Instructions to use DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning") 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("DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSelectedSubsetBasedModel-TransferLearning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cd315465ecde4880b1f5e3dc3e8dbb648823242ff832c2b842626296ee67c96
- Size of remote file:
- 103 MB
- SHA256:
- 7a707c1fafb5ce49ff569fb9fd54344e3cfadfa341a997d5c6828eb2e5151c1b
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