YOLOv3 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
We hope that the resources here will help you get the most out of YOLOv3. Please browse the YOLOv3 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!
To request an Enterprise License please complete the form at Ultralytics Licensing.
We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released at https://github.com/ultralytics/ultralytics. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
See the YOLOv8 Docs for details and get started with:
pip install ultralytics
See the YOLOv3 Docs for full documentation on training, testing and deployment. See below for quickstart examples.
Install
Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.
git clone https://github.com/ultralytics/yolov3 # clone
cd yolov3
pip install -r requirements.txt # installInference
YOLOv3 PyTorch Hub inference. Models download automatically from the latest YOLOv3 release.
import torch
# Model
model = torch.hub.load("ultralytics/yolov3", "yolov3") # or yolov5n - yolov5x6, custom
# Images
img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.Inference with detect.py
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 --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streamTraining
The commands below reproduce YOLOv3 COCO
results. Models
and datasets download automatically from the latest
YOLOv3 release. Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the
largest --batch-size possible, or pass --batch-size -1 for
YOLOv3 AutoBatch. Batch sizes shown for V100-16GB.
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
Tutorials
- Train Custom Data 🚀 RECOMMENDED
- Tips for Best Training Results ☘️
- Multi-GPU Training
- PyTorch Hub 🌟 NEW
- TFLite, ONNX, CoreML, TensorRT Export 🚀
- NVIDIA Jetson platform Deployment 🌟 NEW
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- Transfer Learning with Frozen Layers
- Architecture Summary 🌟 NEW
- Roboflow for Datasets, Labeling, and Active Learning
- ClearML Logging 🌟 NEW
- YOLOv5 with Neural Magic's Deepsparse 🌟 NEW
- Comet Logging 🌟 NEW
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
|---|---|---|---|
| Label and export your custom datasets directly to YOLOv3 for training with Roboflow | Automatically track, visualize and even remotely train YOLOv3 using ClearML (open-source!) | Free forever, Comet lets you save YOLOv3 models, resume training, and interactively visualise and debug predictions | Run YOLOv3 inference up to 6x faster with Neural Magic DeepSparse |
Experience seamless AI with Ultralytics HUB ⭐, the all-in-one solution for data visualization, YOLO 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly Ultralytics App. Start your journey for Free now!
YOLOv3 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
YOLOv3-P5 640 Figure
Figure Notes
- COCO AP val denotes [email protected]:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
- GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
- EfficientDet data from google/automl at batch size 8.
- Reproduce by
python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt
| Model | size (pixels) |
mAPval 50-95 |
mAPval 50 |
Speed CPU b1 (ms) |
Speed V100 b1 (ms) |
Speed V100 b32 (ms) |
params (M) |
FLOPs @640 (B) |
|---|---|---|---|---|---|---|---|---|
| YOLOv5n | 640 | 28.0 | 45.7 | 45 | 6.3 | 0.6 | 1.9 | 4.5 |
| YOLOv5s | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| YOLOv5m | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| YOLOv5l | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| YOLOv5x | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| YOLOv5n6 | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| YOLOv5s6 | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| YOLOv5m6 | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| YOLOv5l6 | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| YOLOv5x6 + TTA |
1280 1536 |
55.0 55.8 |
72.7 72.7 |
3136 - |
26.2 - |
19.4 - |
140.7 - |
209.8 - |
Table Notes
- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.
- mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce bypython val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65 - Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.
Reproduce bypython val.py --data coco.yaml --img 640 --task speed --batch 1 - TTA Test Time Augmentation includes reflection and scale augmentations.
Reproduce bypython val.py --data coco.yaml --img 1536 --iou 0.7 --augment
Our new YOLOv5 release v7.0 instance segmentation models are the fastest and most accurate in the world, beating all current SOTA benchmarks. We've made them super simple to train, validate and deploy. See full details in our Release Notes and visit our YOLOv5 Segmentation Colab Notebook for quickstart tutorials.
Segmentation Checkpoints
We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google Colab Pro notebooks for easy reproducibility.
| Model | size (pixels) |
mAPbox 50-95 |
mAPmask 50-95 |
Train time 300 epochs A100 (hours) |
Speed ONNX CPU (ms) |
Speed TRT A100 (ms) |
params (M) |
FLOPs @640 (B) |
|---|---|---|---|---|---|---|---|---|
| YOLOv5n-seg | 640 | 27.6 | 23.4 | 80:17 | 62.7 | 1.2 | 2.0 | 7.1 |
| YOLOv5s-seg | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
| YOLOv5m-seg | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
| YOLOv5l-seg | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
| YOLOv5x-seg | 640 | 50.7 | 41.4 | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
- All checkpoints are trained to 300 epochs with SGD optimizer with
lr0=0.01andweight_decay=5e-5at image size 640 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official - Accuracy values are for single-model single-scale on COCO dataset.
Reproduce bypython segment/val.py --data coco.yaml --weights yolov5s-seg.pt - Speed averaged over 100 inference images using a Colab Pro A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image).
Reproduce bypython segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1 - Export to ONNX at FP32 and TensorRT at FP16 done with
export.py.
Reproduce bypython export.py --weights yolov5s-seg.pt --include engine --device 0 --half
Segmentation Usage Examples 
YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with --data coco128-seg.yaml argument and manual download of COCO-segments dataset with bash data/scripts/get_coco.sh --train --val --segments and then python train.py --data coco.yaml.
# Single-GPU
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
# Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3Validate YOLOv5s-seg mask mAP on COCO dataset:
bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images)
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validateUse pretrained YOLOv5m-seg.pt to predict bus.jpg:
python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpgmodel = torch.hub.load(
"ultralytics/yolov5", "custom", "yolov5m-seg.pt"
) # load from PyTorch Hub (WARNING: inference not yet supported) |
 |
|---|
Export YOLOv5s-seg model to ONNX and TensorRT:
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0YOLOv5 release v6.2 brings support for classification model training, validation and deployment! See full details in our Release Notes and visit our YOLOv5 Classification Colab Notebook for quickstart tutorials.
Classification Checkpoints
We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google Colab Pro for easy reproducibility.
| Model | size (pixels) |
acc top1 |
acc top5 |
Training 90 epochs 4xA100 (hours) |
Speed ONNX CPU (ms) |
Speed TensorRT V100 (ms) |
params (M) |
FLOPs @224 (B) |
|---|---|---|---|---|---|---|---|---|
| YOLOv5n-cls | 224 | 64.6 | 85.4 | 7:59 | 3.3 | 0.5 | 2.5 | 0.5 |
| YOLOv5s-cls | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
| YOLOv5m-cls | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
| YOLOv5l-cls | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
| YOLOv5x-cls | 224 | 79.0 | 94.4 | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
| ResNet18 | 224 | 70.3 | 89.5 | 6:47 | 11.2 | 0.5 | 11.7 | 3.7 |
| ResNet34 | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
| ResNet50 | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
| ResNet101 | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
| EfficientNet_b0 | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
| EfficientNet_b1 | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
| EfficientNet_b2 | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
| EfficientNet_b3 | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
Table Notes (click to expand)
- All checkpoints are trained to 90 epochs with SGD optimizer with
lr0=0.001andweight_decay=5e-5at image size 224 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 - Accuracy values are for single-model single-scale on ImageNet-1k dataset.
Reproduce bypython classify/val.py --data ../datasets/imagenet --img 224 - Speed averaged over 100 inference images using a Google Colab Pro V100 High-RAM instance.
Reproduce bypython classify/val.py --data ../datasets/imagenet --img 224 --batch 1 - Export to ONNX at FP32 and TensorRT at FP16 done with
export.py.
Reproduce bypython export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224
Classification Usage Examples
YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the --data argument. To start training on MNIST for example use --data mnist.
# Single-GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
# Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validateUse pretrained YOLOv5s-cls.pt to predict bus.jpg:
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpgmodel = torch.hub.load(
"ultralytics/yolov5", "custom", "yolov5s-cls.pt"
) # load from PyTorch HubExport a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224Get started in seconds with our verified environments. Click each icon below for details.
We love your input! We want to make contributing to YOLOv3 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv3 Survey to send us feedback on your experiences. Thank you to all our contributors!
Ultralytics offers two licensing options to accommodate diverse use cases:
- AGPL-3.0 License: This OSI-approved open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the LICENSE file for more details.
- Enterprise License: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through Ultralytics Licensing.
For YOLOv3 bug reports and feature requests please visit GitHub Issues, and join our Discord community for questions and discussions!