Instructions to use Andron00e/CLIPForImageClassification-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/CLIPForImageClassification-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/CLIPForImageClassification-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("Andron00e/CLIPForImageClassification-v1") model = AutoModelForImageClassification.from_pretrained("Andron00e/CLIPForImageClassification-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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processor = CLIPProcessor.from_pretrained("Andron00e/CLIPForImageClassification-v1")
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model = AutoModelForImageClassification.from_pretrained("Andron00e/CLIPForImageClassification-v1")
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dataset = load_dataset("Andron00e/
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dataset = dataset["train"].train_test_split(test_size=0.2)
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from datasets import DatasetDict
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processor = CLIPProcessor.from_pretrained("Andron00e/CLIPForImageClassification-v1")
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model = AutoModelForImageClassification.from_pretrained("Andron00e/CLIPForImageClassification-v1")
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dataset = load_dataset("Andron00e/CIFAR10-custom")
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dataset = dataset["train"].train_test_split(test_size=0.2)
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from datasets import DatasetDict
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