CDMENet-main

This is the code for the paper "Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion" .

Prepare

1.1 Datasets can Found in:

github：Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD

arxiv：Grape detection, segmentation and tracking using deep neural networks and three-dimensional association

youtube：Grape detection, segmentation and tracking

1.2 Setting Runing Environment：

Ubuntu 20.04

Intel Core i9-10900X [email protected]

python 3.8

Pytorch 1.7.1

GeForce RTX 3090

Data Processing:

follow the file "make_dataset.py" to produce the ground-truth density map, file.mat to file.h5

Training the model:

python train.py train.json val.json 0 0 to train your model

Testing the model:

python val.py

Notice the path of all files in these codes, you should modify them to suit your condition.

Optimal model in this paper:

[BaiduDisk](https://pan.baidu.com/s/1OfzuWPKi2SuhqL0cXFvuSw?pwd=g7en password/code：g7en)