Wildlife Species Identification with Fine-Tuned ResNet Models

Conservation researchers collect enormous volumes of wildlife imagery from camera traps and field surveys, but manually identifying the species in every frame is a bottleneck that delays population studies and habitat decisions. Automated species classification lets small research teams process seasons of imagery in hours instead of months. The intern builds a Streamlit application centered on transfer learning: a ResNet model pre-trained on general imagery is fine-tuned in PyTorch to classify wildlife photos into multiple species. The project includes a data augmentation pipeline using OpenCV and NumPy to simulate the messy conditions of field photography, controls for staged fine-tuning of the network layers, and a prediction dashboard that reports ranked species probabilities for each uploaded image. An explanation view highlights the image regions most responsible for each prediction, helping ecologists confirm the model is keying on the animal rather than the background, while pandas tracks evaluation metrics across training runs. The intern comes away understanding how to adapt large pre-trained vision models to a specialized dataset, how augmentation and fine-tuning choices affect generalization, and how to present model evidence in a form that earns the trust of scientific users.