Instructions to use SLBM/streetvision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLBM/streetvision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SLBM/streetvision") 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("SLBM/streetvision") model = AutoModelForImageClassification.from_pretrained("SLBM/streetvision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update model.safetensors after SN72 fine-tune
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Update preprocessor after SN72 fine-tune
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Update ViT weights after SN72 fine-tune (5 epochs)
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Upload folder using huggingface_hub
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initial commit
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