model
- group contains unique settings for each model:gpu_device
- device to use for inference. Right now we support only single GPU.mode
- group contains all mode settings:name
- mode name defines how to apply NN to image (e.g. full_image
- apply NN to full image)model_classes
- which classes will be used, e.g. NN produces 80 classes and you are going to use only a few and ignore others. In that case you should set save_classes
field with the list of interested class names. add_suffix
string will be added to new class to prevent having same class names as already exisiting classes in the project. If you are going to use all model classes just set "save_classes": "__all__"
.person_unet
.mode
- group contains all mode settings:name
: bboxes
- apply NN to bounding boxes of specified objectsfrom_classes
- list of classes. All objects of these classes will be used for inference: 1. get object bounding box, 2. apply padding to bounding box (slightly encrease its size), 3. feed the image area that is defined with the bounding box to the NN.padding
- how to increase input bounding box. Possible values examples: "5%"
or "15px"
save
- save input bounding box after inference if set as true
add_suffix
- suffix for input bounding boxmode
- group contains all mode settings:name
: roi
- apply NN to the image part defined by bounds
bounds
- defines the relevant part of the input image. Below is the graphical explanation.save
- save input bounding box after inference if set as true
class_name
- save input bounding box with given class name if the above option save
is true
.mode
- group contains all mode settings:name
: sliding_window
- selects the sliding window mode for segmentation modelswindow
- defines fixed size of sliding window in pixels; crop with the size will be passed to network (note that in different NN implementations it would be resized to the fixed network input after that )min_overlap
- defines minimal overlap in pixels between different positions of the sliding window; real overlap may be greater to cover the source image entirelysave
- save or not bounds of each sliding window positionclass_name
- class name for the sliding window boundsmodel
- group contains unique settings for each model:confidence_tag_name
- name of the confidence tag for predicted bound boxes.mode
- group contains all mode settings:name
: sliding_window_det
- selects the sliding window mode for detection modelsnms_after
- defines how to apply Non-Maximum-Suppression postprocessing algorithm:enable
- set true
to enable postprocessing, or false
elsewise.iou_threshold
- minimal Intersection over Union value for merging 2 intersected boxes with same classes.confidence_tag_name
- name of confidence tag. (Most always be equal value of model -> confidence_tag_name
)