Instructions to use ansok/cppe5_use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ansok/cppe5_use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ansok/cppe5_use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ansok/cppe5_use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("ansok/cppe5_use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ansok/cppe5_use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/ansok/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ansok/cppe5_use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ansok/cppe5_use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- bd3dd12e6005a27a7b79e9cbe1a5a09ee10ef6b5b7d90b575280faf42a263e16
- Size of remote file:
- 167 MB
- SHA256:
- 2e3b2953b6194dd7df252c1849ed4cce15a2c947e81bc2e16eae23c5442c1f5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.