Mask R-CNN is a popular deep learning model for object detection and segmentation. It can be trained using the maskrcnn\_benchmark library. However, sometimes you might encounter errors during training. In this article, we will discuss some common training errors and how to troubleshoot them.
1. RuntimeError: CUDA out of memory
This error occurs when the GPU memory is not sufficient to hold the model and the input data. The solution is to reduce the batch size or to use a GPU with more memory. You can also use gradient checkpointing to reduce memory usage, but it will increase training time.
2. ValueError: not enough value to unpack (expected 2, got 1)
This error occurs when the number of classes in the dataset is not equal to the number of classes in the model configuration. Make sure that the number of classes in the dataset and the model configuration are the same. You can check the number of classes in the dataset by counting the number of unique labels.
3. UserWarning: The number of detected classes 1 is less than the number of classes 2 in the label map
This warning occurs when the number of classes in the dataset is less than the number of classes in the model configuration. The solution is to add a new class to the dataset or to remove a class from the model configuration. You can also ignore this warning if you are sure that the dataset and the model configuration are correct.
4. AssertionError: Boxes and labels have incompatible shapes
This error occurs when the shapes of the boxes and the labels are not compatible. Make sure that the shapes of the boxes and the labels are the same. You can check the shapes of the boxes and the labels by printing them.
5. UserWarning: The shape of the masks and the boxes are not compatible
This warning occurs when the shapes of the masks and the boxes are not compatible. Make sure that the shapes of the masks and the boxes are the same. You can check the shapes of the masks and the boxes by printing them.
6. UserWarning: The number of masks is different from the number of boxes
This warning occurs when the number of masks is different from the number of boxes. Make sure that the number of masks is equal to the number of boxes. You can check the number of masks and the number of boxes by printing them.
7. UserWarning: The number of masks is different from the number of labels
This warning occurs when the number of masks is different from the number of labels. Make sure that the number of masks is equal to the number of labels. You can check the number of masks and the number of labels by printing them.
8. UserWarning: The shape of the masks is not compatible with the number of labels
This warning occurs when the shape of the masks is not compatible with the number of labels. Make sure that the shape of the masks is compatible with the number of labels. You can check the shape of the masks and the number of labels by printing them.
9. UserWarning: The shape of the masks is not compatible with the shape of the boxes
This warning occurs when the shape of the masks is not compatible with the shape of the boxes. Make sure that the shape of the masks is compatible with the shape of the boxes. You can check the shape of the masks and the shape of the boxes by printing them.
10. UserWarning: The shape of the masks is not compatible with the image
This warning occurs when the shape of the masks is not compatible with the image. Make sure that the shape of the masks is compatible with the image. You can check the shape of the masks and the shape of the image by printing them.
References
| Title | Link |
|---|---|
| Mask R-CNN for Object Detection and Segmentation | https://arxiv.org/abs/1703.06870 |
| Mask R-CNN GitHub Repository | https://github.com/facebookresearch/maskrcnn-benchmark |