271.35MBZIP

This project is a Unet multi-scale segmentation practical project, including dataset, code, trained weights file. After testing, the code can be used directly

Project description: Total size 271MB

Dataset for this project: BraTS 3d Brain Tumor Image Slicing for 2D Image Segmentation Project

With only 10 epochs of training, the network achieves a global pixel accuracy of 0.97 and a miou of 0.53, and the performance will be even better if the training epoch is increased.

Code Introduction:

The [training] TRAIN script will automatically train, and the code will automatically randomly scale the data to between 0.5 and 1.5 times the set size to achieve multi-scale training. In order to realize the multi-segmentation project, the compute_gray function in utils will save the mask grayscale value in txt text, and automatically define the output channel for the UNET network

[Introduction] The learning rate is cos decayed, the loss and iou curves for the training and test sets can be viewed within the run_results file, and the images are drawn by the matplotlib library. In addition to this, a training log is kept, the best weights, etc. In the training log, you can see the iou, recall, precision, and global pixel point accuracy for each category, etc.

Put the image to be inferred in the inference directory and run the predict script directly without setting parameters.

Specific reference to the README file, can be used by all white people

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