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The goal of this project is to perform a full 3D reconstruction pipeline on a real-world object or scene using multiple views, starting with camera calibration, then keypoint matching, and finally …
This project explores the use of CLIP (Contrastive Language–Image Pretraining) for bidirectional retrieval between images and text, with a focus on explainability. Users can query with either an im…
This project serves as a prime example of computer vision's role in revolutionizing healthcare. By utilizing the Detectron2 framework this project enables accurate detection of tumors in brain MRI…
A collection of generative AI model implementations, including Variational Autoencoders (VAE), Conditional VAEs, Generative Adversarial Networks (GANs), CycleGAN, Diffusion Models, and Autoencoder-…
Evaluation of black-box (LIME, RISE) and white-box (Grad-CAM, FEM) explainability techniques. The project evaluates each method using Deletion and Insertion curves, along with Pearson Correlation (…
This project explores model compression methods, specifically Pruning and Quantization, applied to deep neural networks using PyTorch. These techniques reduce model size, computation cost, and infe…
Python
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