Instructions to use kmc0003a/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmc0003a/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="kmc0003a/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("kmc0003a/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("kmc0003a/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kmc0003a/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/kmc0003a/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kmc0003a/cppe5_use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kmc0003a/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 632079fc0bac25d8d281a96c4e90e22d818a77e1379acfd2cdd3edf87e091475
- Size of remote file:
- 167 MB
- SHA256:
- ca2e8ef89a76345b2bf2c81f845bf5e900e8c61798ec404ece472ccf9a934d4e
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