Prepare Dataset
Download COCO dataset and YOLOv5 formated label via [getcoco.sh](/data/scripts/get_coco.sh), after downloading you will see the following foldersDataset/coco$ ls
annotations labels README.txt train2017.cache val2017.cache
images LICENSE test-dev2017.txt train2017.txt val2017.txt
/Dataset/coco$ ls images/
test2017 train2017 val2017
/Dataset/coco$ cat labels/train2017/000000436300.txt
5 0.527578 0.541663 0.680750 0.889628
0 0.982781 0.696762 0.033719 0.174020
0 0.169938 0.659901 0.020406 0.080844
0 0.093156 0.685223 0.015031 0.081315The label format is YOLO format, not the original COCO annotation format. Each row in the .txt label file is (class x_center y_center width height) format, all these are in normalized xywh format (from 0 - 1). The COCO box format is [top left x, top left y, width, height] in pixels.
If you have the original COCO annotation file (.json), I added this python code to do the conversion: cocojsontoyolo.py. You can add the COCO .json file path in the main function of this file. If you have other dataset format, you can direct convert them to YOLO format, you can also convert them to standard COCO json format, then use cocojsontoyolo.py to convert to YOLO format. If you want to use the Waymo dataset, you can check my WaymoObjectDetection repository.
Install additional packages
Install mish (optional), mish activation is disabled in my new codegit clone https://github.com/JunnYu/mish-cuda
cd mish-cuda
MyRepo/mish-cuda$ python setup.py build installInstall ONNX
pip install numpy protobuf==3.16.0
pip install onnxYOLOv5 Training
Training results for yolov5m based on Waymo dataset (data/waymococo.yaml): ```bash Epoch gpu_mem box obj cls labels img_size 49/49 11.6G 0.05748 0.03393 0.002104 388 640: 100%|█| 4026/4026 [1:49:15< Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 252 all 16102 197601 0.873 0.651 0.73 0.466 vehicle 16102 150878 0.881 0.723 0.791 0.547 pedestrian 16102 45392 0.865 0.639 0.719 0.419 cyclist 16102 1331 0.873 0.591 0.68 0.43150 epochs completed in 93.533 hours.
Training results for yolov5l based on Waymo dataset (data/waymococo.yaml):
```bash
Epoch gpu_mem box obj cls labels img_size
49/49 10.3G 0.05516 0.03241 0.001923 183 640: 100%|█| 8052/8052 [3:15:47<
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 504
all 16102 197601 0.906 0.675 0.759 0.493
vehicle 16102 150878 0.911 0.742 0.816 0.575
pedestrian 16102 45392 0.889 0.658 0.746 0.446
cyclist 16102 1331 0.918 0.626 0.715 0.456
50 epochs completed in 166.672 hours.
Add ScaledYOLOv4 model
Add [yolov4-p5.yaml](/models/yolov4-p5.yaml) from Scaled-YOLOv4. To support the new modules in Scaled_YOLOv4, added classes of BottleneckCSP2 and SPPCSP into the [models/common.py](https://github.com/lkk688/yolov5/blob/master/models/common.py)Training results for yolov4-p5 based on COCO dataset:
yolov5$ python train.py
Epoch gpu_mem box obj cls labels img_size
9/9 9.04G 0.0285 0.05297 0.01291 204 640: 100%|█| 7393/7393 [2:50:21<
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 157
all 5000 36335 0.697 0.618 0.658 0.467Training results for yolov4-p5 based on Waymo dataset (data/waymococo.yaml):
Epoch gpu_mem box obj cls labels img_size
9/9 10G 0.05392 0.03474 0.0022 135 640: 100%|█| 8052/8052 [3:00:30<
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|█| 504
all 16102 197601 0.854 0.593 0.671 0.423
vehicle 16102 150878 0.88 0.661 0.744 0.5
pedestrian 16102 45392 0.854 0.594 0.676 0.39
cyclist 16102 1331 0.826 0.525 0.594 0.378
10 epochs completed in 31.442 hours.
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
See the YOLOv5 Docs for full documentation on training, testing and deployment.
Install
Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txtInference
Inference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release.
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, 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 YOLOv5 release and saving results to runs/detect.
$ python detect.py --source 0 # webcam
file.jpg # image
file.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/NUsoVlDFqZg' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streamTraining
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).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40
yolov5l 24
yolov5x 16
Tutorials
- Train Custom Data 🚀 RECOMMENDED
- Tips for Best Training Results ☘️ RECOMMENDED
- Weights & Biases Logging 🌟 NEW
- Roboflow for Datasets, Labeling, and Active Learning 🌟 NEW
- Multi-GPU Training
- PyTorch Hub ⭐ NEW
- TorchScript, ONNX, CoreML Export 🚀
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- Transfer Learning with Frozen Layers ⭐ NEW
- TensorRT Deployment
Get started in seconds with our verified environments. Click each icon below for details.
| Weights and Biases | Roboflow ⭐ NEW |
|---|---|
| Automatically track and visualize all your YOLOv5 training runs in the cloud with Weights & Biases | Label and export your custom datasets directly to YOLOv5 for training with Roboflow |
YOLOv5-P5 640 Figure (click to expand)
Figure Notes (click to expand)
- COCO AP val denotes mAP@0.5:0.95 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 0.5:0.95 |
mAPval 0.5 |
Speed CPU b1 (ms) |
Speed V100 b1 (ms) |
Speed V100 b32 (ms) |
params (M) |
FLOPs @640 (B) |
|---|---|---|---|---|---|---|---|---|
| YOLOv5n | 640 | 28.4 | 46.0 | 45 | 6.3 | 0.6 | 1.9 | 4.5 |
| YOLOv5s | 640 | 37.2 | 56.0 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| YOLOv5m | 640 | 45.2 | 63.9 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| YOLOv5l | 640 | 48.8 | 67.2 | 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 | 34.0 | 50.7 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| YOLOv5s6 | 1280 | 44.5 | 63.0 | 385 | 8.2 | 3.6 | 16.8 | 12.6 |
| YOLOv5m6 | 1280 | 51.0 | 69.0 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| YOLOv5l6 | 1280 | 53.6 | 71.6 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| YOLOv5x6 + TTA |
1280 1536 |
54.7 55.4 |
72.4 72.3 |
3136 - |
26.2 - |
19.4 - |
140.7 - |
209.8 - |
Table Notes (click to expand)
- All checkpoints are trained to 300 epochs with default settings and hyperparameters.
- 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 --conf 0.25 --iou 0.45 - TTA Test Time Augmentation includes reflection and scale augmentations.
Reproduce bypython val.py --data coco.yaml --img 1536 --iou 0.7 --augment
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!
For YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://ultralytics.com/contact.