Instructions to use microsoft/resnet-152 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-152 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-152") 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-152") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-152") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01e699e35ac72dc3f704a02a4e125565ab0f92137352307b988764e06f31f00c
- Size of remote file:
- 242 MB
- SHA256:
- 8daaaa74fcdeff48b2c2466c83802dc9b7a02aa3639f60ad251c3dc798517ae7
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