Instructions to use microsoft/resnet-101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-101") 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-101") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-101") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by amyeroberts - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=8.914e-04; Maximum converted output difference=8.914e-04.
All crossload differences
logits: 3.719e-05
hidden_states[0]: 1.907e-05
hidden_states[1]: 1.310e-04
hidden_states[2]: 4.309e-04
hidden_states[3]: 8.914e-04
hidden_states[4]: 8.297e-05
amyeroberts changed pull request status to merged