Instructions to use DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning") 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-FineTuning") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSelectedSubsetBasedModel-FineTuning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae86f785958d7f213aceab61f3d99acfe6cf2c6ba49c29409311624e33802e8b
- Size of remote file:
- 103 MB
- SHA256:
- 6cb059dc8c825a94fc6dc747b1ccdb767f8da63b136f7bd5ae6b67dff885687a
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