YOLOv3 in PyTorch > ONNX > CoreML > TFLite
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# master branch (default)* Archive branch: Backwards-compatible with original darknet *.cfg models (⚠️ no longer maintained).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.
| 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
python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
python test.py --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:
bash $ 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):
detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv3 release and saving results to
runs/detect.
bash $ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream rtmp://192.168.1.105/live/test # rtmp stream http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on example images in
data/images: ```bash $ python detect.py --source data/images --weights yolov3.pt --conf 0.25
Namespace(agnosticnms=False, augment=False, classes=None, confthres=0.25, device='', existok=False, imgsize=640, iouthres=0.45, name='exp', project='runs/detect', saveconf=False, savetxt=False, source='data/images/', update=False, viewimg=False, weights=['yolov3.pt']) Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)
Downloading https://github.com/ultralytics/yolov3/releases/download/v1.0/yolov3.pt to yolov3.pt... 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)
```
To run batched inference with YOLO3 and PyTorch Hub: ```python import torch from PIL import Image
model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape() # for PIL/cv2/np inputs and NMS
img1 = Image.open('zidane.jpg') img2 = Image.open('bus.jpg') imgs = [img1, img2] # batched list of images
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-sizeyour GPU allows (batch sizes shown for 16 GB devices).
bash $ python train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24 yolov3-spp.yaml 24 yolov3-tiny.yaml 64
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 https://www.ultralytics.com.
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at [email protected]