---
license: apache-2.0
---

# 🍓 Auto-Strawberry: Strawberry Daughter Plant Dataset and Pretrained Models

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.

## 📦 Dataset Overview

The **Strawberry Daughter Plant Dataset** contains:

- **Multi-view RGB images:** 6 views per sample captured using a stereo vision camera setup (`cam0` and `cam1`).
- **Ground truth labels:**
  - Total Leaf Area
  - Fresh Mass
  - Largest Petiole Length
  - Average Crown Diameter
- **Format:** Images in `.jpg` and labels stored in a JSON file.

### Dataset Structure:
```plaintext
- cam0/
- cam1/
- annotations.json
```

## 🧠 Pretrained Models

### Available Backbone Models:
- **ResNet34**
- **Vision Transformer (ViT-B-16)**
- **EfficientNet B0**

### Model Weights:
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.

## 📥 Download and Usage

### Hugging Face Integration:
```bash
pip install huggingface_hub
```

```python
from huggingface_hub import hf_hub_download

# Download the model from Hugging Face Hub
hf_hub_download(repo_id="sinabjam/auto-strawberry", filename="desired_file_name.pth")
```
## 🎯 Applications
- Plant phenotyping
- Automated growth monitoring
- Precision agriculture insights

## 📜 License
This project is licensed under the **Apache 2.0 License**.

## 📬 Contact
- **Author:** Sina Baghbanijam
- **Email:** [sbaghba@ncsu.edu](mailto:sbaghba@ncsu.edu)
- **Project Repository:** [GitHub Link](https://github.com/sbaghba/Auto-Strawberry)

**🌟 If you use this dataset or models in your research, please cite this repository!**