Instructions to use MLLabIISc/ModHiFi-ResNet50-ImageNet-Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLLabIISc/ModHiFi-ResNet50-ImageNet-Tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MLLabIISc/ModHiFi-ResNet50-ImageNet-Tiny", trust_remote_code=True) 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("MLLabIISc/ModHiFi-ResNet50-ImageNet-Tiny", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("MLLabIISc/ModHiFi-ResNet50-ImageNet-Tiny", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbc227017f9d6601ec744c5288c2eb6a709a6374899d7f601ba6b0f1f579fde7
- Size of remote file:
- 33.7 MB
- SHA256:
- 7c1f7c7196f786b25da0b6f9bb67ae60b61ea95234cffaabf8f74d789b53d149
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.