VGG16 Fine-Grained Image Classifier

Step 1

Choose an image

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Drop an image here or browse PNG, JPEG, WebP, or BMP · maximum 10 MB

Try a packaged sample:

Selected image

Step 2

Prediction

Select an image and run inference to see the model output.

Predicted class —

— confidence

Class probabilities

Interpretation

Model card

What powers this demo

86.95%Test accuracy

86.95%Macro F1

57.20%Baseline test accuracy

15.9MModel parameters

Architecture: frozen ImageNet VGG16 feature extractor with a dense two-class head.

Input path: image → 32×32 RGB → 96×96 → VGG16 BGR mean subtraction → softmax.

Dataset: balanced CIFAR-10 cat and dog subsets: 8,000 training, 2,000 validation, and 2,000 test images.

Responsible use

This project is for educational and portfolio demonstration purposes only. Fine-grained image models may confuse visually similar categories, especially when images are unclear, cropped, low-quality, or outside the training distribution. Do not use this model as the sole basis for medical, legal, safety-critical, security, hiring, insurance, financial, or production decisions.

Processing occurs locally in the browser, but do not upload private, sensitive, confidential, copyrighted, or personally identifiable images into a public demo. Predictions are machine-learning outputs, not guaranteed truth.