FineTuning RetinaNet for Wildlife Detection with PyTorch: A Step-by-Step Tutorial
A comprehensive step-by-step guide on fine-tuning RetinaNet using PyTorch to achieve 79% accuracy on wildlife detection tasks.
In this tutorial, we dive deep into RetinaNet’s architecture, explain the benefits of Focal Loss, handle class imbalance, and demonstrate practical tips for efficient fine-tuning—even with limited GPU resources. Plus, we benchmark our RetinaNet model against YOLO11 to showcase key improvements in precision!
Perfect for anyone interested in applying cutting-edge deep learning to real-world wildlife conservation problems.
What is Face Detection? Ultimate Guide 2025 + Model Comparison
Let’s understand what face detection is, how it works, what its challenges are, and in what areas face detection is used. You will also see the journey of face detection methods from classical techniques to State of the art deep learning methods available today and compare the performance of popular methods.
Ensemble Deep Learning-based Defect Classification and Detection in SEM Images
Today’s post will teach how computer vision impacts the semiconductor industry with a specific example of defect detection and classification.
