|
gptkbp:instance_of
|
gptkb:cosmic_ray_detector
|
|
gptkbp:application
|
Real-time object detection
|
|
gptkbp:architecture
|
gptkb:Darknet-53
gptkb:neural_networks
|
|
gptkbp:can_be_used_in
|
gptkb:robotics
Autonomous vehicles
Augmented reality
Surveillance systems
Video analysis
|
|
gptkbp:can_detect
|
Small objects
80 classes
|
|
gptkbp:community_support
|
gptkb:Yes
|
|
gptkbp:developed_by
|
gptkb:Joseph_Redmon
|
|
gptkbp:evaluates
|
m AP (mean Average Precision)
|
|
gptkbp:features
|
Residual connections
Multi-scale predictions
|
|
gptkbp:gpu
|
gptkb:Yes
|
|
gptkbp:has_variants
|
gptkb:Tiny_YOLOv3
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
YOLOv3
|
|
gptkbp:improves
|
gptkb:YOLOv2
m AP (mean Average Precision)
|
|
gptkbp:input_output
|
416x416
Bounding boxes and class probabilities
|
|
gptkbp:is_a_framework_for
|
gptkb:Darknet
|
|
gptkbp:is_adopted_by
|
gptkb:Industry
Academia
|
|
gptkbp:is_based_on
|
gptkb:Single_Shot_Detector_(SSD)
|
|
gptkbp:is_compared_to
|
gptkb:Faster_R-CNN
gptkb:YOLOv2
gptkb:SSD
|
|
gptkbp:is_compatible_with
|
gptkb:Tensor_Flow
gptkb:Keras
gptkb:Py_Torch
|
|
gptkbp:is_considered
|
State-of-the-art in 2018
|
|
gptkbp:is_documented_in
|
Research papers
Technical blogs
Git Hub repositories
|
|
gptkbp:is_enhanced_by
|
Data augmentation
Transfer learning
Hyperparameter tuning
|
|
gptkbp:is_evaluated_by
|
gptkb:KITTI
gptkb:Open_Images
gptkb:PASCAL_VOC
F1 score
Precision and Recall
Inference time
|
|
gptkbp:is_implemented_in
|
gptkb:C++
gptkb:Python
|
|
gptkbp:is_open_source
|
gptkb:True
gptkb:Yes
|
|
gptkbp:is_part_of
|
gptkb:YOLO_family
gptkb:AI_technology
Deep learning frameworks
Computer vision applications
|
|
gptkbp:is_popular_in
|
Computer Vision Community
Computer vision community
|
|
gptkbp:is_supported_by
|
Forums
Community contributions
Online tutorials
|
|
gptkbp:is_trained_in
|
gptkb:COCO_dataset
|
|
gptkbp:is_used_by
|
gptkb:developers
gptkb:researchers
Industry professionals
|
|
gptkbp:is_used_for
|
Image segmentation
Sports analytics
Traffic monitoring
Healthcare applications
Agricultural monitoring
Face detection
Image classification
Wildlife monitoring
Gesture recognition
Retail analytics
Object tracking
Security applications
License plate recognition
|
|
gptkbp:is_used_in
|
gptkb:robotics
Autonomous vehicles
Augmented reality
Surveillance systems
Real-time object detection
|
|
gptkbp:license
|
gptkb:GPLv3
|
|
gptkbp:losses
|
Cross-entropy loss
|
|
gptkbp:performance
|
Object detection tasks
High speed and accuracy
|
|
gptkbp:predecessor
|
gptkb:YOLOv2
|
|
gptkbp:provides_information_on
|
gptkb:COCO_dataset
|
|
gptkbp:release_date
|
gptkb:2018
|
|
gptkbp:release_year
|
gptkb:2018
|
|
gptkbp:requires
|
High computational power
|
|
gptkbp:runs_through
|
gptkb:microprocessor
gptkb:NVIDIA
|
|
gptkbp:speed
|
gptkb:R-CNN
gptkb:YOLOv4
gptkb:SSD
|
|
gptkbp:successor
|
gptkb:YOLOv4
|
|
gptkbp:suitable_for
|
Embedded systems
|
|
gptkbp:supports
|
Multiple classes
multi-scale predictions
|
|
gptkbp:supports_transfer_learning
|
gptkb:Yes
|
|
gptkbp:uses
|
gptkb:Feature_Pyramid_Networks
gptkb:neural_networks
Batch normalization
Darknet framework
Leaky Re LU activation
|
|
gptkbp:uses_anchor_boxes
|
gptkb:Yes
|
|
gptkbp:uses_batch_normalization
|
gptkb:Yes
|
|
gptkbp:uses_residual_connections
|
gptkb:Yes
|
|
gptkbp:written_in
|
C and CUDA
|
|
gptkbp:bfsParent
|
gptkb:YOLO
|
|
gptkbp:bfsLayer
|
5
|