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--- |
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license: apache-2.0 |
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--- |
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# π Auto-Strawberry: Strawberry Daughter Plant Dataset and Pretrained Models |
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Welcome to the **Auto-Strawberry** project! This repository hosts the **strawberry daughter plant sample dataset** and **pretrained deep learning models** developed for automated estimation of plant growth attributes using stereo vision and deep learning techniques. |
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## π¦ Dataset Overview |
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The **Strawberry Daughter Plant Dataset** contains: |
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- **Multi-view RGB images:** 6 views per sample captured using a stereo vision camera setup (`cam0` and `cam1`). |
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- **Ground truth labels:** |
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- Total Leaf Area |
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- Fresh Mass |
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- Largest Petiole Length |
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- Average Crown Diameter |
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- **Format:** Images in `.jpg` and labels stored in a JSON file. |
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### Dataset Structure: |
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```plaintext |
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- cam0/ |
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- cam1/ |
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- annotations.json |
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``` |
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## π§ Pretrained Models |
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### Available Backbone Models: |
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- **ResNet34** |
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- **Vision Transformer (ViT-B-16)** |
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- **EfficientNet B0** |
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### Model Weights: |
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Each model was trained using the **strawberry dataset** for the regression task of estimating leaf area and other growth metrics. The weights provided here can be used for inference and fine-tuning. |
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## π₯ Download and Usage |
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### Hugging Face Integration: |
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```bash |
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pip install huggingface_hub |
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``` |
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```python |
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from huggingface_hub import hf_hub_download |
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# Download the model from Hugging Face Hub |
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hf_hub_download(repo_id="sinabjam/auto-strawberry", filename="desired_file_name.pth") |
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``` |
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## π― Applications |
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- Plant phenotyping |
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- Automated growth monitoring |
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- Precision agriculture insights |
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## π License |
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This project is licensed under the **Apache 2.0 License**. |
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## π¬ Contact |
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- **Author:** Sina Baghbanijam |
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- **Email:** [sbaghba@ncsu.edu](mailto:sbaghba@ncsu.edu) |
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- **Project Repository:** [GitHub Link](https://github.com/sbaghba/Auto-Strawberry) |
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**π If you use this dataset or models in your research, please cite this repository!** |