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