YOLOv3 Files

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: * Master branch: Forward-compatible with all YOLOv5 models and methods (recommended).

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

• Archive branch: Backwards-compatible with original darknet *.cfg models (⚠️ no longer maintained).

  :::bash $ 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

** AP^test 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
Speed_GPU 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:

:::bash
$ pip install -r requirements.txt