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