Yolov3 Versions Save

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

v9.6.0

2 years ago

v9.5.0

3 years ago

v9.1

3 years ago

This release is a minor update implementing numerous bug fixes, feature additions and performance improvements from https://github.com/ultralytics/yolov5 to this repo. Models remain unchanged from v9.0 release.

Branch Notice

The ultralytics/yolov3 repository is now divided into two branches:

$ git clone https://github.com/ultralytics/yolov3  # master branch (default)

$ git clone -b archive https://github.com/ultralytics/yolov3  # archive branch

** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from google/automl at batch size 8.

Pretrained Checkpoints

Model APval APtest AP50 SpeedGPU FPSGPU params FLOPS
YOLOv3 43.3 43.3 63.0 4.8ms 208 61.9M 156.4B
YOLOv3-SPP 44.3 44.3 64.6 4.9ms 204 63.0M 157.0B
YOLOv3-tiny 17.6 34.9 34.9 1.7ms 588 8.9M 13.3B

** APtest denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or TTA. Reproduce mAP by python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
** SpeedGPU averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. Reproduce speed by python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** Test Time Augmentation (TTA) runs at 3 image sizes. Reproduce TTA by python test.py --data coco.yaml --img 832 --iou 0.65 --augment

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

v9.0

3 years ago

This release is a major update to the https://github.com/ultralytics/yolov3 repository that brings forward-compatibility with YOLOv5, and incorporates numerous bug fixes, feature additions and performance improvements from https://github.com/ultralytics/yolov5 to this repo.

Branch Notice

The ultralytics/yolov3 repository is now divided into two branches:

$ git clone https://github.com/ultralytics/yolov3  # master branch (default)

$ git clone -b archive https://github.com/ultralytics/yolov3  # archive branch

** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from google/automl at batch size 8.

Pretrained Checkpoints

Model APval APtest AP50 SpeedGPU FPSGPU params FLOPS
YOLOv3 43.3 43.3 63.0 4.8ms 208 61.9M 156.4B
YOLOv3-SPP 44.3 44.3 64.6 4.9ms 204 63.0M 157.0B
YOLOv3-tiny 17.6 34.9 34.9 1.7ms 588 8.9M 13.3B

** APtest denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or TTA. Reproduce mAP by python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
** SpeedGPU averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. Reproduce speed by python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** Test Time Augmentation (TTA) runs at 3 image sizes. Reproduce TTA by python test.py --data coco.yaml --img 832 --iou 0.65 --augment

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

v8

3 years ago

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

v7

3 years ago

This release requires PyTorch >= v1.4 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases

Breaking Changes

There are no breaking changes in this release.

Bug Fixes

  • Various

Added Functionality

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 (help and PR's welcome!)

v6

5 years ago

This release requires PyTorch >= v1.0.0 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases

Breaking Changes

There are no breaking changes in this release.

Bug Fixes

  • NMS now screens out nan and inf values which caused it to hang during some edge cases.

Added Functionality

  • Rectangular Inference. detect.py now automatically processes images, videos and webcam feeds using rectangular inference, letterboxing to the minimum viable 32-multiple. This speeds up inference by up to 40% on HD video: https://github.com/ultralytics/yolov3/issues/232
  • Conv2d + Batchnorm2d Layer Fusion: detect.py now automatically fuses the Conv2d and Batchnorm2d layers in the model before running inference. This speeds up inference by about 5-10%. https://github.com/ultralytics/yolov3/issues/224
  • Hyperparameters all parameterized and grouped togethor in train.py now. Genetic Hyperparameter Evolution code added to train.py.

Performance

https://cloud.google.com/deep-learning-vm/
Machine type: n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
HDD: 100 GB SSD
Dataset: COCO train 2014

GPUs batch_size batch time epoch time epoch cost
(images) (s/batch)
1 K80 16 1.43s 175min $0.58
1 P4 8 0.51s 125min $0.58
1 T4 16 0.78s 94min $0.55
1 P100 16 0.39s 48min $0.39
2 P100 32 0.48s 29min $0.47
4 P100 64 0.65s 20min $0.65
1 V100 16 0.25s 31min $0.41
2 V100 32 0.29s 18min $0.48
4 V100 64 0.41s 13min $0.70
8 V100 128 0.49s 7min $0.80

TODO (help and PR's welcome!)

v5

5 years ago

This release requires PyTorch >= v1.0.0 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases

Breaking Changes

There are no breaking changes in this release.

Bug Fixes

  • None

Added Functionality

  • Video Inference. detect.py now automatically processes both images and videos. Image and video results are saved in their respective formats now (video inference saves new videos).
  • Transfer learning now operates automatically regardless of yolo layer size https://github.com/ultralytics/yolov3/issues/152.

Performance

https://cloud.google.com/deep-learning-vm/
Machine type: n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
HDD: 100 GB SSD
Dataset: COCO train 2014

GPUs batch_size batch time epoch time epoch cost
(images) (s/batch)
1 K80 16 1.43s 175min $0.58
1 P4 8 0.51s 125min $0.58
1 T4 16 0.78s 94min $0.55
1 P100 16 0.39s 48min $0.39
2 P100 32 0.48s 29min $0.47
4 P100 64 0.65s 20min $0.65
1 V100 16 0.25s 31min $0.41
2 V100 32 0.29s 18min $0.48
4 V100 64 0.41s 13min $0.70
8 V100 128 0.49s 7min $0.80

TODO (help and PR's welcome!)

v4

5 years ago

This release requires PyTorch >= v1.0.0 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases

Breaking Changes

There are no breaking changes in this release.

Bug Fixes

Added Functionality

ultralytics/yolov3 with pycocotools darknet/yolov3
YOLOv3-320 51.8 51.5
YOLOv3-416 55.4 55.3
YOLOv3-608 58.2 57.9 
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3

python3 test.py --save-json --conf-thres 0.001 --img-size 416
Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
      Image      Total          P          R        mAP
Calculating mAP: 100%|█████████████████████████████████| 157/157 [08:34<00:00,  2.53s/it]
       5000       5000     0.0896      0.756      0.555
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.312
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.554
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.317
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.343
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.452
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.268
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.411
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.435
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.477
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.587
 
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3.weights')
Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)
      Image      Total          P          R        mAP
Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00,  1.55s/it]
       5000       5000     0.0966      0.786      0.579
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.331
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.582
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.344
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.281
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.437
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.463
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577

Performance

  • mAP computation is much slower now than before using default settings, as --conf-thres 0.001 captures many boxes that all must be passed through NMS. On a V100 test.py runs in about 8 minutes
  • Training speed is improved substantially compared to v3.0 due to the addition of the multithreaded PyTorch dataloader.

https://cloud.google.com/deep-learning-vm/
Machine type: n1-standard-8 (8 vCPUs, 30 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr)
HDD: 100 GB SSD
Dataset: COCO train 2014

GPUs batch_size batch time epoch time epoch cost
(images) (s/batch)
1 K80 16 1.43s 175min $0.58
1 P4 8 0.51s 125min $0.58
1 T4 16 0.78s 94min $0.55
1 P100 16 0.39s 48min $0.39
2 P100 32 0.48s 29min $0.47
4 P100 64 0.65s 20min $0.65
1 V100 16 0.25s 31min $0.41
2 V100 32 0.29s 18min $0.48
4 V100 64 0.41s 13min $0.70
8 V100 128 0.49s 7min $0.80

TODO (help and PR's welcome!)

v3.0

5 years ago

This release requires PyTorch >= v1.0.0 to function properly. Please install the latest version from https://github.com/pytorch/pytorch/releases

Breaking Changes

There are no breaking changes in this release.

Bug Fixes

Added Functionality

ultralytics/yolov3 mAP darknet mAP
YOLOv3-320 51.3 51.5
YOLOv3-416 54.9 55.3
YOLOv3-608 57.9 57.9

Performance

  • 10% improvement in training speed via code optimization. Performance should be further improved by multithreading the dataloader (on the TODO list).

https://cloud.google.com/deep-learning-vm/
Machine type: n1-highmem-4 (4 vCPUs, 26 GB memory)
CPU platform: Intel Skylake
GPUs: 1-4 x NVIDIA Tesla P100
HDD: 100 GB SSD

GPUs batch_size speed COCO epoch
(P100) (images) (s/batch) (min/epoch)
1 16 0.54s 66min
2 32 0.99s 61min
4 64 1.61s 49min

TODO (help and PR's welcome!)