Instructions to use csr2000/UCF_Crime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csr2000/UCF_Crime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="csr2000/UCF_Crime") 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("csr2000/UCF_Crime") model = AutoModelForImageClassification.from_pretrained("csr2000/UCF_Crime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from csr2000/UCF_Crime: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/csr2000/UCF_Crime/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://csr2000/UCF_Crime/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/csr2000/UCF_Crime/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 45ef0aecb31473c329feffe0cca909f9cffd76e7596c5feaf57455eaf39d3e77
- Size of remote file:
- 343 MB
- SHA256:
- 61ab938a03614c93b41688b9c3283851221c09c739a4bf505ccd2286212c2305
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.