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General Information
| Full Name | Farid Kazimov |
| Title | Master Engineer | AI & Computer Vision Engineer | Neural Networks | Object Detection | Deep Learning |
| kazimov.ferid.99@gmail.com | |
| Languages | Azerbaijani (Native), Turkish (Native), English (Fluent) |
Education
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2023 - 2025 Master of Science (M.Sc.)
Wroclaw University of Science and Technology, Wroclaw, Poland - Applied Computer Science.
- Specializing in Computer Vision and Deep Learning.
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2017 - 2021 Bachelor of Science (B.Sc.)
Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey - Computer Engineering.
Experience
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June 2021 - July 2021 Software Engineer Intern
Mavi Yesil Yazilim ve Otomasyon - Assisted in industrial automation solutions and system integration.
- Performed software validation and contributed to process control systems.
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June 2019 - July 2019 Network Engineer Intern
KSU IT Department - Participated in network setup, configuration, and troubleshooting.
- Prepared technical documentation for IT infrastructure maintenance.
Projects
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2025 Automatic Animal and Crowd Counting
- Developed a computer vision system using YOLOv8 with heatmap and density maps for counting animals and people in dense environments. Validated on datasets including sheep, bees, cows, monkeys, butterflies, and humans.
- YOLOv8 • OpenCV • Heatmap and Density Maps • Computer Vision
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2025 Transfer Learning for Crowded Animals Detection
- Applied transfer learning to adapt YOLOv8 models trained on one species to others (e.g., cows, monkeys, butterflies). Cross-class fine-tuning improved generalization and reduced training data requirements for scalable deployment.
- Transfer Learning • Fine-Tuning • Cross-Class Generalization • Domain Adaptation
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2025 YOLO Version Comparison (YOLOv3 -> YOLO12)
- Benchmarking YOLO models for detecting animals in crowded herds, focusing on accuracy, inference speed, occlusion robustness, and cross-species generalization.
- YOLOv3–YOLOv8+ • Backbone Comparison • Anchor-Free Detection • Non-Maximum Suppression (NMS) • Inference Speed and Latency • Model Optimization (Quantization, Pruning)
Skills
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Programming and Tools
- Python • C++ • TensorFlow • PyTorch • Keras • OpenCV • YOLO • TensorBoard • Git • Google Colab • Roboflow
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Computer Vision
- Convolutional Neural Networks (CNNs) • Heatmap and Density Map Generation • Image Classification and Segmentation • Object Detection and Counting • Transfer Learning and Fine-Tuning • Cross-Class Generalization
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Machine Learning and Deep Learning
- Neural Networks (ANN, RNN, LSTM, Transformers) • Anomaly Detection • Feature Engineering (SMOTE, PCA) • Regularization • Model Evaluation (mAP, F1-score, SHAP) • End-to-End Deep Learning Pipelines
Other Interests
- Reading technical papers, Hiking, Learning new programming frameworks.