Instructions to use MLLabIISc/ModHiFi-ResNet50-ImageNet-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLLabIISc/ModHiFi-ResNet50-ImageNet-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MLLabIISc/ModHiFi-ResNet50-ImageNet-Small", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("MLLabIISc/ModHiFi-ResNet50-ImageNet-Small", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("MLLabIISc/ModHiFi-ResNet50-ImageNet-Small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from MLLabIISc/ModHiFi-ResNet50-ImageNet-Small: direct link, hf CLI and curl.
- Browser
- Download file 69.8 MB
-
https://huggingface.co/MLLabIISc/ModHiFi-ResNet50-ImageNet-Small/resolve/main/model.safetensors
- Command line
-
hf download hf://MLLabIISc/ModHiFi-ResNet50-ImageNet-Small/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MLLabIISc/ModHiFi-ResNet50-ImageNet-Small/resolve/main/model.safetensors
69.8 MB
- Xet hash:
- f2529675d1ebec8a5fb6f3ceefdc838244aed40dd99d8eae566274e17a1279d2
- Size of remote file:
- 69.8 MB
- SHA256:
- 90ddaef19fb7f8ea2bf7c748b4b581e84cd746651563c80b2a84d4164ca795aa
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