Instructions to use mohitsha/resnet-tiny-beans-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitsha/resnet-tiny-beans-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mohitsha/resnet-tiny-beans-quantized") 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("mohitsha/resnet-tiny-beans-quantized") model = AutoModelForImageClassification.from_pretrained("mohitsha/resnet-tiny-beans-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_quantized.onnx from mohitsha/resnet-tiny-beans-quantized: direct link, hf CLI and curl.
- Browser
- Download file 780 kB
-
https://huggingface.co/mohitsha/resnet-tiny-beans-quantized/resolve/main/model_quantized.onnx
- Command line
-
hf download hf://mohitsha/resnet-tiny-beans-quantized/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/mohitsha/resnet-tiny-beans-quantized/resolve/main/model_quantized.onnx
780 kB
- Xet hash:
- ddbf12d8a348aec1f5733822cb3b33f6a694059b7cacc5784aba02e3ea6d4f46
- Size of remote file:
- 780 kB
- SHA256:
- b23f869de63d1158f0fc877f9c97210bd47b4c79fcd0f26d20687febfb9ddaaa
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