Vision Transformer Tutorial: Multi-Class Classification on Custom Dataset with GPU

In this tutorial, we walk through the complete pipeline for building an image classification model using the Vision Transformer (ViT) architecture on a custom multi-class dataset — fully accelerated by GPU. Whether you're a beginner or a deep learning enthusiast, this hands-on guide covers everything from setup to inference.

🛠️ Virtual environment setup and Required Python packages:

conda create -n torch-env python=3.12

conda activate torch-env

pip install jupyter

pip instlal matplotlib

pip install scikit-learn

pip install seaborn

pip install seaborn

📁 Resources and links:

💻 GitHub Code Repository:

📌 What You’ll Learn
1️⃣ Set up environment and install essential packages
2️⃣ Check GPU availability for training
3️⃣ Load and preprocess your custom dataset
4️⃣ Train the ViT model and validate on the validation set
5️⃣ Evaluate performance (Accuracy, Precision, Recall)
6️⃣ Visualize training curves (loss & accuracy)
7️⃣ Generate and interpret the confusion matrix
8️⃣ Run inference on sample test images

🧠 Presented by:
Dr. Noman
Edith Cowan University, Australia
🔗 LinkedIn Profile

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Tags:
Deep Learning, ResNet, ResNet-50, TensorFlow, Keras, Machine Learning, Image Classification, CNN, Computer Vision, AI Projects, ML Projects, Transfer Learning, Artificial Intelligence, Python, Neural Networks, Data Science, Model Training, Custom Dataset, Model Evaluation, Flower Classification, Deep Learning Tutorial, Smart Tech, OpenCV, AI Research
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[Ninit-uv]

This was an excellent and informative video tutorial. Thank you so much, sir.

[sameeppoudel]

what's the difference between test and testing?