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:
- 060dbe4d22b8a5c106ff918920b7f5d0d0c0437958b1839383b967001ffd53fc
- Size of remote file:
- 103 MB
- SHA256:
- 3da1f592b08c210557313f2be2ca8509ce1094df106c52e62bb34e98a321e53a
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