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