IEEE GRSL: Integrating spatial details with long-range contexts for semantic segmentation of very high resolution remote sensing images
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Updated
Jun 6, 2024 - Python
IEEE GRSL: Integrating spatial details with long-range contexts for semantic segmentation of very high resolution remote sensing images
Official implementation of "MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation" in MMSegmentation Framework.
Complete code for the proposed CNN-Transformer model for natural language understanding.
A state-of-the-art hybrid deep learning ensemble that combines the strengths of Convolutional Neural Networks (CNNs) and Transformers for intelligent plant disease detection and real world agricultural applications.
Official implementation of "MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation" in MMSegmentation Framework.
A hybrid CNN–Transformer framework for precise industrial surface defect detection and segmentation, integrating Vision Transformer (ViT) with convolutional modules to effectively capture both local texture details and global contextual features.
A deep learning approach for classifying crop types based on agricultural data.
Custom Video Processing Model
End-to-end yoga pose classification web app using DenseNet121 and Flask, featuring a modular architecture, trained deep learning model, and deployment-ready UI for real-world inference
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