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This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.

• April 11, 2021: v5.0 release: YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations.
• January 5, 2021: v4.0 release: nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration.
• August 13, 2020: v3.0 release: nn.Hardswish() activations, data autodownload, native AMP.
• July 23, 2020: v2.0 release: improved model definition, training and mAP.

Pretrained Checkpoints

Model |size
^{(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPS
640 (B) --- |--- |--- |--- |--- |--- |---|--- |--- YOLOv5s |640 |36.7 |36.7 |55.4 |2.0 | |7.3 |17.0 YOLOv5m |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3 YOLOv5l |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4 YOLOv5x |640 |50.4 |50.4 |68.8 |6.1 | |87.7 |218.8 | | | | | | || | YOLOv5s6 |1280 |43.3 |43.3 |61.9 |4.3 | |12.7 |17.4 YOLOv5m6 |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4 YOLOv5l6 |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7 YOLOv5x6 |1280 |54.4 |54.4 |72.0 |22.4 | |141.8 |222.9 | | | | | | || | YOLOv5x6 TTA |1280 |55.0 |55.0 |72.0 |70.8 | |- |-}

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

Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Supervisely Ecosystem  🌟 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

YOLOv5 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 and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Inference

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 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 'https://youtu.be/NUsoVlDFqZg' # YouTube video 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream

To run inference on example images in data/images: ```bash $ python detect.py --source data/images --weights yolov5s.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=['yolov5s.pt']) YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)

Fusing layers... Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s) image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s) Results saved to runs/detect/exp2 Done. (0.103s) ```

PyTorch Hub

To run batched inference with YOLOv5 and PyTorch Hub: ```python import torch

Model

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

Images

dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/' imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images

Inference

results = model(imgs) results.print() # or .show(), .save() ```

Training

Run commands below to reproduce results on COCO dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices). bash $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 yolov5m 40 yolov5l 24 yolov5x 16

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.