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