Instructions to use LiviaQi/trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiviaQi/trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="LiviaQi/trained_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("LiviaQi/trained_model") model = AutoModelForObjectDetection.from_pretrained("LiviaQi/trained_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LiviaQi/trained_model: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/LiviaQi/trained_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LiviaQi/trained_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LiviaQi/trained_model/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- c75ec1ffc6f77b4c8f632767481ced0af1183fd20e611449d74e053c00aeea9b
- Size of remote file:
- 167 MB
- SHA256:
- 9996b61178f9a70a7c80f8e55123a24237fbcf365b7a716e56f351bc3ec2b8c8
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