Instructions to use Francesco/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet50") 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("Francesco/resnet50") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Francesco/resnet50: direct link, hf CLI and curl.
- Browser
- Download file 103 MB
-
https://huggingface.co/Francesco/resnet50/resolve/496e7981ccc24f3d83d9bd8791e76664df9c3ecf/pytorch_model.bin
- Command line
-
hf download hf://Francesco/resnet50@496e7981ccc24f3d83d9bd8791e76664df9c3ecf/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Francesco/resnet50/resolve/496e7981ccc24f3d83d9bd8791e76664df9c3ecf/pytorch_model.bin
103 MB
- Xet hash:
- fabdea41495fef3e6ff8ea648bd2a162e511346f8c09886b358ad4870517c31b
- Size of remote file:
- 103 MB
- SHA256:
- 2ca1e41e35781d74fcce44d7f00e3a17b9d6592162e3a15c37f74b4a3c8a266d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.