CSV Formatter¶
Class: CSVFormatterBlockV1
Source: inference.core.workflows.core_steps.formatters.csv.v1.CSVFormatterBlockV1
The CSV Formatter block prepares structured CSV content based on specified data configurations within a workflow. It allows users to:
-
choose which data appears as columns
-
apply operations to transform the data within the block
-
aggregate whole batch of data into single CSV document (see Data Aggregation section)
The generated CSV content can be used as input for other blocks, such as File Sink or Email Notifications.
Defining columns¶
Use columns_data
property to specify name of the columns and data sources. Defining UQL operations in
columns_operations
you can perform specific operation on each column.
Timestamp column
The block automatically adds timestamp
column and this column name is reserved and cannot be used.
The value of timestamp would be in the following format: 2024-10-18T14:09:57.622297+00:00
, values
are scaled to UTC time zone.
For example, the following definition
columns_data = {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names",
}
columns_operations = {
"predictions": [
{"type": "DetectionsPropertyExtract", "property_name": "class_name"}
],
}
Will generate CSV content:
timestamp,predictions,reference
"2024-10-16T11:15:15.336322+00:00","['a', 'b', 'c']","['a', 'b']"
When applied on object detection predictions from a single image, assuming that $inputs.reference_class_names
holds a list of reference classes.
Data Aggregation¶
The block may take input from different blocks, hence its behavior may differ depending on context:
-
data
batch_size=1
: whenever single input is provided - block will provide the output as in the example above - CSV header will be placed in the first row, the second row will hold the data -
data
batch_size>1
: each datapoint will create one row in CSV document, but only the last batch element will be fed with the aggregated output, leaving other batch elements' outputs empty
When should I expect batch_size=1
?¶
You may expect batch_size=1
in the following scenarios:
-
CSV Formatter was connected to the output of block that only operates on one image and produces one prediction
-
CSV Formatter was connected to the output of block that aggregates data for whole batch and produces single non-empty output (which is exactly the characteristics of CSV Formatter itself)
When should I expect batch_size>1
?¶
You may expect batch_size=1
in the following scenarios:
- CSV Formatter was connected to the output of block that produces single prediction for single image, but batch of images were fed - then CSV Formatter will aggregate the CSV content and output it in the position of the last batch element:
--- input_batch[0] ----> ┌───────────────────────┐ ----> <Empty>
--- input_batch[1] ----> │ │ ----> <Empty>
... │ CSV Formatter │ ----> <Empty>
... │ │ ----> <Empty>
--- input_batch[n] ----> └───────────────────────┘ ----> {"csv_content": "..."}
Format of CSV document for batch_size>1
If the example presented above is applied for larger input batch sizes - the output document structure would be as follows:
timestamp,predictions,reference
"2024-10-16T11:15:15.336322+00:00","['a', 'b', 'c']","['a', 'b']"
"2024-10-16T11:15:15.436322+00:00","['b', 'c']","['a', 'b']"
"2024-10-16T11:15:15.536322+00:00","['a', 'c']","['a', 'b']"
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/csv_formatter@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
columns_data |
Dict[str, Union[bool, float, int, str]] |
References data to be used to construct each and every column. | ✅ |
columns_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. each column. | ❌ |
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 CSV Formatter
in version v1
.
- inputs:
Anthropic Claude
,Triangle Visualization
,Distance Measurement
,Roboflow Dataset Upload
,Absolute Static Crop
,Segment Anything 2 Model
,QR Code Detection
,SIFT
,Identify Outliers
,Byte Tracker
,Detections Transformation
,Detections Stabilizer
,Keypoint Visualization
,VLM as Detector
,SIFT Comparison
,Image Convert Grayscale
,PTZ Tracking (ONVIF)
.md),Multi-Label Classification Model
,Model Comparison Visualization
,Perception Encoder Embedding Model
,Dynamic Zone
,Path Deviation
,JSON Parser
,Roboflow Custom Metadata
,Image Slicer
,Stitch OCR Detections
,Crop Visualization
,Corner Visualization
,Multi-Label Classification Model
,Pixelate Visualization
,Velocity
,Overlap Filter
,Mask Visualization
,Clip Comparison
,Continue If
,Keypoint Detection Model
,Data Aggregator
,Moondream2
,Bounding Rectangle
,Detections Filter
,Bounding Box Visualization
,Barcode Detection
,Camera Focus
,CogVLM
,Instance Segmentation Model
,Dynamic Crop
,Halo Visualization
,Cosine Similarity
,Florence-2 Model
,Time in Zone
,Florence-2 Model
,VLM as Detector
,LMM For Classification
,Ellipse Visualization
,First Non Empty Or Default
,Llama 3.2 Vision
,Line Counter
,Clip Comparison
,Single-Label Classification Model
,Stitch Images
,Environment Secrets Store
,OCR Model
,Property Definition
,Object Detection Model
,Byte Tracker
,Blur Visualization
,Dominant Color
,YOLO-World Model
,Trace Visualization
,LMM
,Label Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Pixel Color Count
,Rate Limiter
,Image Preprocessing
,Relative Static Crop
,Byte Tracker
,Keypoint Detection Model
,Image Threshold
,Reference Path Visualization
,Slack Notification
,Stability AI Outpainting
,Instance Segmentation Model
,Roboflow Dataset Upload
,Detection Offset
,Google Vision OCR
,Dimension Collapse
,Stability AI Inpainting
,Background Color Visualization
,CSV Formatter
,Circle Visualization
,Image Blur
,Google Gemini
,OpenAI
,Line Counter Visualization
,Expression
,Line Counter
,VLM as Classifier
,Local File Sink
,Image Slicer
,Cache Set
,VLM as Classifier
,Color Visualization
,Qwen2.5-VL
,Polygon Visualization
,Email Notification
,Size Measurement
,Single-Label Classification Model
,Gaze Detection
,Perspective Correction
,Detections Consensus
,Path Deviation
,Camera Calibration
,OpenAI
,Detections Merge
,Delta Filter
,Buffer
,SmolVLM2
,Detections Stitch
,Twilio SMS Notification
,OpenAI
,Detections Classes Replacement
,Depth Estimation
,Dot Visualization
,Template Matching
,Classification Label Visualization
,Webhook Sink
,SIFT Comparison
,Stability AI Image Generation
,Object Detection Model
,Image Contours
,Identify Changes
,Grid Visualization
,CLIP Embedding Model
,Cache Get
,Polygon Zone Visualization
- outputs:
Anthropic Claude
,Triangle Visualization
,Trace Visualization
,YOLO-World Model
,Label Visualization
,LMM
,Distance Measurement
,Model Monitoring Inference Aggregator
,Roboflow Dataset Upload
,Time in Zone
,Pixel Color Count
,Image Preprocessing
,Image Threshold
,Reference Path Visualization
,Segment Anything 2 Model
,Slack Notification
,Stability AI Outpainting
,Instance Segmentation Model
,Roboflow Dataset Upload
,Google Vision OCR
,Stability AI Inpainting
,Background Color Visualization
,Image Blur
,Circle Visualization
,Keypoint Visualization
,Google Gemini
,OpenAI
,PTZ Tracking (ONVIF)
.md),Line Counter Visualization
,Model Comparison Visualization
,Perception Encoder Embedding Model
,Path Deviation
,Roboflow Custom Metadata
,Line Counter
,Crop Visualization
,Corner Visualization
,Local File Sink
,Cache Set
,Mask Visualization
,Clip Comparison
,Color Visualization
,Polygon Visualization
,Email Notification
,Size Measurement
,Perspective Correction
,Path Deviation
,OpenAI
,Bounding Box Visualization
,Detections Stitch
,CogVLM
,Twilio SMS Notification
,OpenAI
,Instance Segmentation Model
,Dynamic Crop
,Detections Classes Replacement
,Halo Visualization
,Florence-2 Model
,Dot Visualization
,Classification Label Visualization
,Webhook Sink
,Time in Zone
,SIFT Comparison
,Stability AI Image Generation
,Florence-2 Model
,LMM For Classification
,Ellipse Visualization
,Llama 3.2 Vision
,Line Counter
,CLIP Embedding Model
,Cache Get
,Polygon Zone Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
CSV Formatter
in version v1
has.
Bindings
Example JSON definition of step CSV Formatter
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/csv_formatter@v1",
"columns_data": {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names"
},
"columns_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
}
}