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--- |
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tags: |
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- image-classification |
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- pytorch |
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- medical |
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- colon |
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metrics: |
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- accuracy: 0.93 |
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--- |
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# Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification |
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## Demo |
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### Drag the following images to the widget to test the model |
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## Training |
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You can find the code [here](https://github.com/qanastek/HugsVision/blob/main/recipes/kvasir_v2/binary_classification/Kvasir_v2_Image_Classifier.ipynb) |
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## Metrics |
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``` |
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precision recall f1-score support |
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dyed-lifted-polyps 0.95 0.93 0.94 60 |
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dyed-resection-margins 0.97 0.95 0.96 64 |
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esophagitis 0.93 0.79 0.85 67 |
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normal-cecum 1.00 0.98 0.99 54 |
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normal-pylorus 0.95 1.00 0.97 57 |
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normal-z-line 0.82 0.93 0.87 67 |
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polyps 0.92 0.92 0.92 52 |
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ulcerative-colitis 0.93 0.95 0.94 59 |
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accuracy 0.93 480 |
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macro avg 0.93 0.93 0.93 480 |
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weighted avg 0.93 0.93 0.93 480 |
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``` |
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## How to use |
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```py |
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from transformers import ViTFeatureExtractor, ViTForImageClassification |
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from hugsvision.inference.VisionClassifierInference import VisionClassifierInference |
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path = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy" |
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classifier = VisionClassifierInference( |
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feature_extractor = ViTFeatureExtractor.from_pretrained(path), |
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model = ViTForImageClassification.from_pretrained(path), |
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) |
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img = "Your image path" |
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label = classifier.predict(img_path=img) |
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print("Predicted class:", label) |
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``` |
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> Disclaimer: This model was trained for research only |
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) |
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> Made with <span style="color: #e25555;">♥</span> in Spain |
