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