Object_Detection

AI-Powered Home Security — Object Detection

One-stop Jupyter notebook implementation of an object detection model for a home security system. The repository contains a single, self-contained notebook:

• AI_Powered_Home_Security_System_|_Object_Detection_Model.ipynb

This README helps you run, inspect, and extend the notebook locally or in Google Colab.

Table of contents

• Project overview
• Quickstart
• Suggested requirements
• Notebook walkthrough
• Dataset and expected structure
• How to run
  • Run locally (Jupyter)
  • Run on Google Colab
• Reproducing results and tips
• Exporting the notebook to a script
• Contributing
• License & contact
• Notes & caveats

Project overview

This notebook implements an object detection pipeline intended for a home security setting. Typical included stages are:

• Data loading and preprocessing (image loading, annotations)
• Data augmentation and dataloader creation
• Model definition or pre-trained model selection
• Training loop with checkpointing
• Evaluation with common object detection metrics (mAP, precision/recall)
• Inference and visualization of bounding boxes on test/stream frames

Open the notebook to see exact model choices, hyperparameters, and dataset specifics.

Quickstart

1. Clone the repository: git clone https://github.com/Stegen54/Object_Detection.git cd Object_Detection

2. Create an isolated Python environment (recommended): python3 -m venv venv source venv/bin/activate # macOS / Linux venv\Scripts\activate # Windows (PowerShell)

3. Install dependencies (see below for a suggested list). If the notebook uses a specific framework/version, install those instead.

Suggested requirements

The notebook likely depends on typical computer-vision and ML libraries. Create a requirements.txt with the following (adjust versions as needed):

numpy pandas matplotlib opencv-python scikit-learn tqdm jupyterlab seaborn

One of these (pick the one the notebook imports):

torch # PyTorch (for PyTorch-based notebooks) torchvision

or

tensorflow # TensorFlow (for TF-based notebooks)

If using COCO or VOC utilities:

pycocotools

Install: pip install -r requirements.txt

Note: Open the notebook's first cells and inspect the import statements to confirm whether it uses PyTorch, TensorFlow, or another framework and install versions accordingly (e.g., pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 for GPU-enabled PyTorch).

Notebook walkthrough

Open the notebook and look for these sections (typical order):

1. Title / Overview / Objective
2. Library imports and environment checks (important to confirm framework)
3. Configuration / Hyperparameters (learning rate, batch size, epochs)
4. Dataset loading and visualization
5. Model definition / loading a pre-trained model
6. Training loop with checkpoint saving
7. Evaluation (mAP, precision/recall, confusion)
8. Inference and visualization of predictions (images or video frames)
9. Conclusions and next steps

Tip: The first few cells often contain explicit instructions about required datasets and paths.

Dataset and expected structure

The notebook may expect a dataset in a specific layout (common conventions):

• data/
  • images/
    • train/
    • val/
    • test/
  • annotations/
    • train.json (COCO-style) or train.xml (VOC-style)

If annotation format is unknown, check the dataset-handling cells for parsing code. The notebook may also download or prepare a dataset automatically (check for download links or dataset preparation cells).

How to run

Run locally (Jupyter)

1. Ensure dependencies are installed and environment is activated.
2. Start Jupyter: jupyter lab

   or

   jupyter notebook
3. Open AI_Powered_Home_Security_System_|_Object_Detection_Model.ipynb.
4. Read the top cells for configuration variables (dataset path, output path, whether to use GPU).
5. Run cells sequentially, or use "Run All" once you confirm configuration values.

Run on Google Colab

1. Open Colab: https://colab.research.google.com
2. File → Open notebook → GitHub → paste the repository URL or the notebook name.
3. (Optional) Mount Google Drive if you want persistent storage: from google.colab import drive drive.mount('/content/drive')
4. Install dependencies in a Colab cell: !pip install -r requirements.txt

   or explicit installs:

   !pip install torch torchvision opencv-python pycocotools
5. Run notebook cells. If GPU is preferred, go to Runtime → Change runtime type → Hardware accelerator → GPU.

Example: run inference (pattern)

The notebook likely exposes a small inference block. A general pattern to run inference on an image looks like:

Pseudo-code (adapt to notebook's exact API)

import cv2 img = cv2.imread("data/images/test/example.jpg")

model = ... # instantiate or load the trained weights

preds = model.detect(img) # or model(img)

visualize bounding boxes on img and save/show

Check the "Inference" section of the notebook for the exact calls and helper utilities.

Reproducing results and tips

• Use a GPU for training to speed up runs. Ensure CUDA and matching framework versions are installed.
• Set random seeds if deterministic behavior is desired (not all operations are fully deterministic across hardware):
  • numpy.random.seed(...)
  • torch.manual_seed(...) (if PyTorch)
  • tensorflow.random.set_seed(...) (if TF)
• Save checkpoints frequently and keep the best model based on validation metrics.
• When working with small home-security datasets, use transfer learning (pre-trained backbones) and strong augmentation to improve generalization.
• For video/stream inference, process frames and throttle visualization to match real-time requirements.

Exporting the notebook to a script

To convert the notebook into a runnable Python script: jupyter nbconvert --to script "AI_Powered_Home_Security_System_|_Object_Detection_Model.ipynb"

You may need to refactor the generated script to add argument parsing or modularize dataset/model code for easier reuse.

Contributing

• Inspect the notebook and propose improvements:
  • Add a requirements.txt with pinned versions
  • Add example dataset or synthetic data generator
  • Add small helper scripts (train.py, infer.py) extracted from the notebook
  • Add a license and contributing.md

If you want to contribute:

1. Fork the repo
2. Create a branch for your feature (e.g., add-requirements)
3. Open a pull request with a clear description and testing instructions

License & contact

• No license file is present in the repository. Before using or redistributing, add an appropriate open-source license (e.g., MIT, Apache-2.0) to clarify permissions.
• For questions or to propose changes, open an issue or submit a pull request via GitHub.

Notes & caveats

• The README provides safe, practical guidance based on the notebook being the main artifact. The notebook itself should be inspected to learn the exact frameworks, versions, dataset format, and model details. Look at the top import/installation cells and configuration/paths cells.
• If you want, I can:
  • Extract exact requirements by reading the notebook imports and produce a pinned requirements.txt.
  • Create a small wrapper script (train.py / infer.py) based on notebook cells if you paste the notebook's key code cells here.

Enjoy experimenting with the object detection notebook! If you'd like, tell me whether the notebook uses PyTorch or TensorFlow and I will generate an exact, pinned requirements.txt and an example inference script.