https://github.com/supershivam07/vision-ai
https://github.com/supershivam07/vision-ai
cifar10 cnn confusion-matrix dataset evaluation fine-tuning inference jyputer-notebook keras mobilenetv2 model prediction preprocessing python tensorflow tensorflow2 training transfer-learning vision-ai weights
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/supershivam07/vision-ai
- Owner: Supershivam07
- Created: 2025-08-13T18:38:56.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-08-18T17:15:56.000Z (11 months ago)
- Last Synced: 2025-08-18T18:12:01.594Z (11 months ago)
- Topics: cifar10, cnn, confusion-matrix, dataset, evaluation, fine-tuning, inference, jyputer-notebook, keras, mobilenetv2, model, prediction, preprocessing, python, tensorflow, tensorflow2, training, transfer-learning, vision-ai, weights
- Language: Jupyter Notebook
- Homepage:
- Size: 55.2 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: history_basic_cnn.png
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README
# 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:
```bash
!unzip vision_ai_cifar10_project.zip -d /content/
%cd /content/vision_ai_cifar10_project
```
2. **(Optional) Preprocess & cache data:**
```bash
!python src/preprocess.py
```
3. **Train a basic CNN** (saves model & plots):
```bash
!python src/train_cnn.py
```
4. **Evaluate saved model** (confusion matrix + report):
```bash
!python src/evaluate.py --model models/basic_cnn.h5 --prefix basic_cnn
```
5. **Transfer learning (MobileNetV2)**:
```bash
!python src/train_transfer.py
!python src/evaluate.py --model models/mobilenetv2_cifar10.h5 --prefix mobilenetv2
```
6. **Single-image inference**:
```bash
# 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! 🚀