singhranjodh/yolov3

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

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

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

Archive branch: Backwards-compatible with original darknet *.cfg models (not recommended).

$ 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	AP^val	AP^test	AP₅₀	Speed_GPU	FPS_GPU	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:

$ pip install -r requirements.txt

Tutorials

Train Custom Data 🚀 RECOMMENDED
Weights & Biases Logging 🌟 NEW
Multi-GPU Training
PyTorch Hub ⭐ NEW
ONNX and TorchScript Export
Test-Time Augmentation (TTA)
Model Ensembling
Model Pruning/Sparsity
Hyperparameter Evolution
Transfer Learning with Frozen Layers ⭐ NEW
TensorRT Deployment

Environments

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

Google Colab Notebook with free GPU:
Kaggle Notebook with free GPU: https://www.kaggle.com/ultralytics/yolov3
Google Cloud Deep Learning VM. See GCP Quickstart Guide
Docker Image https://hub.docker.com/r/ultralytics/yolov3. See Docker Quickstart Guide

Inference

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

$ 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:

$ python detect.py --source data/images --weights yolov3.pt --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=['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)

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

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

Training

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 train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24
                                         yolov3-spp.yaml                       24
                                         yolov3-tiny.yaml                      64

Citation

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 https://www.ultralytics.com.

Contact

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 glenn.jocher@ultralytics.com.

singhranjodh / yolov3