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