Instructions to use microsoft/resnet-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-18") 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-18") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18") - Inference
- 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=2.861e-05; Maximum converted output difference=2.861e-05.
All cross-load differences:
logits: 1.252e-05
hidden_states[0]: 1.431e-06
hidden_states[1]: 1.384e-05
hidden_states[2]: 8.702e-06
hidden_states[3]: 7.004e-06
hidden_states[4]: 2.861e-05
amyeroberts changed pull request status to merged