Instructions to use microsoft/resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-50") 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("microsoft/resnet-50") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50") - Inference
- Notebooks
- Google Colab
- Kaggle
Flax implementation https://github.com/huggingface/transformers/pull/21472 @sanchit-gandhi
#3
by Shubhamai - opened
No description provided.
@nielsr would you mind merging these Flax ResNet weights when you get the chance? Transformers PR: https://github.com/huggingface/transformers/pull/21472
Thanks for working on this!
nielsr changed pull request status to merged