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🔍 Object Detection using TensorFlow

A deep learning project that performs object detection on handwritten digits — simultaneously classifying what the digit is and localizing where it appears in an image using bounding box regression.

Built with TensorFlow and Keras as a custom multi-output CNN from scratch.

📌 What It Does

Given an image with a digit placed at a random location, the model:

  • Classifies the digit (0–9)
  • Localizes it by predicting a bounding box [xmin, ymin, xmax, ymax]

This is the core idea behind object detection — not just what is in the image, but where.

🧠 Model Architecture

Input (75x75x1)
     │
     ▼
Conv2D(16) → AveragePooling
Conv2D(32) → AveragePooling
Conv2D(64) → AveragePooling
     │
     ▼
Flatten → Dense(128)
     │
     ├──► Dense(10, softmax)   → Classification Output (digit 0–9)
     └──► Dense(4)             → Bounding Box Output [xmin, ymin, xmax, ymax]

The model uses two output heads trained simultaneously:

Head Loss Function Metric
Classification Categorical Crossentropy Accuracy
Bounding Box Mean Squared Error (MSE) MSE

📂 Dataset

  • Source: MNIST via tensorflow_datasets
  • Each 28×28 digit is randomly placed inside a 75×75 canvas
  • Bounding box coordinates are normalized between 0 and 1
  • Train set: 60,000 images | Validation set: 10,000 images

🛠️ Tech Stack

Tool Purpose
TensorFlow / Keras Model building & training
TensorFlow Datasets MNIST data loading
NumPy Array operations
Matplotlib Visualization
Pillow (PIL) Bounding box drawing

📊 Evaluation

Model is evaluated using:

  • Classification Accuracy — how often the digit label is correct
  • Bounding Box MSE — how close predicted box coordinates are to ground truth
  • IoU (Intersection over Union) — overlap between predicted and true bounding boxes (threshold: 0.6)

🚀 Getting Started

1. Clone the repo

git clone https://github.com/your-username/object-detection-tensorflow.git
cd object-detection-tensorflow

2. Install dependencies

pip install tensorflow tensorflow-datasets numpy matplotlib pillow

3. Run the notebook

Open objectdetection.ipynb in Jupyter or Google Colab and run all cells.

📈 Training

  • Optimizer: Adam
  • Epochs: 20
  • Batch Size: 64
  • Strategy: tf.distribute (supports multi-GPU)

📁 Project Structure

object-detection-tensorflow/
│
├── objectdetection.ipynb   # Main notebook
├── my_mnist_model.keras    # Saved trained model (after training)
└── README.md

💡 Key Concepts Demonstrated

  • Multi-output neural networks
  • Bounding box regression
  • IoU-based evaluation
  • Custom data preprocessing pipelines with tf.data
  • Transfer of object detection concepts to digit localization

👤 Author

HB Mrudhal Ankith
B.Tech AI & ML | Amity University, Bangalore
GitHub • LinkedIn