Instructions to use bin20/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bin20/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="bin20/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("bin20/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("bin20/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bin20/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/d2d69b73ab968ec2a5121dba8691799bd2fc2b07/pytorch_model.bin
- Command line
-
hf download hf://bin20/cppe5_use_data_finetuning@d2d69b73ab968ec2a5121dba8691799bd2fc2b07/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bin20/cppe5_use_data_finetuning/resolve/d2d69b73ab968ec2a5121dba8691799bd2fc2b07/pytorch_model.bin
167 MB
- Xet hash:
- c0ed18b3c81f43785fb923f61fcde3c002a1a0c11dddb86a536af064b484746d
- Size of remote file:
- 167 MB
- SHA256:
- bfdf1741b650ec89ae81fef29f82159161d4f94146de5d7736e044f66dd633d7
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