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:
- 0206749ab27a2cd8a4f4fdd5ef3c4bf84f23c9936f3e6c4c0e37433dd2eb97ed
- Size of remote file:
- 103 MB
- SHA256:
- 52b6d862ed08942d0937629ac34b1e204107c370b4c026b0a4316104807e104e
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