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:
- 1657b2750d879145f26a424854f681b3920a2cb0f88b89f6a45a8614aa4d952b
- Size of remote file:
- 3.38 kB
- SHA256:
- cb65a76519b3bc3c3a9a5b6ba910c58f0ab131a131e65fa03c4e68edf49a0306
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.