Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:cosmic_ray_detector
|
| gptkbp:application |
gptkb:Augmented_Reality
gptkb:Autonomous_Vehicles gptkb:robotics Surveillance Systems |
| gptkbp:architecture |
15 layers
|
| gptkbp:based_on |
gptkb:YOLOv3
|
| gptkbp:community_support |
Active community
|
| gptkbp:designed_for |
Real-time Object Detection
|
| gptkbp:developed_by |
gptkb:Joseph_Redmon
|
| gptkbp:evaluates |
m AP (mean Average Precision)
|
| gptkbp:feature |
Low latency
Modular design User-friendly API Scalable architecture High FPS Visualization tools available Supports multi-threading Easy to deploy Lightweight model Integration with Io T devices Compatible with Py Torch Compatible with Tensor Flow Integration with Mobile Applications Integration with Flask Customizable architecture Integration with Django Integration with Edge Computing Integration with Open CV Integration with ROS Integration with Web Applications Pre-trained weights available Supports CPU inference Supports GPU acceleration Supports batch processing Supports multiple classes Supports transfer learning |
| https://www.w3.org/2000/01/rdf-schema#label |
Tiny YOLOv3-15l
|
| gptkbp:input_output |
416x416
Bounding boxes and class probabilities |
| gptkbp:is_a_framework_for |
gptkb:Darknet
|
| gptkbp:is_compared_to |
Less accurate than YOLOv3
More efficient than YOLOv3 |
| gptkbp:language |
gptkb:C
gptkb:Python gptkb:CUDA |
| gptkbp:latest_version |
1.0
|
| gptkbp:license |
gptkb:Open_Source
|
| gptkbp:performance |
gptkb:High
Faster than YOLOv3 |
| gptkbp:platform |
Cross-platform
|
| gptkbp:provides_information_on |
gptkb:COCO_Dataset
|
| gptkbp:release_date |
gptkb:2019
|
| gptkbp:speed |
Real-time processing
|
| gptkbp:tutorials |
Numerous online resources
|
| gptkbp:uses |
gptkb:neural_networks
|
| gptkbp:bfsParent |
gptkb:Tiny_YOLOv3
|
| gptkbp:bfsLayer |
7
|