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:
- a6c12ddab6c884b1ad9fd4669661a7fcff2173a57b4b8eb5305c27d7779d74d1
- Size of remote file:
- 103 MB
- SHA256:
- f2305a090675529256766b7640162d103bd8810356599c23862ad7223a013a26
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