Instructions to use amd/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amd/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amd/resnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("amd/resnet50") model = AutoModelForImageClassification.from_pretrained("amd/resnet50") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f09bb3164237eb50fa0746de09c06512e1973725b600dfa5228f9b6ada735b1
- Size of remote file:
- 102 MB
- SHA256:
- b80356db2a2cfdaca2f8d389b4bb2d605db4e202737cbb6e7b7b49c3ce9059da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.