369.01MBZIP

This project is a deeplab+Resnet101 multi-scale segmentation practical project (including dataset), backbone replaced by resnet101

The dataset uses the large BRATS image segmentation dataset, which can be run directly

1.train script will automatically train, the code will automatically randomly scale the data to between 0.5-1.5 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 classes for the UNET network

2. The preprocessing functions of the project are all re-implemented, you can see them in transforms.py by yourself.

3. The network was trained for just 10 epochs, and miou reached around 0.51. The learning rate was cos decayed, and the loss and iou curves for the training and test sets can be viewed within the run_results file, and the images were drawn by the matplotlib library. In addition to this, training logs, best weights, etc. are kept, and in the training logs one can see the iou, recall, precision, and global pixel point accuracy for each category, etc.

4. Prediction scripts can automatically reason about all images under the inference

The code is commented, download and check it by yourself, you want to train your own data, refer to the README file, foolproof operation!

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