Instructions to use Circularmachines/Batch_indexing_machine_ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Circularmachines/Batch_indexing_machine_ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Circularmachines/Batch_indexing_machine_ViT") 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("Circularmachines/Batch_indexing_machine_ViT") model = AutoModelForImageClassification.from_pretrained("Circularmachines/Batch_indexing_machine_ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Upload weights.h5 with huggingface_hub
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Training in progress epoch 4
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Training in progress epoch 3
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Training in progress epoch 2
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Training in progress epoch 1
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Training in progress epoch 0
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initial commit
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