Statements (57)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:cosmic_ray_detector
|
| gptkbp:application |
gptkb:robotics
Autonomous vehicles Augmented reality Surveillance systems Retail analytics |
| gptkbp:architecture |
gptkb:neural_networks
|
| gptkbp:community_support |
Active Git Hub repository
|
| gptkbp:developed_by |
gptkb:Ultralytics
|
| gptkbp:evaluates |
Precision
Recall F1 score Benchmarking against other models Real-world performance testing |
| gptkbp:features |
Speed and accuracy
|
| gptkbp:has_documentation |
Available on Git Hub
|
| https://www.w3.org/2000/01/rdf-schema#label |
YOLOv5l
|
| gptkbp:hyperparameters |
Configurable
|
| gptkbp:input_layer |
Image input layer
|
| gptkbp:input_output |
gptkb:Images
Bounding boxes and class probabilities 640x640 Class labels JSON or CSV |
| gptkbp:is_a_framework_for |
gptkb:Py_Torch
|
| gptkbp:language |
gptkb:Python
|
| gptkbp:latest_version |
v5.0
|
| gptkbp:license |
GPL-3.0
|
| gptkbp:losses |
Cross-entropy loss
Io U loss |
| gptkbp:model |
gptkb:YOLOv5m
gptkb:YOLOv5s gptkb:YOLOv5x Varies by configuration Single-stage detector YOLOv5n |
| gptkbp:output_layer |
Bounding box regression
Class prediction |
| gptkbp:performance |
m AP (mean Average Precision)
|
| gptkbp:predecessor |
gptkb:YOLOv4
|
| gptkbp:provides_information_on |
gptkb:COCO_dataset
Custom datasets supported |
| gptkbp:release_date |
gptkb:2020
|
| gptkbp:successor |
gptkb:YOLOv5
|
| gptkbp:supports |
GPU acceleration
FP16 precision Tensor RT optimization Multi-class detection |
| gptkbp:training |
Varies by hardware
End-to-end training |
| gptkbp:training_programs |
Local or cloud-based
Ultralytics YOLOv5 repository |
| gptkbp:uses |
Transfer learning
Real-time object detection Data augmentation techniques |
| gptkbp:bfsParent |
gptkb:YOLO_family
|
| gptkbp:bfsLayer |
7
|