Name | Modified | Size | Downloads / Week |
---|---|---|---|
Parent folder | |||
darknet53.conv.74 | 2020-11-18 | 162.5 MB | |
yolov3-spp.pt | 2020-11-18 | 252.3 MB | |
yolov3-spp.weights | 2020-11-18 | 252.2 MB | |
yolov3-tiny.conv.15 | 2020-11-18 | 31.0 MB | |
yolov3-tiny.pt | 2020-11-18 | 35.4 MB | |
yolov3-tiny.weights | 2020-11-18 | 35.4 MB | |
yolov3.pt | 2020-11-18 | 248.1 MB | |
yolov3.weights | 2020-11-18 | 248.0 MB | |
yolov3-spp-ultralytics.pt | 2020-11-18 | 252.3 MB | |
README.md | 2020-11-13 | 2.4 kB | |
v8 - Final Darknet Compatible Release.tar.gz | 2020-11-13 | 1.4 MB | |
v8 - Final Darknet Compatible Release.zip | 2020-11-13 | 1.4 MB | |
Totals: 12 Items | 1.5 GB | 119 |
This is the final release of the darknet-compatible version of the https://github.com/ultralytics/yolov3 repository. This release is backwards-compatible with darknet *.cfg files for model configuration.
All pytorch (*.pt
) and darknet (*.weights
) models/backbones available are attached to this release in the Assets section below.
Breaking Changes
There are no breaking changes in this release.
Bug Fixes
- Various
Added Functionality
- Various
Speed
https://cloud.google.com/deep-learning-vm/
Machine type: preemptible n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.14/hr), T4 ($0.11/hr), V100 ($0.74/hr) CUDA with Nvidia Apex FP16/32
HDD: 300 GB SSD
Dataset: COCO train 2014 (117,263 images)
Model: yolov3-spp.cfg
Command: python3 train.py --data coco2017.data --img 416 --batch 32
GPU | n | --batch-size |
img/s | epoch time |
epoch cost |
---|---|---|---|---|---|
K80 | 1 | 32 x 2 | 11 | 175 min | $0.41 |
T4 | 1 2 |
32 x 2 64 x 1 |
41 61 |
48 min 32 min |
$0.09 $0.11 |
V100 | 1 2 |
32 x 2 64 x 1 |
122 178 |
16 min 11 min |
$0.21 $0.28 |
2080Ti | 1 2 |
32 x 2 64 x 1 |
81 140 |
24 min 14 min |
- - |
mAP
Size | COCO mAP @0.5...0.95 |
COCO mAP @0.5 |
|
---|---|---|---|
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
320 | 14.0 28.7 30.5 37.7 |
29.1 51.8 52.3 56.8 |
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
416 | 16.0 31.2 33.9 41.2 |
33.0 55.4 56.9 60.6 |
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
512 | 16.6 32.7 35.6 42.6 |
34.9 57.7 59.5 62.4 |
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics |
608 | 16.6 33.1 37.0 43.1 |
35.4 58.2 60.7 62.8 |
TODO
- NA