README.md

metadata

license: mit
title: SmartVision AI
sdk: streamlit
emoji: 🚀
colorFrom: red
colorTo: red
short_description: Multi-domain smart object detection and classification syste

SmartVision AI – Complete Vision Pipeline (YOLOv8 + CNN Classifiers + Streamlit Dashboard)

SmartVision AI is a fully integrated Computer Vision system that combines:

• Object Detection using YOLOv8
• Image Classification using 4 deep-learning models:
  VGG16, ResNet50, MobileNetV2, EfficientNetB0
• A complete Streamlit-based Dashboard for inference, comparison, metrics visualization, and webcam snapshots
• A modified dataset built on a 25‑class COCO subset

This README explains setup, architecture, training, deployment, and usage.

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🚀 Features

✅ 1. Image Classification (4 Models)

Each model is fine‑tuned on your custom 25‑class dataset:

• VGG16
• ResNet50
• MobileNetV2
• EfficientNetB0

Outputs:

• Top‑1 class prediction
• Top‑5 predictions
• Class probabilities

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🎯 2. Object Detection – YOLOv8s

YOLO detects multiple objects in images or webcam snapshots.

Features:

• Bounding boxes
• Confidence scores
• Optional classification verification using ResNet50
• Annotated images saved automatically

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🔗 3. Integrated Classification + Detection Pipeline

For each YOLO‑detected box:

1. Crop region
2. Classify using chosen CNN model
3. Display YOLO label + classifier label
4. Draw combined annotated results

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📊 4. Metrics Dashboard

Displays:

• Accuracy
• Weighted F1 score
• Top‑5 accuracy
• Images per second
• Model size
• YOLOv8 mAP scores
• Confusion matrices
• Comparison bar charts

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📷 5. Webcam Snapshot Detection

Take a photo via webcam → YOLO detection → annotated results.

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📁 Project Structure

SmartVision_AI/
│
├── app.py                     # Main Streamlit App
├── saved_models/              # Trained weights (VGG16, ResNet, MobileNetV2, EfficientNet)
├── yolo_runs/                 # YOLOv8 training folder
├── smartvision_dataset/       # 25-class dataset
│   ├── classification/
│   │   ├── train/
│   │   ├── val/
│   │   └── test/
│   └── detection/             # Labels + images for YOLOv8
│
├── smartvision_metrics/       # Accuracy, F1, confusion matrices
├── scripts/                   # Weight converters, training scripts
├── inference_outputs/         # Annotated results
├── requirements.txt           
└── README.md                  

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⚙️ Installation

1️⃣ Clone Repository

git clone https://github.com/<your-username>/SmartVision_AI.git
cd SmartVision_AI

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Install YOLOv8 (Ultralytics)

pip install ultralytics

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▶️ Run Streamlit App

streamlit run app.py

App will open at:

http://localhost:8501

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🏋️ Training Workflow

1️⃣ Classification Models

Each model has:

• Stage 1 → Train head with frozen backbone
• Stage 2 → Unfreeze top layers + fine‑tune

Scripts:

scripts/train_mobilenetv2.py
scripts/train_efficientnetb0.py
scripts/train_resnet50.py
scripts/train_vgg16.py

2️⃣ YOLO Training

yolo task=detect mode=train model=yolov8s.pt data=data.yaml epochs=50 imgsz=640

Outputs saved to:

yolo_runs/smartvision_yolov8s/

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🧪 Supported Classes (25 COCO Classes)

airplane, bed, bench, bicycle, bird, bottle, bowl,
bus, cake, car, cat, chair, couch, cow, cup, dog,
elephant, horse, motorcycle, person, pizza, potted plant,
stop sign, traffic light, truck

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🧰 Deployment on Hugging Face Spaces

You can deploy using Streamlit SDK.

Steps:

1. Create public repository on GitHub
2. Push project files
3. Create new Hugging Face Space → select Streamlit
4. Connect GitHub repo
5. Add requirements.txt
6. Enable GPU for YOLO (optional)
7. Deploy 🚀

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🧾 requirements.txt Example

streamlit
tensorflow==2.13.0
ultralytics
numpy
pandas
Pillow
matplotlib
scikit-learn
opencv-python-headless

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📄 .gitignore Example

saved_models/
*.h5
*.pt
*.weights.h5
yolo_runs/
smartvision_metrics/
inference_outputs/
__pycache__/
*.pyc
.DS_Store
env/

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🙋 Developer

SmartVision AI Project
Yogesh Kumar V M.Sc. Seed Science & Technology (TNAU)
Passion: AI, Computer Vision, Agribusiness Technology

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🏁 Conclusion

SmartVision AI integrates:

• Multi‑model classification
• YOLO detection
• Streamlit visualization
• Full evaluation suite

Perfect for:

• Research
• Demonstrations
• CV/AI portfolio
• Real‑world image understanding

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Enjoy using SmartVision AI! 🚀🧠