# 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.augelab.com/function-blocks/data-logic/flow-control/batch-processing.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.