Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection

We provide the pytorch implementation of a semantically constrained GAN to generate artificial realisitc fruit images for training to reduce the need of real image labeling in fruit detection.

Paper Link

1. Install Requirements

pip install -r requirements.txt

2. Generate Semantic Consistent GAN Fruits

Use generateGANImages.ipynb notebook to load the pre-trained Semantic Consistent GAN model and generate target domain images from source synthetic image.

3. Prepare Data

Source domain data

The dataset used in this research:

1. Synthetic Grape Data

Target domain data

The dataset used in this research:

1. Night Grape
2. Day Grape