In machine learning---especially in frameworks like PyTorch---"collate" refers to the process of assembling individual data samples into a batch during training.
It does not mean "ordering" like in printing.
Instead, it means combining multiple samples into a single structure that the model can process at once.
✅ What "collate" means in ML data preparation
When a DataLoader fetches several samples, the collate function:
- Takes a list of samples
For example, each sample might be:
python
(image, label)- Combines ("collates") them into a batch
Turning a list like:
python
[(image1, label1),
(image2, label2),
(image3, label3)]Into tensors like:
batched_images = [image1, image2, image3] → stacked into a tensor
batched_labels = [label1, label2, label3] → tensorThis batching step is the collation.
✅ Why collate is needed
Because your dataset returns one sample at a time , but your model needs a batch .
The collate function ensures:
Images are stacked correctly
Variable-length sequences are padded
Metadata is merged
Custom data structures are handled properly
✔ Example: PyTorch default collate_fn
PyTorch provides a default collator that:
-
Stacks tensors
-
Converts lists of numbers to tensors
-
Leaves strings as lists
-
Works recursively
But you can also write a custom collate_fn if your data requires padding, merging dictionaries, handling variable shapes, etc.