Instructions to use dmc2334/use_data_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmc2334/use_data_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="dmc2334/use_data_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("dmc2334/use_data_finetuning") model = AutoModelForObjectDetection.from_pretrained("dmc2334/use_data_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dmc2334/use_data_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/dmc2334/use_data_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dmc2334/use_data_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dmc2334/use_data_finetuning/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- d96e47bf87fc3fe1cae5c9fabbda0801d828b56b5ae9100124a97d704e25c0c3
- Size of remote file:
- 167 MB
- SHA256:
- d9bc389bdbc94259890cae96043a192df7bf813a60c3cc81246b8332f3098023
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