Projects using PyTorch in ThaneProjects using PyTorch in Thane
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Cover image for Everyone's talking about quantum computing.
Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.
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Cover image for A Neural Network Visualization
A Neural Network Visualization Tool that demystifies AI! I built an interactive web application using Flask and PyTorch that doesn't just recognize handwritten digits it shows you exactly how the AI "thinks." When you draw a digit (0-9) on the canvas, the app processes it through a Convolutional Neural Network and generates a real-time visualization of every layer: from edge detection in the first convolutional layer, through pattern recognition, pooling, and feature extraction, all the way to the final classification. The tech stack includes Python, Flask, PyTorch, and vanilla JavaScript for the frontend. What makes this unique is the educational aspect each layer's activations are visualized using matplotlib, showing the 32 filters in Conv Layer 1, the 64 filters in Conv Layer 2, and the 128-neuron fully connected layer.
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