Roboflow Dataset Upload

v2

Class: RoboflowDatasetUploadBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v2.RoboflowDatasetUploadBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Block let users save their images and predictions into Roboflow Dataset. Persisting data from production environments helps iteratively building more robust models.

Block provides configuration options to decide how data should be stored and what are the limits to be applied. We advice using this block in combination with rate limiter blocks to effectively collect data that the model struggle with.

Type identifier

Use the following identifier in step "type" field: roboflow_core/roboflow_dataset_upload@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
target_project	str	Roboflow project where data will be saved..	✅
data_percentage	float	Percent of data that will be saved (0.0 to 100.0)..	✅
minutely_usage_limit	int	Maximum number of image uploads allowed per minute..	❌
hourly_usage_limit	int	Maximum number of image uploads allowed per hour..	❌
daily_usage_limit	int	Maximum number of image uploads allowed per day..	❌
usage_quota_name	str	A unique identifier for tracking usage quotas (minutely, hourly, daily limits)..	❌
max_image_size	Tuple[int, int]	Maximum size of the image to be saved. Bigger images will be downsized preserving aspect ratio..	❌
compression_level	int	Compression level for the registered image..	❌
registration_tags	List[str]	Tags to be attached to the registered image..	✅
persist_predictions	bool	Boolean flag to specify if model predictions should be saved along with the image..	✅
disable_sink	bool	Boolean flag to disable block execution..	✅
fire_and_forget	bool	Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..	✅
labeling_batch_prefix	str	Target batch name for the registered image..	✅
labeling_batches_recreation_frequency	str	Frequency in which new labeling batches are created for uploaded images..	❌

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 Roboflow Dataset Upload in version v2.

• inputs: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Detections Filter, Stability AI Outpainting, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Seg Preview, Overlap Filter, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Circle Visualization, Object Detection Model, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Path Deviation, LMM, Time in Zone, Morphological Transformation, Gaze Detection, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Byte Tracker, Segment Anything 2 Model, Cosine Similarity, SIFT Comparison, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Detection Offset, Icon Visualization, Detections Transformation, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Dynamic Zone, Time in Zone, Twilio SMS Notification, Detections Stitch, Llama 3.2 Vision, Image Blur, Velocity, Slack Notification, Byte Tracker, OpenAI, Multi-Label Classification Model, Image Slicer, OpenAI, Dynamic Crop, Mask Visualization, Detections Merge, Stitch Images, SIFT, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, Byte Tracker, SAM 3, Object Detection Model, Path Deviation, Detections Combine, Anthropic Claude, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Clip Comparison, Email Notification, Depth Estimation, Roboflow Dataset Upload, Image Convert Grayscale, SAM 3, Multi-Label Classification Model, VLM as Detector, CogVLM, Roboflow Custom Metadata, Buffer, Keypoint Detection Model, Stitch OCR Detections, Bounding Rectangle, Keypoint Detection Model, JSON Parser, Camera Calibration, Polygon Visualization, Detections Classes Replacement, Identify Changes, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Stability AI Image Generation, Identify Outliers, QR Code Generator, Relative Static Crop, Dot Visualization, Dimension Collapse, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Single-Label Classification Model, Camera Focus, Detections Stabilizer, Webhook Sink, Image Threshold, Instance Segmentation Model, Perspective Correction, VLM as Classifier, OpenAI, Trace Visualization
• outputs: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Stability AI Outpainting, Pixelate Visualization, Single-Label Classification Model, Perception Encoder Embedding Model, Seg Preview, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Cache Set, Circle Visualization, Object Detection Model, Moondream2, Model Monitoring Inference Aggregator, Path Deviation, LMM, Time in Zone, Morphological Transformation, Gaze Detection, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Segment Anything 2 Model, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Icon Visualization, Distance Measurement, Line Counter Visualization, Halo Visualization, Size Measurement, Dynamic Zone, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, OpenAI, Multi-Label Classification Model, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, SAM 3, Object Detection Model, Path Deviation, Anthropic Claude, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Roboflow Dataset Upload, SAM 3, CogVLM, Multi-Label Classification Model, Roboflow Custom Metadata, Keypoint Detection Model, Stitch OCR Detections, Keypoint Detection Model, Line Counter, Polygon Visualization, CLIP Embedding Model, Detections Classes Replacement, Cache Get, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, Stability AI Image Generation, QR Code Generator, Dot Visualization, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Single-Label Classification Model, Webhook Sink, Instance Segmentation Model, Image Threshold, Perspective Correction, OpenAI, Trace Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Roboflow Dataset Upload in version v2 has.

Bindings

• input

  • images (image): The image to upload..
  • target_project (roboflow_project): Roboflow project where data will be saved..
  • predictions (Union[classification_prediction, instance_segmentation_prediction, keypoint_detection_prediction, object_detection_prediction]): Model predictions to be uploaded..
  • data_percentage (float): Percent of data that will be saved (0.0 to 100.0)..
  • registration_tags (Union[string, list_of_values]): Tags to be attached to the registered image..
  • persist_predictions (boolean): Boolean flag to specify if model predictions should be saved along with the image..
  • disable_sink (boolean): Boolean flag to disable block execution..
  • fire_and_forget (boolean): Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..
  • labeling_batch_prefix (string): Target batch name for the registered image..

• output

  • error_status (boolean): Boolean flag.
  • message (string): String value.

Example JSON definition of step Roboflow Dataset Upload in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_dataset_upload@v2",
    "images": "$inputs.image",
    "target_project": "my_dataset",
    "predictions": "$steps.object_detection_model.predictions",
    "data_percentage": true,
    "minutely_usage_limit": 10,
    "hourly_usage_limit": 10,
    "daily_usage_limit": 10,
    "usage_quota_name": "quota-for-data-sampling-1",
    "max_image_size": [
        1920,
        1080
    ],
    "compression_level": 95,
    "registration_tags": [
        "location-florida",
        "factory-name",
        "$inputs.dynamic_tag"
    ],
    "persist_predictions": true,
    "disable_sink": true,
    "fire_and_forget": "<block_does_not_provide_example>",
    "labeling_batch_prefix": "my_labeling_batch_name",
    "labeling_batches_recreation_frequency": "never"
}

v1

Class: RoboflowDatasetUploadBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.RoboflowDatasetUploadBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Block let users save their images and predictions into Roboflow Dataset. Persisting data from production environments helps iteratively building more robust models.

Block provides configuration options to decide how data should be stored and what are the limits to be applied. We advice using this block in combination with rate limiter blocks to effectively collect data that the model struggle with.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
target_project	str	Roboflow project where data will be saved..	✅
minutely_usage_limit	int	Maximum number of image uploads allowed per minute..	❌
hourly_usage_limit	int	Maximum number of image uploads allowed per hour..	❌
daily_usage_limit	int	Maximum number of image uploads allowed per day..	❌
usage_quota_name	str	A unique identifier for tracking usage quotas (minutely, hourly, daily limits)..	❌
max_image_size	Tuple[int, int]	Maximum size of the image to be saved. Bigger images will be downsized preserving aspect ratio..	❌
compression_level	int	Compression level for the registered image..	❌
registration_tags	List[str]	Tags to be attached to the registered image..	✅
persist_predictions	bool	Boolean flag to specify if model predictions should be saved along with the image..	❌
disable_sink	bool	Boolean flag to disable block execution..	✅
fire_and_forget	bool	Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..	✅
labeling_batch_prefix	str	Target batch name for the registered image..	✅
labeling_batches_recreation_frequency	str	Frequency in which new labeling batches are created for uploaded images..	❌

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 Roboflow Dataset Upload in version v1.

• inputs: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Detections Filter, Stability AI Outpainting, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Seg Preview, Overlap Filter, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Circle Visualization, Object Detection Model, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Path Deviation, LMM, Time in Zone, Morphological Transformation, Gaze Detection, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Byte Tracker, Segment Anything 2 Model, SIFT Comparison, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Detection Offset, Icon Visualization, Detections Transformation, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Dynamic Zone, Time in Zone, Twilio SMS Notification, Detections Stitch, Llama 3.2 Vision, Image Blur, Velocity, Slack Notification, Byte Tracker, OpenAI, Multi-Label Classification Model, Image Slicer, OpenAI, Dynamic Crop, Mask Visualization, Detections Merge, Stitch Images, SIFT, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, Byte Tracker, SAM 3, Object Detection Model, Path Deviation, Detections Combine, Anthropic Claude, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Clip Comparison, Email Notification, Depth Estimation, Roboflow Dataset Upload, Image Convert Grayscale, SAM 3, Multi-Label Classification Model, VLM as Detector, CogVLM, Roboflow Custom Metadata, Buffer, Keypoint Detection Model, Stitch OCR Detections, Bounding Rectangle, Keypoint Detection Model, JSON Parser, Camera Calibration, Polygon Visualization, Detections Classes Replacement, Identify Changes, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Stability AI Image Generation, Identify Outliers, QR Code Generator, Relative Static Crop, Dot Visualization, Dimension Collapse, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Single-Label Classification Model, Camera Focus, Detections Stabilizer, Webhook Sink, Image Threshold, Instance Segmentation Model, Perspective Correction, VLM as Classifier, OpenAI, Trace Visualization
• outputs: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Stability AI Outpainting, Pixelate Visualization, Single-Label Classification Model, Perception Encoder Embedding Model, Seg Preview, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Cache Set, Circle Visualization, Object Detection Model, Moondream2, Model Monitoring Inference Aggregator, Path Deviation, LMM, Time in Zone, Morphological Transformation, Gaze Detection, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Segment Anything 2 Model, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Icon Visualization, Distance Measurement, Line Counter Visualization, Halo Visualization, Size Measurement, Dynamic Zone, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, OpenAI, Multi-Label Classification Model, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, SAM 3, Object Detection Model, Path Deviation, Anthropic Claude, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Roboflow Dataset Upload, SAM 3, CogVLM, Multi-Label Classification Model, Roboflow Custom Metadata, Keypoint Detection Model, Stitch OCR Detections, Keypoint Detection Model, Line Counter, Polygon Visualization, CLIP Embedding Model, Detections Classes Replacement, Cache Get, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, Stability AI Image Generation, QR Code Generator, Dot Visualization, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Single-Label Classification Model, Webhook Sink, Instance Segmentation Model, Image Threshold, Perspective Correction, OpenAI, Trace Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Roboflow Dataset Upload in version v1 has.

Bindings

• input

  • image (image): Image to upload..
  • predictions (Union[classification_prediction, instance_segmentation_prediction, keypoint_detection_prediction, object_detection_prediction]): Model predictions to be uploaded..
  • target_project (roboflow_project): Roboflow project where data will be saved..
  • registration_tags (Union[string, list_of_values]): Tags to be attached to the registered image..
  • disable_sink (boolean): Boolean flag to disable block execution..
  • fire_and_forget (boolean): Boolean flag to run the block asynchronously (True) for faster workflows or synchronously (False) for debugging and error handling..
  • labeling_batch_prefix (string): Target batch name for the registered image..

• output

  • error_status (boolean): Boolean flag.
  • message (string): String value.

Example JSON definition of step Roboflow Dataset Upload in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_dataset_upload@v1",
    "image": "$inputs.image",
    "predictions": "$steps.object_detection_model.predictions",
    "target_project": "my_project",
    "minutely_usage_limit": 10,
    "hourly_usage_limit": 10,
    "daily_usage_limit": 10,
    "usage_quota_name": "quota-for-data-sampling-1",
    "max_image_size": [
        512,
        512
    ],
    "compression_level": 75,
    "registration_tags": [
        "location-florida",
        "factory-name",
        "$inputs.dynamic_tag"
    ],
    "persist_predictions": true,
    "disable_sink": true,
    "fire_and_forget": true,
    "labeling_batch_prefix": "my_labeling_batch_name",
    "labeling_batches_recreation_frequency": "never"
}