Instructions to use jhoppanne/Dogs-Breed-Image-Classification-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhoppanne/Dogs-Breed-Image-Classification-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jhoppanne/Dogs-Breed-Image-Classification-V1") 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("jhoppanne/Dogs-Breed-Image-Classification-V1") model = AutoModelForImageClassification.from_pretrained("jhoppanne/Dogs-Breed-Image-Classification-V1") - Notebooks
- Google Colab
- Kaggle
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# Dogs-Breed-Image-Classification-V1
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.4469
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- Accuracy: 0.8758
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# Dogs-Breed-Image-Classification-V1
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This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the the [Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.).
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It achieves the following results on the evaluation set:
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- Loss: 0.4469
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- Accuracy: 0.8758
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