Statements (72)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:adapted_into |
edge computing
different hardware platforms |
| gptkbp:consists_of |
53 convolutional layers
|
| gptkbp:developed_by |
gptkb:Joseph_Redmon
|
| gptkbp:has_achievements |
real-time performance
|
| https://www.w3.org/2000/01/rdf-schema#label |
Darknet-53
|
| gptkbp:improves |
gptkb:Darknet-19
|
| gptkbp:input_output |
bounding boxes
|
| gptkbp:is_adopted_by |
gptkb:academic_research
commercial products researchers and developers |
| gptkbp:is_available_in |
open-source repositories
|
| gptkbp:is_compared_to |
gptkb:Faster_R-CNN
|
| gptkbp:is_compatible_with |
various operating systems
NVIDIAGP Us |
| gptkbp:is_designed_for |
object detection
|
| gptkbp:is_documented_in |
gptkb:archive
|
| gptkbp:is_enhanced_by |
transfer learning
data augmentation techniques |
| gptkbp:is_evaluated_by |
large-scale datasets
precision and recall metrics PASCALVOC m AP (mean Average Precision) real-time benchmarks real-time detection speed |
| gptkbp:is_implemented_in |
Tensor Flow and Py Torch
|
| gptkbp:is_influenced_by |
gptkb:Res_Net_architecture
|
| gptkbp:is_integrated_with |
gptkb:Cloud_Computing_Service
API services machine learning pipelines other AI frameworks |
| gptkbp:is_known_for |
high accuracy
|
| gptkbp:is_optimized_for |
GPU acceleration
|
| gptkbp:is_part_of |
Darknet framework
|
| gptkbp:is_popular_in |
computer vision community
|
| gptkbp:is_promoted_by |
tutorials and courses
|
| gptkbp:is_recognized_by |
state-of-the-art model
a benchmark for speed |
| gptkbp:is_recognized_for |
flexibility in design
|
| gptkbp:is_related_to |
gptkb:YOLO_(You_Only_Look_Once)
|
| gptkbp:is_supported_by |
gptkb:NVIDIA_Tensor_RT
research papers community contributions community forums online forums |
| gptkbp:is_tested_for |
scalability
real-world datasets speed and accuracy custom datasets real-time video streams robustness against adversarial attacks various input sizes |
| gptkbp:is_used_in |
gptkb:engine
robotics applications medical imaging applications |
| gptkbp:is_utilized_in |
startups and enterprises
image classification feature extraction video surveillance security applications augmented reality applications facial recognition systems |
| gptkbp:performance |
object detection tasks
|
| gptkbp:recognizes |
80 object classes
|
| gptkbp:suitable_for |
real-time applications
|
| gptkbp:supports |
multi-scale predictions
|
| gptkbp:training |
gptkb:COCO_dataset
|
| gptkbp:used_in |
YOL Ov3
|
| gptkbp:uses |
skip connections
|
| gptkbp:utilizes |
leaky Re LU activation function
|
| gptkbp:written_in |
C and CUDA
|