigns-Classification") 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/Road-Subsigns-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Road-Subsigns-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-873/optimizer.pt from prithivMLmods/Road-Subsigns-Classification: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Road-Subsigns-Classification/resolve/main/checkpoint-873/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Road-Subsigns-Classification/checkpoint-873/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Road-Subsigns-Classification/resolve/main/checkpoint-873/optimizer.pt
687 MB
- Xet hash:
- d0638684abc58a03c95d60f94f92003500378127b3ea1b48946a98e0d42b46c5
- Size of remote file:
- 687 MB
- SHA256:
- cdfc32b436433b42dfb4316a266d1f81b378b619945ebf35b760ebaafe52bdb1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.