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@v2
to 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 |
name of Roboflow dataset / project to be used as target for collected data. | ✅ |
usage_quota_name |
str |
Unique name for Roboflow project pointed by target_project parameter, that identifies usage quota applied for this block.. |
❌ |
data_percentage |
float |
Percent of data that will be saved (in range [0.0, 100.0]). | ✅ |
persist_predictions |
bool |
Boolean flag to decide if predictions should be registered along with images. | ✅ |
minutely_usage_limit |
int |
Maximum number of data registration requests per minute accounted in scope of single server or whole Roboflow platform, depending on context of usage.. | ❌ |
hourly_usage_limit |
int |
Maximum number of data registration requests per hour accounted in scope of single server or whole Roboflow platform, depending on context of usage.. | ❌ |
daily_usage_limit |
int |
Maximum number of data registration requests per day accounted in scope of single server or whole Roboflow platform, depending on context of usage.. | ❌ |
max_image_size |
Tuple[int, int] |
Maximum size of the image to be registered - bigger images will be downsized preserving aspect ratio. Format of data: (width, height) . |
❌ |
compression_level |
int |
Compression level for images registered. | ❌ |
registration_tags |
List[str] |
Tags to be attached to registered datapoints. | ✅ |
disable_sink |
bool |
boolean flag that can be also reference to input - to arbitrarily disable data collection for specific request. | ✅ |
fire_and_forget |
bool |
Boolean flag dictating if sink is supposed to be executed in the background, not waiting on status of registration before end of workflow run. Use True if best-effort registration is needed, use False while debugging and if error handling is needed. |
✅ |
labeling_batch_prefix |
str |
Prefix of the name for labeling batches that will be registered in Roboflow app. | ✅ |
labeling_batches_recreation_frequency |
str |
Frequency in which new labeling batches are created in Roboflow app. New batches are created with name prefix provided in labeling_batch_prefix in given time intervals.Useful in organising labeling flow.. |
❌ |
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:
Identify Changes
,Reference Path Visualization
,Triangle Visualization
,Florence-2 Model
,Stitch OCR Detections
,Florence-2 Model
,Circle Visualization
,Dot Visualization
,Keypoint Detection Model
,Mask Visualization
,Multi-Label Classification Model
,Stability AI Image Generation
,Polygon Zone Visualization
,Velocity
,SIFT Comparison
,Stitch Images
,Classification Label Visualization
,LMM For Classification
,VLM as Detector
,Background Color Visualization
,Byte Tracker
,Camera Focus
,Local File Sink
,Image Convert Grayscale
,Color Visualization
,Single-Label Classification Model
,Detections Transformation
,Pixelate Visualization
,Google Gemini
,Gaze Detection
,Perspective Correction
,Clip Comparison
,Image Preprocessing
,Trace Visualization
,Cosine Similarity
,Absolute Static Crop
,Corner Visualization
,VLM as Classifier
,Stability AI Inpainting
,Keypoint Detection Model
,Path Deviation
,VLM as Classifier
,Detection Offset
,Template Matching
,Identify Outliers
,Halo Visualization
,Roboflow Custom Metadata
,Keypoint Visualization
,SIFT
,CSV Formatter
,Webhook Sink
,SIFT Comparison
,Label Visualization
,Image Contours
,Single-Label Classification Model
,Image Threshold
,Time in Zone
,Twilio SMS Notification
,Line Counter Visualization
,Detections Consensus
,Instance Segmentation Model
,Polygon Visualization
,Byte Tracker
,Object Detection Model
,Time in Zone
,Grid Visualization
,Detections Filter
,VLM as Detector
,Bounding Box Visualization
,Bounding Rectangle
,Roboflow Dataset Upload
,Object Detection Model
,OpenAI
,Model Comparison Visualization
,Ellipse Visualization
,Detections Stitch
,Instance Segmentation Model
,YOLO-World Model
,Slack Notification
,JSON Parser
,Relative Static Crop
,Crop Visualization
,Image Blur
,Email Notification
,LMM
,Google Vision OCR
,Anthropic Claude
,CogVLM
,Llama 3.2 Vision
,Multi-Label Classification Model
,Detections Classes Replacement
,Model Monitoring Inference Aggregator
,Dynamic Crop
,Segment Anything 2 Model
,Line Counter
,Detections Stabilizer
,OCR Model
,Image Slicer
,Path Deviation
,OpenAI
,Roboflow Dataset Upload
,Byte Tracker
,Blur Visualization
- outputs:
Reference Path Visualization
,Triangle Visualization
,Florence-2 Model
,Florence-2 Model
,Circle Visualization
,Dot Visualization
,Keypoint Detection Model
,Mask Visualization
,Multi-Label Classification Model
,Stability AI Image Generation
,Polygon Zone Visualization
,SIFT Comparison
,Classification Label Visualization
,LMM For Classification
,Background Color Visualization
,Local File Sink
,Color Visualization
,Single-Label Classification Model
,Pixelate Visualization
,Google Gemini
,Gaze Detection
,Perspective Correction
,Clip Comparison
,Image Preprocessing
,Trace Visualization
,Corner Visualization
,Keypoint Detection Model
,Stability AI Inpainting
,Path Deviation
,Template Matching
,Halo Visualization
,Roboflow Custom Metadata
,Keypoint Visualization
,Webhook Sink
,Label Visualization
,Single-Label Classification Model
,Line Counter
,Image Threshold
,Time in Zone
,Twilio SMS Notification
,Detections Consensus
,Line Counter Visualization
,Instance Segmentation Model
,Polygon Visualization
,Object Detection Model
,Time in Zone
,Bounding Box Visualization
,Roboflow Dataset Upload
,Size Measurement
,Object Detection Model
,OpenAI
,Model Comparison Visualization
,Ellipse Visualization
,Cache Set
,Detections Stitch
,Instance Segmentation Model
,Slack Notification
,YOLO-World Model
,Pixel Color Count
,CLIP Embedding Model
,Crop Visualization
,Email Notification
,Image Blur
,LMM
,Google Vision OCR
,CogVLM
,Anthropic Claude
,Llama 3.2 Vision
,Multi-Label Classification Model
,Distance Measurement
,Model Monitoring Inference Aggregator
,Segment Anything 2 Model
,Dynamic Crop
,Line Counter
,Path Deviation
,OpenAI
,Roboflow Dataset Upload
,Cache Get
,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 infer on.target_project
(roboflow_project
): name of Roboflow dataset / project to be used as target for collected data.predictions
(Union[object_detection_prediction
,keypoint_detection_prediction
,classification_prediction
,instance_segmentation_prediction
]): Model predictions to be saved.data_percentage
(float
): Percent of data that will be saved (in range [0.0, 100.0]).persist_predictions
(boolean
): Boolean flag to decide if predictions should be registered along with images.registration_tags
(string
): Tags to be attached to registered datapoints.disable_sink
(boolean
): boolean flag that can be also reference to input - to arbitrarily disable data collection for specific request.fire_and_forget
(boolean
): Boolean flag dictating if sink is supposed to be executed in the background, not waiting on status of registration before end of workflow run. UseTrue
if best-effort registration is needed, useFalse
while debugging and if error handling is needed.labeling_batch_prefix
(string
): Prefix of the name for labeling batches that will be registered in Roboflow app.
-
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": "$inputs.image",
"target_project": "my_dataset",
"usage_quota_name": "quota-for-data-sampling-1",
"predictions": "$steps.object_detection_model.predictions",
"data_percentage": true,
"persist_predictions": true,
"minutely_usage_limit": 10,
"hourly_usage_limit": 10,
"daily_usage_limit": 10,
"max_image_size": [
1920,
1080
],
"compression_level": 95,
"registration_tags": [
"location-florida",
"factory-name",
"$inputs.dynamic_tag"
],
"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@v1
to 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 |
name of Roboflow dataset / project to be used as target for collected data. | ✅ |
usage_quota_name |
str |
Unique name for Roboflow project pointed by target_project parameter, that identifies usage quota applied for this block.. |
❌ |
persist_predictions |
bool |
Boolean flag to decide if predictions should be registered along with images. | ❌ |
minutely_usage_limit |
int |
Maximum number of data registration requests per minute accounted in scope of single server or whole Roboflow platform, depending on context of usage.. | ❌ |
hourly_usage_limit |
int |
Maximum number of data registration requests per hour accounted in scope of single server or whole Roboflow platform, depending on context of usage.. | ❌ |
daily_usage_limit |
int |
Maximum number of data registration requests per day accounted in scope of single server or whole Roboflow platform, depending on context of usage.. | ❌ |
max_image_size |
Tuple[int, int] |
Maximum size of the image to be registered - bigger images will be downsized preserving aspect ratio. Format of data: (width, height) . |
❌ |
compression_level |
int |
Compression level for images registered. | ❌ |
registration_tags |
List[str] |
Tags to be attached to registered datapoints. | ✅ |
disable_sink |
bool |
boolean flag that can be also reference to input - to arbitrarily disable data collection for specific request. | ✅ |
fire_and_forget |
bool |
Boolean flag dictating if sink is supposed to be executed in the background, not waiting on status of registration before end of workflow run. Use True if best-effort registration is needed, use False while debugging and if error handling is needed. |
✅ |
labeling_batch_prefix |
str |
Prefix of the name for labeling batches that will be registered in Roboflow app. | ✅ |
labeling_batches_recreation_frequency |
str |
Frequency in which new labeling batches are created in Roboflow app. New batches are created with name prefix provided in labeling_batch_prefix in given time intervals.Useful in organising labeling flow.. |
❌ |
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:
Identify Changes
,Reference Path Visualization
,Triangle Visualization
,Florence-2 Model
,Stitch OCR Detections
,Florence-2 Model
,Circle Visualization
,Dot Visualization
,Keypoint Detection Model
,Mask Visualization
,Multi-Label Classification Model
,Stability AI Image Generation
,Polygon Zone Visualization
,Velocity
,SIFT Comparison
,Stitch Images
,Classification Label Visualization
,LMM For Classification
,VLM as Detector
,Background Color Visualization
,Byte Tracker
,Camera Focus
,Local File Sink
,Image Convert Grayscale
,Color Visualization
,Single-Label Classification Model
,Detections Transformation
,Pixelate Visualization
,Google Gemini
,Gaze Detection
,Perspective Correction
,Clip Comparison
,Image Preprocessing
,Trace Visualization
,Absolute Static Crop
,Corner Visualization
,VLM as Classifier
,Stability AI Inpainting
,Keypoint Detection Model
,Path Deviation
,VLM as Classifier
,Detection Offset
,Template Matching
,Identify Outliers
,Halo Visualization
,Roboflow Custom Metadata
,Keypoint Visualization
,SIFT
,CSV Formatter
,Webhook Sink
,SIFT Comparison
,Label Visualization
,Image Contours
,Single-Label Classification Model
,Image Threshold
,Time in Zone
,Twilio SMS Notification
,Line Counter Visualization
,Detections Consensus
,Instance Segmentation Model
,Polygon Visualization
,Byte Tracker
,Object Detection Model
,Time in Zone
,Grid Visualization
,Detections Filter
,VLM as Detector
,Bounding Box Visualization
,Bounding Rectangle
,Roboflow Dataset Upload
,Object Detection Model
,OpenAI
,Model Comparison Visualization
,Ellipse Visualization
,Detections Stitch
,Instance Segmentation Model
,YOLO-World Model
,Slack Notification
,JSON Parser
,Relative Static Crop
,Crop Visualization
,Image Blur
,Email Notification
,LMM
,Google Vision OCR
,Anthropic Claude
,CogVLM
,Llama 3.2 Vision
,Multi-Label Classification Model
,Detections Classes Replacement
,Model Monitoring Inference Aggregator
,Dynamic Crop
,Segment Anything 2 Model
,Line Counter
,Detections Stabilizer
,OCR Model
,Image Slicer
,Path Deviation
,OpenAI
,Roboflow Dataset Upload
,Byte Tracker
,Blur Visualization
- outputs:
Reference Path Visualization
,Triangle Visualization
,Florence-2 Model
,Florence-2 Model
,Circle Visualization
,Dot Visualization
,Keypoint Detection Model
,Mask Visualization
,Multi-Label Classification Model
,Stability AI Image Generation
,Polygon Zone Visualization
,SIFT Comparison
,Classification Label Visualization
,LMM For Classification
,Background Color Visualization
,Local File Sink
,Color Visualization
,Single-Label Classification Model
,Pixelate Visualization
,Google Gemini
,Gaze Detection
,Perspective Correction
,Clip Comparison
,Image Preprocessing
,Trace Visualization
,Corner Visualization
,Keypoint Detection Model
,Stability AI Inpainting
,Path Deviation
,Template Matching
,Halo Visualization
,Roboflow Custom Metadata
,Keypoint Visualization
,Webhook Sink
,Label Visualization
,Single-Label Classification Model
,Line Counter
,Image Threshold
,Time in Zone
,Twilio SMS Notification
,Detections Consensus
,Line Counter Visualization
,Instance Segmentation Model
,Polygon Visualization
,Object Detection Model
,Time in Zone
,Bounding Box Visualization
,Roboflow Dataset Upload
,Size Measurement
,Object Detection Model
,OpenAI
,Model Comparison Visualization
,Ellipse Visualization
,Cache Set
,Detections Stitch
,Instance Segmentation Model
,Slack Notification
,YOLO-World Model
,Pixel Color Count
,CLIP Embedding Model
,Crop Visualization
,Email Notification
,Image Blur
,LMM
,Google Vision OCR
,CogVLM
,Anthropic Claude
,Llama 3.2 Vision
,Multi-Label Classification Model
,Distance Measurement
,Model Monitoring Inference Aggregator
,Segment Anything 2 Model
,Dynamic Crop
,Line Counter
,Path Deviation
,OpenAI
,Roboflow Dataset Upload
,Cache Get
,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
images
(image
): The image to infer on.predictions
(Union[object_detection_prediction
,keypoint_detection_prediction
,classification_prediction
,instance_segmentation_prediction
]): Reference q detection-like predictions.target_project
(roboflow_project
): name of Roboflow dataset / project to be used as target for collected data.registration_tags
(string
): Tags to be attached to registered datapoints.disable_sink
(boolean
): boolean flag that can be also reference to input - to arbitrarily disable data collection for specific request.fire_and_forget
(boolean
): Boolean flag dictating if sink is supposed to be executed in the background, not waiting on status of registration before end of workflow run. UseTrue
if best-effort registration is needed, useFalse
while debugging and if error handling is needed.labeling_batch_prefix
(string
): Prefix of the name for labeling batches that will be registered in Roboflow app.
-
output
Example JSON definition of step Roboflow Dataset Upload
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_dataset_upload@v1",
"images": "$inputs.image",
"predictions": "$steps.object_detection_model.predictions",
"target_project": "my_dataset",
"usage_quota_name": "quota-for-data-sampling-1",
"persist_predictions": true,
"minutely_usage_limit": 10,
"hourly_usage_limit": 10,
"daily_usage_limit": 10,
"max_image_size": [
512,
512
],
"compression_level": 75,
"registration_tags": [
"location-florida",
"factory-name",
"$inputs.dynamic_tag"
],
"disable_sink": true,
"fire_and_forget": true,
"labeling_batch_prefix": "my_labeling_batch_name",
"labeling_batches_recreation_frequency": "never"
}