Instructions to use nenzilea/car-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nenzilea/car-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nenzilea/car-classification") 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("nenzilea/car-classification") model = AutoModelForImageClassification.from_pretrained("nenzilea/car-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- config.json +16 -14
- model.safetensors +2 -2
config.json
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"hidden_size": 768,
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"id2label": {
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"0": "BMW",
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"1": "
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"2": "
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"BMW": 0,
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"hidden_size": 768,
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"id2label": {
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"0": "BMW",
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"1": "Dodge",
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"2": "Ferrari",
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"3": "Ford",
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"4": "Jeep",
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"5": "Lamborghini",
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"6": "Porsche",
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"7": "Rolls-Royce",
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"8": "Toyota"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"BMW": 0,
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"Dodge": 1,
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"Ferrari": 2,
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"Ford": 3,
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"Jeep": 4,
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"Lamborghini": 5,
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"Porsche": 6,
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"Rolls-Royce": 7,
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"Toyota": 8
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},
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee16a6570f48674c971a62c3c767b2464a4a7b88bb544b1554fef6fc55ab3311
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size 343245508
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