Instance Segmentation Model¶
Version v1
¶
Run inference on an instance segmentation model hosted on or uploaded to Roboflow.
You can query any model that is private to your account, or any public model available on Roboflow Universe.
You will need to set your Roboflow API key in your Inference environment to use this block. To learn more about setting your Roboflow API key, refer to the Inference documentation.
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/roboflow_instance_segmentation_model@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
The unique name of this step.. | ❌ |
model_id |
str |
Roboflow model identifier. | ✅ |
class_agnostic_nms |
bool |
Value to decide if NMS is to be used in class-agnostic mode.. | ✅ |
class_filter |
List[str] |
List of classes to retrieve from predictions (to define subset of those which was used while model training). | ✅ |
confidence |
float |
Confidence threshold for predictions. | ✅ |
iou_threshold |
float |
Parameter of NMS, to decide on minimum box intersection over union to merge boxes. | ✅ |
max_detections |
int |
Maximum number of detections to return. | ✅ |
max_candidates |
int |
Maximum number of candidates as NMS input to be taken into account.. | ✅ |
mask_decode_mode |
str |
Parameter of mask decoding in prediction post-processing.. | ✅ |
tradeoff_factor |
float |
Post-processing parameter to dictate tradeoff between fast and accurate. | ✅ |
disable_active_learning |
bool |
Parameter to decide if Active Learning data sampling is disabled for the model. | ✅ |
active_learning_target_dataset |
str |
Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information. | ✅ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Check what blocks you can connect to Instance Segmentation Model
in version v1
.
- inputs:
Label Visualization
,Crop Visualization
,Mask Visualization
,Blur Visualization
,Image Contours
,Bounding Box Visualization
,Image Convert Grayscale
,Camera Focus
,Dot Visualization
,Color Visualization
,Corner Visualization
,Circle Visualization
,Perspective Correction
,Image Slicer
,Triangle Visualization
,Relative Static Crop
,Absolute Static Crop
,Halo Visualization
,Background Color Visualization
,SIFT
,Pixelate Visualization
,Polygon Visualization
,Dynamic Crop
,Image Blur
,Ellipse Visualization
,Image Threshold
- outputs:
Detection Offset
,Dynamic Zone
,Detections Consensus
,Label Visualization
,Crop Visualization
,Roboflow Dataset Upload
,Detections Stitch
,Segment Anything 2 Model
,Blur Visualization
,Mask Visualization
,Detections Classes Replacement
,Property Definition
,Bounding Box Visualization
,Dot Visualization
,Color Visualization
,Roboflow Custom Metadata
,Circle Visualization
,Corner Visualization
,Perspective Correction
,Triangle Visualization
,Detections Filter
,Halo Visualization
,Background Color Visualization
,Roboflow Dataset Upload
,Detections Transformation
,Pixelate Visualization
,Polygon Visualization
,Dynamic Crop
,Ellipse Visualization
The available connections depend on its binding kinds. Check what binding kinds
Instance Segmentation Model
in version v1
has.
Bindings
-
input
images
(image
): The image to infer on.model_id
(roboflow_model_id
): Roboflow model identifier.class_agnostic_nms
(boolean
): Value to decide if NMS is to be used in class-agnostic mode..class_filter
(list_of_values
): List of classes to retrieve from predictions (to define subset of those which was used while model training).confidence
(float_zero_to_one
): Confidence threshold for predictions.iou_threshold
(float_zero_to_one
): Parameter of NMS, to decide on minimum box intersection over union to merge boxes.max_detections
(integer
): Maximum number of detections to return.max_candidates
(integer
): Maximum number of candidates as NMS input to be taken into account..mask_decode_mode
(string
): Parameter of mask decoding in prediction post-processing..tradeoff_factor
(float_zero_to_one
): Post-processing parameter to dictate tradeoff between fast and accurate.disable_active_learning
(boolean
): Parameter to decide if Active Learning data sampling is disabled for the model.active_learning_target_dataset
(roboflow_project
): Target dataset for Active Learning data sampling - see Roboflow Active Learning docs for more information.
-
output
inference_id
(string
): String value.predictions
(instance_segmentation_prediction
): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.
Example JSON definition of step Instance Segmentation Model
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_instance_segmentation_model@v1",
"images": "$inputs.image",
"model_id": "my_project/3",
"class_agnostic_nms": true,
"class_filter": [
"a",
"b",
"c"
],
"confidence": 0.3,
"iou_threshold": 0.4,
"max_detections": 300,
"max_candidates": 3000,
"mask_decode_mode": "accurate",
"tradeoff_factor": 0.3,
"disable_active_learning": true,
"active_learning_target_dataset": "my_project"
}