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Achievements

Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLO

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Abdulsametklc/README.md

💫 About Me:

A passionate AI developer

🌐 Socials:

LinkedIn X email

💻 Tech Stack:

C# C++ Python HTML5 Anaconda nVIDIA Django Next JS OpenCV Qt TailwindCSS MicrosoftSQLServer Postgres Matplotlib NumPy Pandas TensorFlow PyTorch GitHub Git GitLab Arduino FFmpeg Raspberry Pi Trello Ubiquiti nVIDIA

📊 GitHub Stats:



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  1. uav-tracker-evaluation uav-tracker-evaluation Public

    A benchmarking toolkit for evaluating multiple object tracking algorithms under a unified pipeline. Compares accuracy, stability, speed, and resource usage to help select the optimal tracker for re…

    Python

  2. Ensemble-and-Hybrid-CNN-Architectures-for-Brain-Tumor-Classification Ensemble-and-Hybrid-CNN-Architectures-for-Brain-Tumor-Classification Public

    Multi-model hybrid deep learning framework for accurate brain tumor classification using MRI images.

    Jupyter Notebook

  3. C80-Acoustic-Quality-Prediction-Mel-Spectrogram-Based-Deep-Learning-Approach C80-Acoustic-Quality-Prediction-Mel-Spectrogram-Based-Deep-Learning-Approach Public

    This project aims to estimate the C80 acoustic quality values ​​of seats in a concert hall. C80 values ​​are modeled using deep learning using Mel spectrogram features. Missing measurement data is …

    Jupyter Notebook

  4. CIFAR10-Inpainting-Autoencoder CIFAR10-Inpainting-Autoencoder Public

    This project aims to solve the image inpainting problem using a ResNet-based autoencoder architecture on the CIFAR-10 dataset. The model is trained to reconstruct missing or masked regions in 32x32…

    Jupyter Notebook

  5. Parallel-Image-Processor Parallel-Image-Processor Public

    Real-time parallel image processing with CPU core visualization. Demonstrates Thread Pool, Mutex, Semaphore and SIMD optimization using C++ and WPF.

    C++

  6. Semantic-Segmentation-UNet-PyTorch Semantic-Segmentation-UNet-PyTorch Public

    Semantic image segmentation using U-Net architecture on Oxford-IIIT Pets dataset. Achieves ~90% Dice score with BCE+Dice loss.

    Python

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