Instructions to use wnsdud030415/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnsdud030415/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="wnsdud030415/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("wnsdud030415/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("wnsdud030415/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wnsdud030415/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/wnsdud030415/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wnsdud030415/cppe5_use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wnsdud030415/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 6680285573246a5695d433ecd1d6026e8c8d58c8587c8ba50dca3e01e12e531e
- Size of remote file:
- 167 MB
- SHA256:
- bae3d3f300c09c14520d327130e64e92f795c4fc8acae7e5749f95467e50f89d
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