Image Classification
Transformers
PyTorch
TensorBoard
resnet
Generated from Trainer
Eval Results (legacy)
Instructions to use SiddharthaM/resnet-18-feature-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SiddharthaM/resnet-18-feature-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SiddharthaM/resnet-18-feature-extraction") 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("SiddharthaM/resnet-18-feature-extraction") model = AutoModelForImageClassification.from_pretrained("SiddharthaM/resnet-18-feature-extraction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3c04d1e0590e24c6c6acef91671aa094d3ca0cc7d076daa2f26687eac69d97f
- Size of remote file:
- 44.8 MB
- SHA256:
- b0216682802b08cfae03ccddd163dfb1cc827ba7be19fb8d48a91a8354b08617
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