metadata
tags:
- image-classification
- pytorch
- medical
- colon
metrics:
- f1
Vision Transformer fine-tuned on kvasir_v2 for colonoscopy classification
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Metrics
precision recall f1-score support
dyed-lifted-polyps 0.95 0.93 0.94 60
dyed-resection-margins 0.97 0.95 0.96 64
esophagitis 0.93 0.79 0.85 67
normal-cecum 1.00 0.98 0.99 54
normal-pylorus 0.95 1.00 0.97 57
normal-z-line 0.82 0.93 0.87 67
polyps 0.92 0.92 0.92 52
ulcerative-colitis 0.93 0.95 0.94 59
accuracy 0.93 480
macro avg 0.93 0.93 0.93 480
weighted avg 0.93 0.93 0.93 480
How to use
from transformers import ViTFeatureExtractor, ViTForImageClassification
from hugsvision.inference.VisionClassifierInference import VisionClassifierInference
path = "mrm8488/vit-base-patch16-224_finetuned-kvasirv2-colonoscopy"
classifier = VisionClassifierInference(
feature_extractor = ViTFeatureExtractor.from_pretrained(path),
model = ViTForImageClassification.from_pretrained(path),
)
img = "Your image path"
label = classifier.predict(img_path=img)
print("Predicted class:", label)
Disclaimer: This model was trained for research only
Created by Manuel Romero/@mrm8488 | LinkedIn
Made with ♥ in Spain