Instructions to use bazyl/gtsrb-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bazyl/gtsrb-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bazyl/gtsrb-model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("bazyl/gtsrb-model") model = AutoModelForImageClassification.from_pretrained("bazyl/gtsrb-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Add base_model information to model
This pull request aims to enrich the metadata of your model by adding [`google/vit-base-patch16-224-in21k`](https://huggingface.co/google/vit-base-patch16-224-in21k) as a `base_model` field, situated in the `YAML` block of your model's `README.md`. How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection. **Why add this?** Enhancing your model's metadata in this way: - **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub. - **Highlights Impact** - It showcases the contributions and influences different models have within the community. For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer). This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you ha…cc08016
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