/YOLOv8_Efficient

🚀Simple and efficient use for Ultralytics yolov8🚀

Primary LanguagePythonApache License 2.0Apache-2.0

Yolov8_Efficient

Simple and efficient use for yolov8

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About

This is an unofficial repository maintained by independent developers for learning and communication based on the ultralytics v8 Weights and ultralytics Project. If you have more questions and ideas, please feel free to discuss them together. In addition, if ultralytics releases the latest yolov8 warehouse, it is suggested to give priority to the official one.

Performance

  • wandb train log: log
  • Experiment log: log

new News!

  • ... ...
  • 2023/01/10 - add yolov8 metrics and logs
  • 2023/01/09 - add val.py and fix some error
  • 2023/01/07 - fix some error and warning
  • 2023/01/06 - add train.py, detect.py and README.md
  • 2023/01/06 - Create and Init a new repository

TODO：

  • Model testing and validation in progress
  • [ ]

person_with_probing_cane Quickstart

1. CLI

To simply use the latest Ultralytics YOLO models

yolo task=detect    mode=train    model=yolov8n.yaml      args=...
          classify       predict        yolov8n-cls.yaml  args=...
          segment        val            yolov8n-seg.yaml  args=...
                         export         yolov8n.pt        format=onnx

2. Python SDK

To use pythonic interface of Ultralytics YOLO model

from ultralytics import YOLO

model = YOLO("yolov8n.yaml")  # create a new model from scratch
model = YOLO(
    "yolov8n.pt"
)  # load a pretrained model (recommended for best training results)
results = model.train(data="coco128.yaml", epochs=100, imgsz=640, ...)
results = model.val()
results = model.predict(source="bus.jpg")
success = model.export(format="onnx")

If you're looking to modify YOLO for R&D or to build on top of it, refer to Using Trainer Guide on our docs.

mage_man Pretrained Checkpoints

Model size (pixels) mAPval 50-95 mAPval 50 Speed CPU b1 (ms) Speed RTX 3080 b1(ms) layers params (M) FLOPs @640 (B)
yolov8n 640 37.2 53.2 47.2 5.6 168 3.15 8.7
yolov8n-seg 640 36.8 53.0 - 11.3 195 3.40 12.6
yolov8s 640 44.7 62.2 - 5.7 168 11.15 28.6
yolov8s-seg 640 37.0 58.8 - 11.4 195 11.81 42.6
yolov8m 640 49.9 67.4 - 8.3 218 25.89 78.9
yolov8m-seg 640 40.6 63.5 - 15.3 245 27.27 110.2
yolov8l 640 52.4 69.9 - 13.1 268 43.67 165.2
yolov8l-seg 640 42.5 66.1 296.9 16.8 295 45.97 220.5
yolov8x 640 53.5 70.9 334.6 20.4 268 68.20 257.8
yolov8x-seg 640 43.2 67.1 418.8 23.8 295 71.80 344.1

Table Notes The above data is generated by running tests in the following configured environment. See below for details.

  • GPU: NVIDIA GeForce RTX 3080/PCIe/SSE2
  • CPU: Intel® Coreâ„¢ i9-10900K CPU @ 3.70GHz × 20
  • Memory: 31.3 GiB
  • System: Ubuntu 18.04 LTS
  • (ms): The statistical speed here is inference speed

Install

pip install

pip install ultralytics

Development

git clone git@github.com:isLinXu/YOLOv8_Efficient.git
cd YOLOv8_Efficient
cd ultralytics-master
pip install -e .

Usage

Train

  • Single-GPU training:
python train.py --data coco128.yaml --weights weights/yolov8ns.pt --img 640  # from pretrained (recommended)
python train.py --data coco128.yaml --weights '' --cfg yolov8ns.yaml --img 640  # from scratch

Use IDE Pycharm

  • Multi-GPU DDP training:
    python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov8ns.pt --img 640 --device 0,1,2,3

​

detect

python detect.py --weights yolov8s.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 stream

Use IDE Pycharm

val

  • i.e coco128:

Usage:

python val.py --weights yolov8n.pt --data coco128.yaml --img 640

Usage - formats:

python val.py --weights yolov8s.pt                 # PyTorch
                              yolov8s.torchscript        # TorchScript
                              yolov8s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                              yolov8s_openvino_model     # OpenVINO
                              yolov8s.engine             # TensorRT
                              yolov8s.mlmodel            # CoreML (macOS-only)
                              yolov8s_saved_model        # TensorFlow SavedModel
                              yolov8s.pb                 # TensorFlow GraphDef
                              yolov8s.tflite             # TensorFlow Lite
                              yolov8s_edgetpu.tflite     # TensorFlow Edge TPU
                              yolov8s_paddle_model       # PaddlePaddle

Use IDE Pycharm

Acknowledgements

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