ResNet-34 Model trained from scratch to classify 450 different species of birds with 98.6% accuracy.
Signature Work @ DKU: Large Scale Bird Sound Recognition in China Region
This is my solution for the RecVis Challenge where i ended 1st on fine-grained classification on bird dataset
Deep learning bird species classifier achieving 96.51% accuracy using PyTorch and transfer learning. Implements 5 approaches from traditional computer vision (HOG, SIFT) to CNNs (ResNet-50, VGG-16). Trained on 25 Indian bird species with 22,498 images. Comprehensive comparison of feature extraction vs deep learning.
Bird Watching App
Identify bird species from images along with audio and explore them with AI-powered insights.
Bird Classification application using yolov8 model trained on the CalTechBirds dataset. Segmentation was also implemented using UNet.
This workspace contains a trained MobileNetV2 model for bird-species classification, the notebooks used to train and test it, and the dataset layout used for training/validation/testing.
Classifies 10 bird species from audio using Transfer Learning with Google's YAMNet. Deployed on Hugging Face Spaces with a Gradio UI.
ML app that recognizes bird by its photo
Fuses the BirdNET v2.4 feature extractor with a BSG regional classifier into a single end-to-end ONNX model
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