luke12121212/cifar10-vit-classifier

ViT CIFAR-10 Classifier

Model Description

This model is a Vision Transformer (ViT) fine-tuned for image classification on the CIFAR-10 dataset.

It recognizes 10 image categories:

• airplane
• automobile
• bird
• cat
• deer
• dog
• frog
• horse
• ship
• truck

Base Model

google/vit-base-patch16-224-in21k

Dataset

CIFAR-10 (uoft-cs/cifar10).

The original dataset contains 50,000 training images and 10,000 test images.

For this experiment, we used:

• Training samples: 2,000
• Validation samples: 500

Training Configuration

Parameter	Value
Epochs	1
Batch size	8
Learning rate	2e-5
Weight decay	0.01
Image size	224 × 224
Hardware	NVIDIA T4 GPU

Evaluation Results

Evaluation on 500 validation images:

Metric	Value
Accuracy	90.0%
Validation loss	1.5738

Per-class F1 Scores

Class	F1-score
airplane	0.986
automobile	0.933
bird	0.750
cat	0.809
deer	0.931
dog	0.828
frog	0.903
horse	0.941
ship	0.990
truck	0.941

Limitations

• The model was fine-tuned on only 2,000 training images.
• Evaluation was performed on a 500-image validation subset.
• Performance varies between classes, especially for birds.
• The reported results are not a full test-set evaluation.

Experiment Tracking

Training parameters, metrics, confusion matrix, and per-class metrics were recorded using MLflow.

Intended Use

Educational image classification experiments and demonstrations using CIFAR-10.