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Buffer

Class: BufferBlockV1

Source: inference.core.workflows.core_steps.fusion.buffer.v1.BufferBlockV1

Maintain a sliding window buffer of the last N values by storing recent inputs in a FIFO (First-In-First-Out) queue, with newest elements added to the beginning and oldest elements automatically removed when the buffer exceeds the specified length, enabling temporal data collection, frame history tracking, batch processing preparation, and sliding window analysis workflows.

How This Block Works

This block maintains a rolling buffer that stores the most recent values passed to it, creating a sliding window of data over time. The block:

  1. Receives input data of any type (images, detections, values, etc.) and configuration parameters (buffer length and padding option)
  2. Maintains an internal buffer that persists across workflow executions:
  3. Buffer is initialized as an empty list when the block is first created
  4. Buffer state persists for the lifetime of the workflow execution
  5. Each buffer block instance maintains its own separate buffer
  6. Adds new data to the buffer:
  7. Inserts the newest value at the beginning (index 0) of the buffer array
  8. Most recent values appear first in the buffer
  9. Older values are shifted to later positions in the array
  10. Manages buffer size:
  11. When buffer length exceeds the specified length parameter, removes the oldest elements
  12. Keeps only the most recent length values
  13. Automatically maintains the sliding window size
  14. Applies optional padding:
  15. If pad is True: Fills the buffer with None values until it reaches exactly length elements
  16. Ensures consistent buffer size even when fewer than length values have been received
  17. If pad is False: Buffer size grows from 0 to length as values are added, then stays at length
  18. Returns the buffered array:
  19. Outputs a list containing the buffered values in order (newest first)
  20. List length equals length (if padding enabled) or current buffer size (if padding disabled)
  21. Values are ordered from most recent (index 0) to oldest (last index)

The buffer implements a sliding window pattern where new data enters at the front and old data exits at the back when capacity is reached. This creates a temporal history of recent values, useful for operations that need to look back at previous frames, detections, or measurements. The buffer works with any data type, making it flexible for images, detections, numeric values, or other workflow outputs.

Common Use Cases

  • Frame History Tracking: Maintain a history of recent video frames for temporal analysis (e.g., track frame sequences, maintain recent image history, collect frames for comparison), enabling temporal frame analysis workflows
  • Detection History: Buffer recent detections for trend analysis or comparison (e.g., track detection changes over time, compare current vs previous detections, analyze detection patterns), enabling detection history workflows
  • Batch Processing Preparation: Collect multiple values before processing them together (e.g., batch process recent images, aggregate multiple detections, prepare data for batch operations), enabling batch processing workflows
  • Sliding Window Analysis: Perform analysis on a rolling window of data (e.g., analyze trends over recent frames, calculate moving averages, detect changes in sequences), enabling sliding window analysis workflows
  • Visualization Sequences: Maintain recent data for animation or sequence visualization (e.g., create frame sequences, visualize temporal changes, display recent history), enabling temporal visualization workflows
  • Temporal Comparison: Compare current values with recent historical values (e.g., compare current frame with previous frames, detect changes over time, analyze temporal patterns), enabling temporal comparison workflows

Connecting to Other Blocks

This block receives data of any type and produces a buffered output array:

  • After any block that produces values to buffer (e.g., buffer images from image sources, buffer detections from detection models, buffer values from analytics blocks), enabling data buffering workflows
  • Before blocks that process arrays to provide batched or historical data (e.g., process buffered images, analyze detection arrays, work with value sequences), enabling array processing workflows
  • Before visualization blocks to display sequences or temporal data (e.g., visualize frame sequences, display detection history, show temporal patterns), enabling temporal visualization workflows
  • Before analysis blocks that require historical data (e.g., analyze trends over time, compare current vs historical, process temporal sequences), enabling temporal analysis workflows
  • Before aggregation blocks to provide multiple values for aggregation (e.g., aggregate buffered values, process multiple detections, combine recent data), enabling aggregation workflows
  • In temporal processing pipelines where maintaining recent history is required (e.g., track changes over time, maintain frame sequences, collect data for temporal analysis), enabling temporal processing workflows

Requirements

This block works with any data type (images, detections, values, etc.). The buffer maintains state across workflow executions within the same workflow instance. The length parameter determines the maximum number of values to keep in the buffer. When pad is enabled, the buffer will always return exactly length elements (padded with None if needed). When pad is disabled, the buffer grows from 0 to length elements as values are added, then maintains length elements by removing oldest values. The buffer persists for the lifetime of the workflow execution and resets when the workflow is restarted.

Type identifier

Use the following identifier in step "type" field: roboflow_core/buffer@v1to add the block as as step in your workflow.

Properties

Name Type Description Refs
name str Enter a unique identifier for this step.. ❌
length int Maximum number of elements to keep in the buffer. When the buffer exceeds this length, the oldest elements are automatically removed. Determines the size of the sliding window. Must be greater than 0. Typical values range from 2-10 for frame sequences, or higher for longer histories.. ❌
pad bool Enable padding to maintain consistent buffer size. If True, the buffer is padded with None values until it reaches exactly length elements, ensuring the output always has length items even when fewer values have been received. If False, the buffer grows from 0 to length as values are added, then maintains length by removing oldest values. Use padding when downstream blocks require a fixed-size array.. ❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Buffer in version v1.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Buffer in version v1 has.

Bindings

  • input

    • data (Union[*, image, list_of_values]): Input data of any type to add to the buffer. Can be images, detections, values, or any other workflow output. Newest values are added to the beginning of the buffer array. The buffer maintains a sliding window of the most recent values..

  • output

Example JSON definition of step Buffer in version v1 
{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/buffer@v1",
    "data": "$steps.visualization",
    "length": 5,
    "pad": true
}