Instructions to use DeepLearner101/ResNet50FTImageNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/ResNet50FTImageNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/ResNet50FTImageNet") 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/ResNet50FTImageNet") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/ResNet50FTImageNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2058afef0c9ecea874fbdbfba841d9614c5d6e2a03bbd0336cc947d59febb895
- Size of remote file:
- 103 MB
- SHA256:
- 7431de96da9aeef074edcc25ba8363c307ddfb3bf80cc2184b1969e942c1bc7b
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