Instructions to use prithivMLmods/imagenet-50-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/imagenet-50-subset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/imagenet-50-subset") 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/imagenet-50-subset") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/imagenet-50-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c49713d58a925c39cb0af93647f0534eb3dda26261b4bf79431ce8743e3c79a3
- Size of remote file:
- 5.3 kB
- SHA256:
- d8d23e4dd21d70dc3decbc150e22ae936ad50be95a366a34502fabd29ae06140
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.