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

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

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, object_detection_prediction, keypoint_detection_prediction, instance_segmentation_prediction]): Model predictions to be uploaded..
  • target_project (roboflow_project): Roboflow project where data will be saved..
  • registration_tags (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"
}