Deep learning fish classifier combining ConvNeXt-Tiny (40 species, 98.96% accuracy) with BioCLIP-2 zero-shot recognition and AI-powered habitat mapping
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Updated
Feb 8, 2026 - Python
Deep learning fish classifier combining ConvNeXt-Tiny (40 species, 98.96% accuracy) with BioCLIP-2 zero-shot recognition and AI-powered habitat mapping
Explainable AI-based Diabetic Retinopathy Detection using ConvNeXt-Tiny and Grad-CAM++
InSwapper Detector is a production-focused deepfake detection system designed to identify faces manipulated with INSwapper. It combines face detection, RGB image analysis, frequency artifact extraction, and a multi-task ConvNeXt-Tiny model for image, batch, and video-based detection.
1st place solution for the CentraleSupélec Deep Learning Kaggle challenge "3-MD-4040 2026 ZooCAM Challenge". Plankton image classification (1.2M samples, 86 classes) using CNNs trained from scratch and a weighted logits ensemble of ResNet50, EfficientNet-B3 and ConvNeXt-Tiny with label smoothing, weighted sampling and TTA.
Classifying Brain tumor images using Late fusion of two pre trained cnn model ConvNextTiny , you can test MRI image to classify
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