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

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

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[instance_segmentation_prediction, object_detection_prediction, keypoint_detection_prediction, classification_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"
}