Instructions to use SoulPerforms/Butterfly_image_classification_resnet18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SoulPerforms/Butterfly_image_classification_resnet18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SoulPerforms/Butterfly_image_classification_resnet18") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SoulPerforms/Butterfly_image_classification_resnet18", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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metrics:
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- accuracy
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pipeline_tag: image-classification
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library_name: diffusers
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---
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Butterfly image classification model that use pre-trained cnn model resnet18 and fine-tuned the last fully connected layer to classify 75 categories of butterfly species.
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The model used the best checkpoint with 90% test accuracy. The model constructed on Pytorch environment.
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metrics:
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- accuracy
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pipeline_tag: image-classification
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---
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Butterfly image classification model that use pre-trained cnn model resnet18 and fine-tuned the last fully connected layer to classify 75 categories of butterfly species.
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The model used the best checkpoint with 90% test accuracy. The model constructed on Pytorch environment.
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