When constructing a dataset pipeline for semantic segmentation that utilizes random fixed-shape cropping, some input images may possess spatial dimensions (height or width) that are smaller than the designated output crop size. To prevent out-of-bounds errors during the cropping operation, these insufficiently sized examples must be programmatically filtered out and excluded from the training pipeline.

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In semantic segmentation tasks, the input image and its corresponding ground-truth label maintain a strict one-to-one spatial correspondence at the pixel level. Because of this precise alignment, simply rescaling images to fit a model's required input shape is problematic; it requires inversely rescaling the predicted pixel classes back to the original dimensions during inference, which introduces inaccuracies along the boundaries of different semantic regions. To avoid these artifacts and preserve exact pixel correspondence, the input images and their labels are typically subjected to random fixed-shape cropping rather than rescaling.

Fixed-Shape Cropping in Semantic Segmentation

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Image Filtering for Random Cropping in Semantic Segmentation

In a Fully Convolutional Network (FCN), input images are processed using random fixed-shape cropping to maintain exact pixel correspondence for semantic segmentation tasks. To ensure that the spatial dimensions of the network's final output perfectly match the input crop after a series of downsampling and upsampling operations, the selected crop dimensions must be exactly divisible by the network's total downsampling factor. For example, if the feature extraction backbone reduces the spatial dimensions by a factor of $$32$$, the height and width of the randomly cropped inputs (such as $$320 \times 480$$) must both be exactly divisible by $$32$$. This prevents spatial mismatch errors when the transposed convolutional layer subsequently upsamples the feature maps back to the original crop size.

```python
# PyTorch
batch_size, crop_size = 32, (320, 480)
train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)
```

```python
# MXNet
batch_size, crop_size = 32, (320, 480)
train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)
```

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