This model classifies facial skin images into 6 common dermatological conditions using a fine-tuned EfficientNetV2B0 architecture. - Evaluated on a balanced, augmented custom dataset of real-world dermatological images. - Confusion Matrix indicates strong separation for all classes, with minor overlap in visually similar conditions. - Global Average Pooling + Dense(512, ReLU) + Dropout(0.4) + Dense(6, Softmax) - Fine-tuned with learning rate scheduling and layer unfreezing Softmax probabilities for 6 classes. argmax(pred) gives class index from 0–5. Tanishq Shinde B.E. Computer Engineering, PICT
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Published tasks: Image classification.
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