Instructions to use microsoft/resnet-34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-34") 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("microsoft/resnet-34") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-34", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/resnet-34: direct link, hf CLI and curl.
- Browser
- Download file 87.3 MB
-
https://huggingface.co/microsoft/resnet-34/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/resnet-34/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/resnet-34/resolve/main/pytorch_model.bin
87.3 MB
- Xet hash:
- c472b03a80a485976b91987feb7a326156497929b62f537f3f390095a87bbb2e
- Size of remote file:
- 87.3 MB
- SHA256:
- 75519549c18bebc6642677bcee247114fa24ce5ad4b1e4f4747be318a194ecb6
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