🔍 Neural Lens: High-Performance Image Recognition Engine

A professional-grade Computer Vision dashboard built with Python and Streamlit. This tool utilizes Deep Residual Learning via the ResNet50 architecture to transform raw visual data into structured mathematical insights with high probabilistic precision.

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🚀 Live Demo

Click here to try the Live App

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📺 Demo Preview

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✨ Features

• Deep Residual Learning: Leverages a 50-layer ResNet architecture to overcome the vanishing gradient problem, ensuring high-fidelity feature extraction.
• Probabilistic Classification: Identifies over 1,000 object categories from the ImageNet dataset with a Top-5 confidence ranking system.
• Real-Time Inference: Optimized image preprocessing pipeline (224x224 RGB normalization) for near-instant classification.
• Interactive Analytics: Dynamic bar charts powered by Plotly to visualize the model's confidence distribution across different classes.
• Technical UI: Includes a comprehensive sidebar with model metadata, supported formats, and architecture specifications.

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🛠️ Tech Stack

• Language: Python 3.12
• Framework: Streamlit (Web UI)
• Deep Learning Engine: TensorFlow / Keras
• Architecture: ResNet50 (Pre-trained on ImageNet)
• Data Visualization: Plotly Express
• Image Processing: Pillow & NumPy

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🚀 Installation & Local Setup

1. Clone the repository:

   git clone [https://github.com/ali-faraz-py/NeuralLens](https://github.com/ali-faraz-py/NeuralLens)
   cd NeuralLens
   

2. Install dependencies:

    pip install -r requirements.txt
   

3. Run the application:

    streamlit run app.py
   

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📂 Project Structure

neurallens/
├── app.py              # Streamlit Web UI and visualization logic
├── predict.py          # ResNet50 model loading and inference engine
├── requirements.txt    # Project dependencies (TensorFlow, Streamlit, etc.)
├── .gitattributes      # LFS tracking and GitHub language statistics
├── .gitignore          # Prevents tracking of cache and hidden files
└── explore.ipynb       # Research and benchmarking of various CV models

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🧠 Model Insights

The engine utilizes a ResNet50 (Residual Network), a landmark architecture in Computer Vision.

• The Architecture: Unlike traditional sequential models, ResNet uses shortcut connections (identity mapping) to allow gradients to flow through deeper layers.

• Input Transformation: Images are mathematically resized to 224x224x3 and normalized using the specific mean/std-dev requirements of the ImageNet-trained weights.

• The Output: The model generates a Softmax probability distribution across 1,000 classes, which is then decoded into human-readable labels with associated confidence scores.

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👤 Author

Syed Ali Faraz - GitHub Profile

If you found this NLP pipeline useful, please give the repository a ⭐!