singhranjodh / yolov3

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

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CI CPU testing

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

$ git clone  # master branch (default)
$ git clone -b archive  # 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 --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 --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 --data coco.yaml --img 832 --iou 0.65 --augment


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

$ pip install -r requirements.txt



YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Inference runs inference on a variety of sources, downloading models automatically from the latest YOLOv3 release and saving results to runs/detect.

$ python --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            rtsp://  # rtsp stream
                            rtmp://  # rtmp stream
                    # http stream

To run inference on example images in data/images:

$ python --source data/images --weights --conf 0.25

Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=[''])
Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)

Downloading to 100% 118M/118M [00:05<00:00, 24.2MB/s]

Fusing layers... 
Model Summary: 261 layers, 61922845 parameters, 0 gradients
image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 buss, Done. (0.014s)
image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.014s)
Results saved to runs/detect/exp
Done. (0.133s)

PyTorch Hub

To run batched inference with YOLO3 and PyTorch Hub:

import torch
from PIL import Image

# Model
model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape()  # for PIL/cv2/np inputs and NMS

# Images
img1 ='zidane.jpg')
img2 ='bus.jpg')
imgs = [img1, img2]  # batched list of images

# Inference
prediction = model(imgs, size=640)  # includes NMS


Download COCO and run command below. Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).

$ python --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24
                                         yolov3-spp.yaml                       24
                                         yolov3-tiny.yaml                      64



About Us

Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:

  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.

For business inquiries and professional support requests please visit us at


Issues should be raised directly in the repository. For business inquiries or professional support requests please visit or email Glenn Jocher at


YOLOv3 in PyTorch > ONNX > CoreML > TFLite

License:GNU General Public License v3.0


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