Single Shot Detector (SSD)
GPTKB entity
Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Model
|
| gptkbp:based_on |
Convolutional Neural Networks (CN Ns)
|
| gptkbp:can_be_used_with |
other detection methods
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
|
| gptkbp:first_introduced |
gptkb:2016
|
| gptkbp:has_achievements |
high accuracy
|
| https://www.w3.org/2000/01/rdf-schema#label |
Single Shot Detector (SSD)
|
| gptkbp:improves |
gptkb:YOLO_(You_Only_Look_Once)
|
| gptkbp:input_output |
bounding boxes
class scores |
| gptkbp:is_adopted_by |
research institutions
tech companies |
| gptkbp:is_compared_to |
gptkb:Faster_R-CNN
|
| gptkbp:is_considered_as |
state-of-the-art technique
|
| gptkbp:is_evaluated_by |
gptkb:COCO_dataset
gptkb:Pascal_VOC_dataset real-world scenarios mean Average Precision (m AP) |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch |
| gptkbp:is_influenced_by |
YOL Ov2
R-FCN (Region-based Fully Convolutional Networks) |
| gptkbp:is_optimized_for |
speed and accuracy
|
| gptkbp:is_part_of |
deep learning frameworks
computer vision applications |
| gptkbp:is_popular_in |
computer vision community
|
| gptkbp:is_related_to |
image classification
semantic segmentation |
| gptkbp:is_scalable |
different resolutions
|
| gptkbp:is_used_for |
real-time object detection
|
| gptkbp:is_used_in |
gptkb:robot
gptkb:engine gesture recognition surveillance systems augmented reality applications face detection object tracking vehicle detection |
| gptkbp:recognizes |
large objects
small objects |
| gptkbp:requires |
less computational power
|
| gptkbp:security_features |
background clutter
occlusions variations in lighting |
| gptkbp:speed |
traditional methods
|
| gptkbp:suitable_for |
gptkb:smartphone
|
| gptkbp:supports |
multiple object classes
|
| gptkbp:training |
backpropagation
stochastic gradient descent custom datasets |
| gptkbp:uses |
default anchor boxes
|
| gptkbp:utilizes |
feature maps
|