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

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

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[object_detection_prediction, keypoint_detection_prediction, classification_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"
}