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:
- Receives input data of any type (images, detections, values, etc.) and configuration parameters (buffer length and padding option)
- Maintains an internal buffer that persists across workflow executions:
- Buffer is initialized as an empty list when the block is first created
- Buffer state persists for the lifetime of the workflow execution
- Each buffer block instance maintains its own separate buffer
- Adds new data to the buffer:
- Inserts the newest value at the beginning (index 0) of the buffer array
- Most recent values appear first in the buffer
- Older values are shifted to later positions in the array
- Manages buffer size:
- When buffer length exceeds the specified
lengthparameter, removes the oldest elements - Keeps only the most recent
lengthvalues - Automatically maintains the sliding window size
- Applies optional padding:
- If
padis True: Fills the buffer withNonevalues until it reaches exactlylengthelements - Ensures consistent buffer size even when fewer than
lengthvalues have been received - If
padis False: Buffer size grows from 0 tolengthas values are added, then stays atlength - Returns the buffered array:
- Outputs a list containing the buffered values in order (newest first)
- List length equals
length(if padding enabled) or current buffer size (if padding disabled) - 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.
- inputs:
Florence-2 Model,Detections Combine,Roboflow Dataset Upload,Trace Visualization,Delta Filter,Classification Label Visualization,Single-Label Classification Model,Line Counter,Clip Comparison,Ellipse Visualization,Qwen3-VL,Detections Stabilizer,Triangle Visualization,Morphological Transformation,Path Deviation,LMM,First Non Empty Or Default,SmolVLM2,Dimension Collapse,Barcode Detection,Local File Sink,VLM As Classifier,Icon Visualization,QR Code Generator,Stability AI Outpainting,OpenAI,Keypoint Detection Model,Moondream2,Florence-2 Model,Pixelate Visualization,Object Detection Model,Gaze Detection,Cosine Similarity,Background Color Visualization,Clip Comparison,Background Subtraction,Time in Zone,Keypoint Detection Model,Keypoint Visualization,Perception Encoder Embedding Model,Overlap Filter,EasyOCR,Image Blur,Anthropic Claude,Polygon Visualization,SIFT,Webhook Sink,Object Detection Model,Dominant Color,Cache Get,YOLO-World Model,Property Definition,Heatmap Visualization,Image Threshold,Multi-Label Classification Model,Google Gemini,Text Display,Detection Event Log,OpenAI,Instance Segmentation Model,Qwen2.5-VL,Continue If,Single-Label Classification Model,Anthropic Claude,Time in Zone,CSV Formatter,Path Deviation,Rate Limiter,Detections Consensus,Stability AI Inpainting,Roboflow Custom Metadata,QR Code Detection,Polygon Visualization,CogVLM,Velocity,Bounding Box Visualization,CLIP Embedding Model,Identify Outliers,Camera Focus,Llama 3.2 Vision,Email Notification,Dynamic Crop,Image Contours,Time in Zone,LMM For Classification,Buffer,Seg Preview,Segment Anything 2 Model,Stitch Images,Bounding Rectangle,Image Slicer,Line Counter,Byte Tracker,SAM 3,Distance Measurement,Crop Visualization,Grid Visualization,Roboflow Dataset Upload,Google Gemini,Stitch OCR Detections,Reference Path Visualization,Multi-Label Classification Model,Twilio SMS/MMS Notification,Data Aggregator,Image Slicer,Detections Classes Replacement,Detection Offset,Detections Transformation,Google Vision OCR,Camera Focus,Pixel Color Count,Model Comparison Visualization,Template Matching,Model Monitoring Inference Aggregator,Image Preprocessing,Twilio SMS Notification,Color Visualization,Polygon Zone Visualization,OpenAI,Halo Visualization,Instance Segmentation Model,Contrast Equalization,Mask Area Measurement,Google Gemini,Perspective Correction,Circle Visualization,Blur Visualization,Dot Visualization,Camera Calibration,Relative Static Crop,Email Notification,Depth Estimation,VLM As Detector,Mask Visualization,Dynamic Zone,Stability AI Image Generation,Detections Filter,Byte Tracker,Environment Secrets Store,Size Measurement,Halo Visualization,Absolute Static Crop,Detections Stitch,OCR Model,Label Visualization,Detections Merge,Motion Detection,Anthropic Claude,Corner Visualization,Cache Set,Image Convert Grayscale,Stitch OCR Detections,Expression,SIFT Comparison,SIFT Comparison,Detections List Roll-Up,SAM 3,VLM As Detector,Line Counter Visualization,SAM 3,VLM As Classifier,JSON Parser,PTZ Tracking (ONVIF),Slack Notification,Identify Changes,Byte Tracker,OpenAI - outputs:
Florence-2 Model,Roboflow Dataset Upload,Trace Visualization,Seg Preview,Classification Label Visualization,Line Counter,Clip Comparison,Ellipse Visualization,Line Counter,SAM 3,Roboflow Dataset Upload,Grid Visualization,Triangle Visualization,Twilio SMS/MMS Notification,Google Gemini,Path Deviation,Reference Path Visualization,Crop Visualization,VLM As Classifier,Detections Classes Replacement,OpenAI,Keypoint Detection Model,Florence-2 Model,Object Detection Model,Clip Comparison,Color Visualization,Polygon Zone Visualization,OpenAI,Halo Visualization,Time in Zone,Keypoint Detection Model,Keypoint Visualization,Instance Segmentation Model,Anthropic Claude,Polygon Visualization,Google Gemini,Webhook Sink,Perspective Correction,Object Detection Model,Circle Visualization,Dot Visualization,YOLO-World Model,Google Gemini,Email Notification,Instance Segmentation Model,Anthropic Claude,Time in Zone,VLM As Detector,Mask Visualization,Path Deviation,Buffer,Size Measurement,Halo Visualization,Label Visualization,Detections Consensus,Motion Detection,Anthropic Claude,Corner Visualization,Cache Set,Polygon Visualization,Detections List Roll-Up,SAM 3,VLM As Detector,Line Counter Visualization,Bounding Box Visualization,SAM 3,Llama 3.2 Vision,VLM As Classifier,Email Notification,Time in Zone,LMM For Classification,OpenAI
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
output(list_of_values): List of values of any type.
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
}