Vision AI in 5 Days – CIFAR-10

This repo is a complete starter to build, evaluate, and demo an image-classification system in Python + TensorFlow/Keras. It follows the bootcamp flow: preprocess → train basic CNN → evaluate → transfer learning → fine-tune → demo → publish.

Quickstart (Google Colab recommended)

1. Upload this zip to Colab and unzip:

!unzip vision_ai_cifar10_project.zip -d /content/
%cd /content/vision_ai_cifar10_project

2. (Optional) Preprocess & cache data:

!python src/preprocess.py

3. Train a basic CNN (saves model & plots):

!python src/train_cnn.py

4. Evaluate saved model (confusion matrix + report):

!python src/evaluate.py --model models/basic_cnn.h5 --prefix basic_cnn

5. Transfer learning (MobileNetV2):

!python src/train_transfer.py
!python src/evaluate.py --model models/mobilenetv2_cifar10.h5 --prefix mobilenetv2

6. Single-image inference:

# Replace with your image path
!python src/infer.py --model models/mobilenetv2_cifar10.h5 --image sample.jpg

Repo Layout

vision_ai_cifar10_project/
  ├─ data/               # cached dataset (created automatically)
  ├─ docs/               # slides, LinkedIn post, demo script templates
  ├─ models/             # trained .h5 models
  ├─ outputs/            # plots, confusion matrices, reports
  └─ src/                # all Python code

Notes

• Default dataset: CIFAR-10 (built into Keras). You can swap to Cats vs Dogs via tensorflow_datasets with small changes.
• Training uses GPU if available. In Colab: Runtime → Change runtime type → GPU.
• For fast submission, you can reduce epochs in the training scripts.

Good luck! 🚀