Instance Segmentation Model
Predict the shape, size, and location of objects.
v4
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.
This version of block introduces breaking change in behaviour of mask construction - it uses rle format instead polygon making it possible to retrieve shapes of any kind from remote server.
Type identifier
Use the following identifier in step "type" field: roboflow_core/roboflow_instance_segmentation_model@v4 to add the block as a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence_mode
str
How confidence thresholds are determined..
✅
custom_confidence
float
Custom confidence threshold for predictions..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
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
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Instance Segmentation Model in version v4 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence_mode(string): How confidence thresholds are determined..custom_confidence(float_zero_to_one): Custom confidence threshold for predictions..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..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): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..
output
inference_id(inference_id): Inference identifier.predictions(Union[rle_instance_segmentation_prediction,instance_segmentation_prediction]): Prediction with detected bounding boxes and RLE-encoded segmentation masks in form of sv.Detections(...) object ifrle_instance_segmentation_predictionor Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_prediction.model_id(roboflow_model_id): Roboflow model id.
v3
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@v3 to add the block as a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence_mode
str
How confidence thresholds are determined..
✅
custom_confidence
float
Custom confidence threshold for predictions..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
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
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
enforce_dense_masks_in_inference_models
bool
Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Instance Segmentation Model in version v3 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence_mode(string): How confidence thresholds are determined..custom_confidence(float_zero_to_one): Custom confidence threshold for predictions..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..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): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..enforce_dense_masks_in_inference_models(boolean): Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
output
inference_id(inference_id): Inference identifier.predictions(instance_segmentation_prediction): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.model_id(roboflow_model_id): Roboflow model id.
v2
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@v2 to add the block as a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence
float
Confidence threshold for predictions..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
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
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
enforce_dense_masks_in_inference_models
bool
Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Instance Segmentation Model in version v2 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence(float_zero_to_one): Confidence threshold for predictions..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..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): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..enforce_dense_masks_in_inference_models(boolean): Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
output
inference_id(inference_id): Inference identifier.predictions(instance_segmentation_prediction): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.model_id(roboflow_model_id): Roboflow model id.
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 a step in your workflow.
Properties
Name
Type
Description
Refs
name
str
Enter a unique identifier for this step..
❌
model_id
str
Roboflow model identifier..
✅
confidence
float
Confidence threshold for predictions..
✅
class_filter
List[str]
List of accepted classes. Classes must exist in the model's training set..
✅
iou_threshold
float
Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
✅
max_detections
int
Maximum number of detections to return..
✅
class_agnostic_nms
bool
Boolean flag to specify if NMS is to be used in class-agnostic mode..
✅
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
Boolean flag to disable project-level active learning for this block..
✅
active_learning_target_dataset
str
Target dataset for active learning, if enabled..
✅
enforce_dense_masks_in_inference_models
bool
Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
✅
The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Instance Segmentation Model in version v1 has.
Input and output bindings
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence(float_zero_to_one): Confidence threshold for predictions..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..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): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..enforce_dense_masks_in_inference_models(boolean): Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
output
inference_id(string): String value.predictions(instance_segmentation_prediction): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.
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