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:
- e3f10b88a9a4f79d8c614567b3cbbccb4814982c85f3d04bd7e8d2b614d7db9e
- Size of remote file:
- 103 MB
- SHA256:
- f1b4293cf5d2725a254711b19ea633e0551c1d568e3d3e832dde38d16d9bdc86
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