README.md

---
title: Utensil Detector
emoji: 🍽️
colorFrom: pink
colorTo: purple
sdk: streamlit
sdk_version: "1.32.0"
app_file: app/app.py
pinned: false
---

# 🍽️ Utensils Object Detection System

Welcome to **Utensils Object Detection System** — an end-to-end pipeline that detects Utensils items like plates, glasses, spoons, and forkss using a custom-trained deep learning model.

This project was built **from scratch** (no Roboflow or auto-annotation tools!) and demonstrates a full lifecycle: dataset creation, model training, performance evaluation, and an interactive demo app.

---

## 🏗️ Project Overview

We set out to solve a real-world problem:
> _“Can we reliably detect common Utensils items in images, videos, or real-time webcam streams using only a small, custom-labeled dataset?”_

To achieve this, we:
✅ Collected & annotated a custom dataset (100–500 images)
✅ Built a clean Python codebase to handle training, inference, and deployment
✅ Delivered an interactive demo using **Streamlit / Flask**

---

## 📁 Project Structure

```
├── app/                # Streamlit or Flask app for demo
│   └── app.py
├── dataset/            # Custom dataset (images + labels)
│   ├── images/
│   └── labels/
├── inference/          # Inference scripts (image, video, webcam)
│   ├── detect_image.py
│   ├── detect_video.py
│   └── detect_webcam.py
├── runs/detect/        # Training results & saved weights
│   ├── weights/
│   ├── results.png
│   └── Other Metrics ...
├── training/           # Training pipeline
│   ├── train.py
│   └── model_training.ipynb
├── data.yaml           # Dataset config
├── requirements.txt    # Python dependencies
└── README.md           # This file
```

---

## 🗂️ Dataset

- **Images collected:** Manually photographed or sourced from public domain (Kaggle)
- **Classes:** Example — plate, fork, spoon, glass
- **Annotation tool:** [LabelImg](https://github.com/heartexlabs/labelImg)
- **Format:** YOLO txt labels

---

## 🏋️‍♂️ Model Training

- **Framework:** YOLOv8
- **Training script:** `training/train.py`
- **Best checkpoint:** `runs/detect/weights/best.pt`
- **Metrics logged:** loss curves, mAP, precision, recall, F1

---

## 🔍 Inference & Results

- Run detection on:
  - Static images → `inference/detect_image.py`
  - Video files → `inference/detect_video.py`
  - Real-time webcam → `inference/detect_webcam.py`

- Visual outputs include:
  - Bounding boxes with class names and confidence
  - Confusion matrix
  - Precision-recall, F1 curves

---

## 🌐 Interactive Demo

Launch the demo app:
```bash
pip install -r requirements.txt
streamlit run app/app.py 
```

Features:
- Upload image or video and get detections
- View predicted bounding boxes + class names + confidence scores
- (Optional) Real-time webcam support

---

## 🚀 Getting Started

1️⃣ Clone the repo:
```bash
git clone https://github.com/yourusername/Utensils-object-detection.git
cd Utensils-object-detection
```

2️⃣ Install dependencies:
```bash
pip install -r requirements.txt
```

3️⃣ Run training:
```bash
python training/train.py --data data.yaml
```

4️⃣ Try inference:
```bash
python inference/detect_image.py --source path/to/image.jpg
```

5️⃣ Launch app:
```bash
streamlit run app/app.py
```
Model summary (fused): 92 layers, 25,842,076 parameters, 0 gradients, 78.7 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:02<00:00,  1.48it/s]
                   all         40         40      0.681      0.725      0.731      0.468
                  fork         10         10      0.338        0.2      0.265      0.113
                 glass         10         10      0.643        0.9      0.888      0.432
                 plate         10         10          1          1      0.995      0.833
                 spoon         10         10      0.744        0.8      0.776      0.496
---

## 📊 Performance

| Metric         | Value    |
|---------------|----------|
| mAP@0.5       | 78.0%    |
| mAP@0.5:0.95  | 50.8%    |
| Precision     | 85.5%    |
| Recall        | 67.5%    |

> _These numbers are based on our custom dataset; actual results may vary depending on data size and quality._

---

## 💡 Challenges & Learnings

- **Challenge:** Small dataset size → risk of overfitting
- **Solution:** Data augmentation and careful validation splitting
- **Challenge:** Labeling errors → noisy annotations
- **Solution:** Manual re-checking of all labels
- **Challenge:** Real-time inference speed
- **Solution:** Optimized image preprocessing pipeline


---

## 🛡️ License & Acknowledgments

- Built using open-source tools: [Ultralytics YOLO](https://github.com/ultralytics/yolov5), [Streamlit](https://streamlit.io/)
- Dataset annotated manually, no pre-annotated sources used
- No external pre-trained models on non-custom data

---

If you like this project, ⭐ the repo and feel free to contribute!
Happy detecting! 🍳🍴🥄