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---
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language: en
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license: mit
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tags:
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- keras
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- tensorflow
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- computer-vision
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- image-processing
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- corn-kernel-classification
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pipeline_tag: image-classification
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library_name: keras
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---
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# CornViT
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A Multi-Stage Convolutional Vision Transformer Framework for Corn Kernel Analysis
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## Overview
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Three-stage hierarchical classification pipeline for automated corn kernel quality assessment:
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- **Stage 1**: Purity detection (Pure vs Impure)
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- **Stage 2**: Shape classification (Flat vs Round)
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- **Stage 3**: Embryo orientation (Up vs Down)
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## Architecture
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- **Model**: CvT-13 (384×384) with ImageNet-22k pretraining
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- **Framework**: PyTorch + Microsoft CvT
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- **Test Accuracy**: 93.8% (Stage 1), 94.1% (Stage 2), 91.1% (Stage 3)
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## Setup
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```bash
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# Clone repository
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git clone https://github.com/microsoft/CvT.git
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# Install dependencies
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pip install -r requirements.txt
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```
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## Training
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Each stage has independent training scripts:
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```bash
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python stage1/train_cvt13.py # Purity classification
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python stage2/train_cvt13.py # Shape classification
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python stage3/train_cvt13.py # Embryo orientation
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```
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## Inference
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```bash
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python stage1/inference_cvt13.py
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python stage2/inference_cvt13.py
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python stage3/inference_cvt13.py
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```
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## Baselines
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ResNet50 and DenseNet121 baselines available in `baselines/`.
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## Structure
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```
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├── stage1/ # Purity classification
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├── stage2/ # Shape classification
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├── stage3/ # Embryo orientation
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└── preprocess/ # Data preprocessing scripts
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```
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## Requirements
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- Python 3.13+
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- PyTorch 2.9+
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- CUDA (optional, for GPU training)
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---
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## Citation
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If you use this code, models, or catalog in your research, please cite:
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```bibtex
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@Article{computers15010002,
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AUTHOR = {Erukude, Sai Teja and Mascarenhas, Jane and Shamir, Lior},
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TITLE = {CornViT: A Multi-Stage Convolutional Vision Transformer Framework for Hierarchical Corn Kernel Analysis},
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JOURNAL = {Computers},
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VOLUME = {15},
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YEAR = {2026},
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NUMBER = {1},
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ARTICLE-NUMBER = {2},
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URL = {https://www.mdpi.com/2073-431X/15/1/2},
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ISSN = {2073-431X},
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DOI = {10.3390/computers15010002}
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}
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``` |
