Instructions to use littlebird13/resnet-50-web with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use littlebird13/resnet-50-web with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="littlebird13/resnet-50-web") 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("littlebird13/resnet-50-web") model = AutoModelForImageClassification.from_pretrained("littlebird13/resnet-50-web", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from littlebird13/resnet-50-web: direct link, hf CLI and curl.
- Browser
- Download file 102 MB
-
https://huggingface.co/littlebird13/resnet-50-web/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://littlebird13/resnet-50-web/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/littlebird13/resnet-50-web/resolve/main/flax_model.msgpack
102 MB
- Xet hash:
- f03c53f73f734d156fb44c4939de4434895544bc125206d6473ee7b08dc6009b
- Size of remote file:
- 102 MB
- SHA256:
- 1dba0174a596673117b14b7b97d27bbfef1928a94377b18b4cb849b9c8569b90
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