> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/data-logic/flow-control/batch-processing.md). # Batch Processing This function block collects multiple input values into a single batch container to reduce memory usage during processing. Use it when you want to group items (images, data, or generic values) into a single stream that other blocks can consume as a batch. ## đŸ“Ĩ Inputs `Input 1`\ First value to be included in the batch. Can be image data, numbers, text, lists, or other generic values. `Input 2`\ Second value to be included in the batch. Additional inputs can be connected depending on your workflow. ## 📤 Outputs `Batch`\ A single batched list that contains the connected input values grouped for batch processing. ## 🕹ī¸ Controls This function block has no interactive controls. It works by grouping whatever is connected to its input sockets. ## đŸŽ¯ Features * Groups multiple inputs into a single, memory-friendly batch object for downstream processing. * Accepts generic data types so it can batch images, numbers, text or lists. * Useful for lowering memory footprint when handling many items in a pipeline. ## 📝 How to use 1. Connect the items you want to group into `Input 1` and `Input 2` (or more inputs if available). 2. The block will output a single `Batch` that contains those inputs as a list. 3. Feed the `Batch` output into blocks that accept batch-style input or into blocks that can iterate over the batch. ## ⚙ī¸ Running behavior When the scenario runs, the function block collects values present at its input sockets and emits them bundled as a single `Batch`. If an input is missing or invalid, the batch will include a placeholder for that entry so downstream blocks can handle it consistently. ## 💡 Tips and Tricks * Create grouped image sets from multiple sources by combining image inputs like `Load Image`, `Camera USB`, `Camera IP (ONVIF)`, or `Video` into a single `Batch` for processing. * Use `Batch Processing` before heavy AI blocks such as `Object Detection`, `Mask Detection`, `Super Resolution`, or `OCR` to reduce peak memory usage when running many images. * After processing a batch, use `Debatch` to split the results back into individual items for drawing or saving. * Merge multiple batches with `Batch Concatenation` when you need to combine batches created in different parts of a workflow. * Use `Get Batch Size` and `Get Element` to inspect or access items inside the `Batch` for conditional logic or selective processing. * When saving results, connect the per-item outputs (after `Debatch`) to `Image Logger`, `Image Write`, or `Record Video` to store processed images efficiently. ## 🛠ī¸ Troubleshooting * No data in `Batch`: Verify the upstream blocks are producing values on the inputs. If an input is intentionally empty, that empty slot will appear in the batch. * Unexpected item order: The batch preserves the order of inputs as connected. Reorder connections if a different sequence is required. * Downstream blocks not accepting the batch: Some blocks expect single items rather than batches. Use `Debatch` to convert a batch back to individual items before feeding those blocks.