Instructions to use DeepLearner101/ImageNetSubsetForFineTuningBasedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ImageNetSubsetForFineTuningBasedModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ImageNetSubsetForFineTuningBasedModel") 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/ImageNetSubsetForFineTuningBasedModel") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ImageNetSubsetForFineTuningBasedModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec539513fd38d25dc0dc4f890af27d1375a3cc5f90aa61b45abb0b4527136270
- Size of remote file:
- 103 MB
- SHA256:
- bf015d66fb8a6f01af4c8b4af7a1bda5c7070f2a53d187a05a09fa1419db288a
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