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
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# outputs
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This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on an
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It achieves the following results on the evaluation set:
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- Loss: 0.8115
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- Accuracy: 0.8255
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# outputs
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This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on an [CIFAR10](https://huggingface.co/datasets/Andron00e/CIFAR10-custom) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8115
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- Accuracy: 0.8255
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