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/16d8a5554583ec6eeb4d58b6fb3dd6736e20b4aa/pytorch_model.bin
- Command line
-
hf download hf://swjin/cppe5_use_data_finetuning@16d8a5554583ec6eeb4d58b6fb3dd6736e20b4aa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/swjin/cppe5_use_data_finetuning/resolve/16d8a5554583ec6eeb4d58b6fb3dd6736e20b4aa/pytorch_model.bin
167 MB
- Xet hash:
- d349b1b20ca72d8f0fdbf728fe9564f95b6e286b8a9b67100aee6c7219d100fe
- Size of remote file:
- 167 MB
- SHA256:
- 3905de61f1b58ae071dbf25bb7f7f88275280c55aebc24dd2f11e0be8c07226f
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