Instructions to use schumannc/detect-chess-pieces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schumannc/detect-chess-pieces with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="schumannc/detect-chess-pieces")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("schumannc/detect-chess-pieces") model = AutoModelForObjectDetection.from_pretrained("schumannc/detect-chess-pieces", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from schumannc/detect-chess-pieces: direct link, hf CLI and curl.
- Browser
- Download file 123 MB
-
https://huggingface.co/schumannc/detect-chess-pieces/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://schumannc/detect-chess-pieces/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/schumannc/detect-chess-pieces/resolve/main/pytorch_model.bin
123 MB
- Xet hash:
- 3b5d3da10113c0fd7fa76e0f57c1e6173247a54af8b8ed6174eeaa9016e763f6
- Size of remote file:
- 123 MB
- SHA256:
- 44db4218fdf6725ae9a35fc1811484ad6dd8b6053b18a0c90eafe778a01c608f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.