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