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

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

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, keypoint_detection_prediction, object_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"
}