Instructions to use swjin/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swjin/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="swjin/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("swjin/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("swjin/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from swjin/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/swjin/cppe5_use_data_finetuning/resolve/8ec00ea069b1ea6cd807fd42ff74702ef0dde9b5/pytorch_model.bin
- Command line
-
hf download hf://swjin/cppe5_use_data_finetuning@8ec00ea069b1ea6cd807fd42ff74702ef0dde9b5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/swjin/cppe5_use_data_finetuning/resolve/8ec00ea069b1ea6cd807fd42ff74702ef0dde9b5/pytorch_model.bin
167 MB
- Xet hash:
- 4bd834ce60887a55c06d490e88bb7d6a710c24f0cd904f3c226486736ade9b85
- Size of remote file:
- 167 MB
- SHA256:
- e5b63beda6d98b1a57ea7d80625af92fc17a4d1b7a211f4ed869b734450506d8
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