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")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Road-Subsigns-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Road-Subsigns-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8ba38ae45a4c609d87cd30dd82181e97843633d2fe48ade01bc3023a05496c9
- Size of remote file:
- 5.3 kB
- SHA256:
- e7ec2d4b143bf3a91f4103e0cf2a281af2d0110d4d1d6466037642a17f2e2850
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.