Image Classification
Transformers
Safetensors
vit
vision-transformer
flowers
Generated from Trainer
Eval Results (legacy)
Instructions to use lst0004/flower-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lst0004/flower-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lst0004/flower-vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("lst0004/flower-vit") model = AutoModelForImageClassification.from_pretrained("lst0004/flower-vit") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
config.json
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"pooler_output_size": 768,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"transformers_version": "5.5.
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"use_cache": false
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}
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"pooler_output_size": 768,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"transformers_version": "5.5.4",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8ea12d3eebe50ec740385df549f7c3a4a2f36d1cce2af390167dcd9ed45c8ef
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size 5201
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