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Cleanup (#168)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kalen Michael <kalenmike@gmail.com>
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Glenn Jocher
GitHub
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commit
422c49d439
26
.github/translate-readme.yml
vendored
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26
.github/translate-readme.yml
vendored
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@ -0,0 +1,26 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md
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name: Translate README
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on:
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push:
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branches:
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- translate_readme # replace with 'main' to enable action
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paths:
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- README.md
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jobs:
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Translate:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- name: Setup Node.js
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uses: actions/setup-node@v3
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with:
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node-version: 16
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# ISO Language Codes: https://cloud.google.com/translate/docs/languages
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- name: Adding README - Chinese Simplified
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uses: dephraiim/translate-readme@main
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with:
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LANG: zh-CN
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2
.github/workflows/ci.yaml
vendored
2
.github/workflows/ci.yaml
vendored
@ -1,4 +1,4 @@
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# YOLO 🚀 by Ultralytics, GPL-3.0 license
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# YOLO Continuous Integration (CI) GitHub Actions tests
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name: Ultralytics CI
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2
.github/workflows/docker.yaml
vendored
2
.github/workflows/docker.yaml
vendored
@ -1,4 +1,4 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Builds ultralytics/ultralytics:latest images on DockerHub https://hub.docker.com/r/ultralytics
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name: Publish Docker Images
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33
README.md
33
README.md
@ -68,7 +68,22 @@ pip install ultralytics
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<details open>
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<summary>Usage</summary>
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YOLOv8 may be used in a python environment:
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YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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```bash
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yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
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```
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`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/config/) in the YOLOv8 [Docs](https://docs.ultralytics.com).
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```bash
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yolo task=detect mode=train model=yolov8n.pt args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
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```
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YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
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```python
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from ultralytics import YOLO
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@ -81,18 +96,16 @@ model.predict(source="https://ultralytics.com/images/bus.jpg") # predict on an
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model.export(format="onnx") # export the model to ONNX format
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```
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Or with CLI `yolo` commands:
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```bash
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yolo task=detect mode=train model=yolov8n.pt args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
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```
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/ultralytics/releases).
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### Known Issues / TODOs
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- [ ] TensorFlow exports
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- [ ] GPU exports
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- [ ] DDP resume
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- [ ] [arxiv.org](https://arxiv.org) paper
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</details>
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## <div align="center">Checkpoints</div>
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@ -1,4 +1,4 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
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@ -1,4 +1,4 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
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@ -1,4 +1,4 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
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@ -2,7 +2,6 @@
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# Usage: pip install -r requirements.txt
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# Base ----------------------------------------
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fire>=0.4.0
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hydra-core>=1.2.0
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matplotlib>=3.2.2
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numpy>=1.18.5
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2
setup.py
2
setup.py
@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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import re
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from pathlib import Path
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@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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import os
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from pathlib import Path
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@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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from ultralytics import YOLO
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from ultralytics.yolo.configs import get_config
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from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT
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@ -1,3 +1,5 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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from pathlib import Path
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from ultralytics import YOLO
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675
tutorial.ipynb
675
tutorial.ipynb
@ -1,675 +0,0 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "YOLOv8 Tutorial",
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"provenance": [],
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"toc_visible": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "t6MPjfT5NrKQ"
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},
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"source": [
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"<div align=\"center\">\n",
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"\n",
|
||||
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
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" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
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"\n",
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"\n",
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"<br>\n",
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" <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a>\n",
|
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" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
|
||||
" <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
|
||||
"<br>\n",
|
||||
"\n",
|
||||
"Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) object detection and image segmentation model developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
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"\n",
|
||||
"The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection and image segmentation tasks. They can be trained on large datasets and are capable of running on a variety of hardware platforms, from CPUs to GPUs.\n",
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||||
"\n",
|
||||
"Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page will help you get the most out of YOLOv8. Please feel free to browse the <a href=\"https://docs.ultralytics.com/\">YOLOv8 Docs</a> and reach out to us with any questions or feedback.\n",
|
||||
"\n",
|
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"</div>"
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]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
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||||
"metadata": {
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"id": "7mGmQbAO5pQb"
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||||
},
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"source": [
|
||||
"# Setup\n",
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"\n",
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"Clone GitHub [repository](https://github.com/ultralytics/ultralytics), install [dependencies](https://github.com/ultralytics/ultralytics/blob/master/requirements.txt) and check PyTorch and GPU."
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||||
]
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||||
},
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{
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||||
"cell_type": "code",
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"metadata": {
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"id": "wbvMlHd_QwMG",
|
||||
"colab": {
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||||
"base_uri": "https://localhost:8080/"
|
||||
},
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||||
"outputId": "276a3983-eedd-4eeb-9701-2ae78e1b4c44"
|
||||
},
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||||
"source": [
|
||||
"# Pip install (recommended)\n",
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"!pip install ultralytics\n",
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"\n",
|
||||
"from IPython import display\n",
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||||
"display.clear_output()\n",
|
||||
"!yolo mode=checks"
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||||
],
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||||
"execution_count": null,
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||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"\u001b[2K\r\u001b[2K\rUltralytics YOLO 🚀 0.0.59 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.1/166.8 GB disk)\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
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{
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"cell_type": "code",
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||||
"source": [
|
||||
"# Git clone (requires GitHub token)\n",
|
||||
"!git clone https://github.com/ultralytics/ultralytics\n",
|
||||
"!pip install -e ultralytics\n",
|
||||
"\n",
|
||||
"from IPython import display\n",
|
||||
"display.clear_output()\n",
|
||||
"!yolo mode=checks"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "TUFPge7f_1ms"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
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||||
"id": "4JnkELT0cIJg"
|
||||
},
|
||||
"source": [
|
||||
"# 1. Predict\n",
|
||||
"\n",
|
||||
"`yolo mode=predict` runs YOLOv8 inference on a variety of sources, downloading models automatically from the [latest YOLOv8 release](https://github.com/ultralytics/ultralytics/releases), and saving results to `runs/predict`.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
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||||
"id": "zR9ZbuQCH7FX",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "b4d62712-a064-4f5b-856f-8a1a58e900ec"
|
||||
},
|
||||
"source": [
|
||||
"!yolo task=detect mode=predict model=yolov8n.pt conf=0.25 source='https://ultralytics.com/images/zidane.jpg'\n",
|
||||
"# display.Image(filename='runs/detect/predict/zidane.jpg', width=600)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
|
||||
"100% 165k/165k [00:00<00:00, 72.6MB/s]\n",
|
||||
"Ultralytics YOLO 🚀 0.0.59 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
|
||||
"100% 6.24M/6.24M [00:03<00:00, 2.14MB/s]\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 78.0ms\n",
|
||||
"Speed: 0.6ms pre-process, 78.0ms inference, 29.2ms postprocess per image at shape (1, 3, 640, 640)\n",
|
||||
"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "hkAzDWJ7cWTr"
|
||||
},
|
||||
"source": [
|
||||
" \n",
|
||||
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "0eq1SMWl6Sfn"
|
||||
},
|
||||
"source": [
|
||||
"# 2. Val\n",
|
||||
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv8 release](https://github.com/ultralytics/ultralytics/releases). To show results by class use the `--verbose` flag."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "WQPtK1QYVaD_"
|
||||
},
|
||||
"source": [
|
||||
"# Download COCO val\n",
|
||||
"import torch\n",
|
||||
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
|
||||
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "X58w8JLpMnjH",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "a3853c39-e2d3-40ca-bdab-32880af1751b"
|
||||
},
|
||||
"source": [
|
||||
"# Validate YOLOv8n on COCO128 val\n",
|
||||
"!yolo task=detect mode=val model=yolov8n.pt data=coco128.yaml"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Ultralytics YOLO 🚀 0.0.59 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
"\n",
|
||||
"Dataset not found ⚠️, missing paths ['/datasets/coco128/images/train2017']\n",
|
||||
"Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
|
||||
"100% 6.66M/6.66M [00:03<00:00, 2.27MB/s]\n",
|
||||
"Dataset download success ✅ (4.8s), saved to \u001b[1m/datasets\u001b[0m\n",
|
||||
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
|
||||
"100% 755k/755k [00:00<00:00, 115MB/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 948.62it/s] \n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /datasets/coco128/labels/train2017.cache\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:07<00:00, 1.08it/s]\n",
|
||||
" all 128 929 0.639 0.523 0.608 0.447\n",
|
||||
" person 128 254 0.82 0.679 0.767 0.54\n",
|
||||
" bicycle 128 6 0.369 0.167 0.294 0.203\n",
|
||||
" car 128 46 0.746 0.196 0.24 0.147\n",
|
||||
" motorcycle 128 5 0.687 0.8 0.898 0.707\n",
|
||||
" airplane 128 6 0.645 0.667 0.851 0.689\n",
|
||||
" bus 128 7 0.743 0.714 0.727 0.633\n",
|
||||
" train 128 3 0.532 0.667 0.741 0.554\n",
|
||||
" truck 128 12 0.667 0.333 0.448 0.263\n",
|
||||
" boat 128 6 0.226 0.167 0.332 0.184\n",
|
||||
" traffic light 128 14 0.535 0.143 0.193 0.146\n",
|
||||
" stop sign 128 2 0.601 0.5 0.828 0.68\n",
|
||||
" bench 128 9 0.716 0.444 0.518 0.292\n",
|
||||
" bird 128 16 0.815 0.75 0.859 0.499\n",
|
||||
" cat 128 4 0.782 0.909 0.945 0.749\n",
|
||||
" dog 128 9 0.603 0.889 0.764 0.58\n",
|
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" horse 128 2 0.73 1 0.995 0.572\n",
|
||||
" elephant 128 17 0.794 0.882 0.934 0.74\n",
|
||||
" bear 128 1 0.615 1 0.995 0.995\n",
|
||||
" zebra 128 4 0.837 1 0.995 0.964\n",
|
||||
" giraffe 128 9 0.717 1 0.975 0.676\n",
|
||||
" backpack 128 6 0.584 0.333 0.432 0.276\n",
|
||||
" umbrella 128 18 0.83 0.556 0.674 0.409\n",
|
||||
" handbag 128 19 1 0.11 0.255 0.111\n",
|
||||
" tie 128 7 0.669 0.714 0.711 0.519\n",
|
||||
" suitcase 128 4 0.773 0.867 0.945 0.644\n",
|
||||
" frisbee 128 5 0.586 0.8 0.732 0.686\n",
|
||||
" skis 128 1 0.733 1 0.995 0.497\n",
|
||||
" snowboard 128 7 1 0.546 0.753 0.516\n",
|
||||
" sports ball 128 6 0.574 0.5 0.464 0.263\n",
|
||||
" kite 128 10 0.597 0.5 0.548 0.22\n",
|
||||
" baseball bat 128 4 0.496 0.5 0.269 0.12\n",
|
||||
" baseball glove 128 7 0.598 0.429 0.429 0.293\n",
|
||||
" skateboard 128 5 0.806 0.6 0.635 0.411\n",
|
||||
" tennis racket 128 7 0.955 0.429 0.564 0.373\n",
|
||||
" bottle 128 18 0.601 0.444 0.444 0.281\n",
|
||||
" wine glass 128 16 0.815 0.562 0.604 0.377\n",
|
||||
" cup 128 36 0.606 0.25 0.425 0.308\n",
|
||||
" fork 128 6 0.518 0.167 0.17 0.169\n",
|
||||
" knife 128 16 0.646 0.438 0.581 0.365\n",
|
||||
" spoon 128 22 0.491 0.227 0.334 0.186\n",
|
||||
" bowl 128 28 0.643 0.536 0.632 0.498\n",
|
||||
" banana 128 1 0 0 0.142 0.0529\n",
|
||||
" sandwich 128 2 0.226 0.5 0.414 0.414\n",
|
||||
" orange 128 4 1 0 0.87 0.562\n",
|
||||
" broccoli 128 11 0.416 0.182 0.239 0.197\n",
|
||||
" carrot 128 24 0.719 0.458 0.583 0.377\n",
|
||||
" hot dog 128 2 0.269 0.5 0.695 0.695\n",
|
||||
" pizza 128 5 0.673 1 0.995 0.859\n",
|
||||
" donut 128 14 0.673 1 0.976 0.886\n",
|
||||
" cake 128 4 0.627 1 0.945 0.824\n",
|
||||
" chair 128 35 0.428 0.543 0.498 0.251\n",
|
||||
" couch 128 6 0.47 0.5 0.673 0.537\n",
|
||||
" potted plant 128 14 0.808 0.643 0.714 0.501\n",
|
||||
" bed 128 3 0.842 0.667 0.741 0.559\n",
|
||||
" dining table 128 13 0.413 0.538 0.47 0.378\n",
|
||||
" toilet 128 2 0.618 0.5 0.62 0.596\n",
|
||||
" tv 128 2 0.517 0.5 0.828 0.712\n",
|
||||
" laptop 128 3 1 0 0.592 0.422\n",
|
||||
" mouse 128 2 1 0 0.0768 0.023\n",
|
||||
" remote 128 8 0.843 0.5 0.512 0.439\n",
|
||||
" cell phone 128 8 0.254 0.0636 0.109 0.0757\n",
|
||||
" microwave 128 3 0.459 1 0.995 0.798\n",
|
||||
" oven 128 5 0.436 0.4 0.391 0.31\n",
|
||||
" sink 128 6 0.265 0.167 0.198 0.124\n",
|
||||
" refrigerator 128 5 0.654 0.4 0.61 0.468\n",
|
||||
" book 128 29 0.466 0.0619 0.394 0.178\n",
|
||||
" clock 128 9 0.653 0.778 0.778 0.595\n",
|
||||
" vase 128 2 0.18 1 0.695 0.695\n",
|
||||
" scissors 128 1 1 0 0.124 0.0224\n",
|
||||
" teddy bear 128 21 0.756 0.429 0.573 0.341\n",
|
||||
" toothbrush 128 5 1 0.37 0.803 0.533\n",
|
||||
"Speed: 2.6ms pre-process, 5.2ms inference, 0.0ms loss, 3.9ms post-process per image\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ZY2VXXXu74w5"
|
||||
},
|
||||
"source": [
|
||||
"# 3. Train\n",
|
||||
"\n",
|
||||
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
|
||||
"\n",
|
||||
"Train YOLOv8 on detection, segmentation and classification datasets."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "1NcFxRcFdJ_O",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "a264b615-5f73-4b84-c708-61f15a7e5c03"
|
||||
},
|
||||
"source": [
|
||||
"# Train YOLOv8n on COCO128 for 3 epochs\n",
|
||||
"!yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, cache=False, device=, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, hydra={'output_subdir': None, 'run': {'dir': '.'}}, v5loader=False, save_dir=runs/detect/train\n",
|
||||
"Ultralytics YOLO 🚀 0.0.59 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"\n",
|
||||
" from n params module arguments \n",
|
||||
" 0 -1 1 464 ultralytics.nn.modules.Conv [3, 16, 3, 2] \n",
|
||||
" 1 -1 1 4672 ultralytics.nn.modules.Conv [16, 32, 3, 2] \n",
|
||||
" 2 -1 1 7360 ultralytics.nn.modules.C2f [32, 32, 1, True] \n",
|
||||
" 3 -1 1 18560 ultralytics.nn.modules.Conv [32, 64, 3, 2] \n",
|
||||
" 4 -1 2 49664 ultralytics.nn.modules.C2f [64, 64, 2, True] \n",
|
||||
" 5 -1 1 73984 ultralytics.nn.modules.Conv [64, 128, 3, 2] \n",
|
||||
" 6 -1 2 197632 ultralytics.nn.modules.C2f [128, 128, 2, True] \n",
|
||||
" 7 -1 1 295424 ultralytics.nn.modules.Conv [128, 256, 3, 2] \n",
|
||||
" 8 -1 1 460288 ultralytics.nn.modules.C2f [256, 256, 1, True] \n",
|
||||
" 9 -1 1 164608 ultralytics.nn.modules.SPPF [256, 256, 5] \n",
|
||||
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 12 -1 1 148224 ultralytics.nn.modules.C2f [384, 128, 1] \n",
|
||||
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 15 -1 1 37248 ultralytics.nn.modules.C2f [192, 64, 1] \n",
|
||||
" 16 -1 1 36992 ultralytics.nn.modules.Conv [64, 64, 3, 2] \n",
|
||||
" 17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 18 -1 1 123648 ultralytics.nn.modules.C2f [192, 128, 1] \n",
|
||||
" 19 -1 1 147712 ultralytics.nn.modules.Conv [128, 128, 3, 2] \n",
|
||||
" 20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 21 -1 1 493056 ultralytics.nn.modules.C2f [384, 256, 1] \n",
|
||||
" 22 [15, 18, 21] 1 897664 ultralytics.nn.modules.Detect [80, [64, 128, 256]] \n",
|
||||
"Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
|
||||
"\n",
|
||||
"Transferred 355/355 items from pretrained weights\n",
|
||||
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"Image sizes 640 train, 640 val\n",
|
||||
"Using 2 dataloader workers\n",
|
||||
"Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
|
||||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||||
" 1/3 4.31G 1.221 1.429 1.241 196 640: 100% 8/8 [00:08<00:00, 1.04s/it]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.48it/s]\n",
|
||||
" all 128 929 0.645 0.539 0.617 0.454\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||||
" 2/3 5.31G 1.186 1.306 1.255 287 640: 100% 8/8 [00:06<00:00, 1.17it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.37it/s]\n",
|
||||
" all 128 929 0.654 0.601 0.643 0.474\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||||
" 3/3 5.31G 1.17 1.408 1.267 189 640: 100% 8/8 [00:06<00:00, 1.18it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:04<00:00, 1.17s/it]\n",
|
||||
" all 128 929 0.636 0.619 0.648 0.479\n",
|
||||
"\n",
|
||||
"3 epochs completed in 0.013 hours.\n",
|
||||
"Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
|
||||
"Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
|
||||
"\n",
|
||||
"Validating runs/detect/train/weights/best.pt...\n",
|
||||
"Ultralytics YOLO 🚀 0.0.59 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
|
||||
"Fusing layers... \n",
|
||||
"Model summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:05<00:00, 1.42s/it]\n",
|
||||
" all 128 929 0.658 0.605 0.65 0.481\n",
|
||||
" person 128 254 0.764 0.717 0.777 0.547\n",
|
||||
" bicycle 128 6 0.4 0.167 0.333 0.192\n",
|
||||
" car 128 46 0.76 0.196 0.263 0.153\n",
|
||||
" motorcycle 128 5 0.711 0.987 0.938 0.786\n",
|
||||
" airplane 128 6 0.756 0.833 0.927 0.666\n",
|
||||
" bus 128 7 1 0.705 0.723 0.632\n",
|
||||
" train 128 3 0.708 1 0.913 0.742\n",
|
||||
" truck 128 12 0.733 0.417 0.452 0.308\n",
|
||||
" boat 128 6 0.453 0.421 0.502 0.317\n",
|
||||
" traffic light 128 14 0.528 0.143 0.164 0.137\n",
|
||||
" stop sign 128 2 0.597 0.5 0.828 0.663\n",
|
||||
" bench 128 9 0.953 0.556 0.627 0.371\n",
|
||||
" bird 128 16 0.697 0.875 0.948 0.622\n",
|
||||
" cat 128 4 0.856 1 0.995 0.834\n",
|
||||
" dog 128 9 0.641 0.889 0.855 0.624\n",
|
||||
" horse 128 2 0.732 1 0.995 0.597\n",
|
||||
" elephant 128 17 0.829 0.941 0.946 0.74\n",
|
||||
" bear 128 1 0.618 1 0.995 0.995\n",
|
||||
" zebra 128 4 0.843 1 0.995 0.972\n",
|
||||
" giraffe 128 9 0.879 1 0.995 0.642\n",
|
||||
" backpack 128 6 0.554 0.333 0.426 0.271\n",
|
||||
" umbrella 128 18 0.707 0.67 0.719 0.469\n",
|
||||
" handbag 128 19 0.892 0.158 0.262 0.101\n",
|
||||
" tie 128 7 0.682 0.714 0.665 0.447\n",
|
||||
" suitcase 128 4 0.505 1 0.995 0.594\n",
|
||||
" frisbee 128 5 0.629 0.8 0.732 0.672\n",
|
||||
" skis 128 1 0.876 1 0.995 0.497\n",
|
||||
" snowboard 128 7 0.504 0.571 0.68 0.511\n",
|
||||
" sports ball 128 6 0.576 0.5 0.514 0.263\n",
|
||||
" kite 128 10 0.657 0.5 0.603 0.24\n",
|
||||
" baseball bat 128 4 0.452 0.427 0.267 0.103\n",
|
||||
" baseball glove 128 7 0.531 0.429 0.43 0.327\n",
|
||||
" skateboard 128 5 0.869 0.6 0.6 0.415\n",
|
||||
" tennis racket 128 7 0.851 0.429 0.504 0.342\n",
|
||||
" bottle 128 18 0.574 0.389 0.395 0.236\n",
|
||||
" wine glass 128 16 0.892 0.517 0.664 0.402\n",
|
||||
" cup 128 36 0.719 0.361 0.446 0.326\n",
|
||||
" fork 128 6 0.542 0.167 0.183 0.179\n",
|
||||
" knife 128 16 0.837 0.562 0.698 0.386\n",
|
||||
" spoon 128 22 0.522 0.348 0.386 0.219\n",
|
||||
" bowl 128 28 0.681 0.685 0.676 0.55\n",
|
||||
" banana 128 1 0.0197 0.0985 0.199 0.039\n",
|
||||
" sandwich 128 2 0.457 1 0.995 0.995\n",
|
||||
" orange 128 4 0.75 0.752 0.845 0.627\n",
|
||||
" broccoli 128 11 0.274 0.182 0.262 0.209\n",
|
||||
" carrot 128 24 0.625 0.667 0.667 0.443\n",
|
||||
" hot dog 128 2 0.494 1 0.828 0.796\n",
|
||||
" pizza 128 5 0.737 1 0.995 0.866\n",
|
||||
" donut 128 14 0.599 1 0.95 0.891\n",
|
||||
" cake 128 4 0.781 1 0.995 0.877\n",
|
||||
" chair 128 35 0.385 0.571 0.488 0.264\n",
|
||||
" couch 128 6 0.661 0.652 0.798 0.624\n",
|
||||
" potted plant 128 14 0.819 0.571 0.682 0.506\n",
|
||||
" bed 128 3 0.866 1 0.995 0.611\n",
|
||||
" dining table 128 13 0.454 0.615 0.591 0.451\n",
|
||||
" toilet 128 2 0.524 0.5 0.62 0.608\n",
|
||||
" tv 128 2 0.545 0.634 0.828 0.762\n",
|
||||
" laptop 128 3 1 0 0.708 0.562\n",
|
||||
" mouse 128 2 1 0 0.0681 0.0272\n",
|
||||
" remote 128 8 0.849 0.5 0.525 0.432\n",
|
||||
" cell phone 128 8 0.491 0.125 0.118 0.0949\n",
|
||||
" microwave 128 3 0.379 1 0.913 0.729\n",
|
||||
" oven 128 5 0.5 0.6 0.462 0.361\n",
|
||||
" sink 128 6 0.285 0.167 0.226 0.14\n",
|
||||
" refrigerator 128 5 0.552 0.505 0.724 0.544\n",
|
||||
" book 128 29 0.651 0.258 0.436 0.212\n",
|
||||
" clock 128 9 0.666 0.778 0.886 0.72\n",
|
||||
" vase 128 2 0.241 1 0.695 0.695\n",
|
||||
" scissors 128 1 1 0 0.0995 0.00995\n",
|
||||
" teddy bear 128 21 0.631 0.619 0.629 0.379\n",
|
||||
" toothbrush 128 5 1 0.687 0.92 0.614\n",
|
||||
"Speed: 3.4ms pre-process, 4.6ms inference, 0.0ms loss, 3.6ms post-process per image\n",
|
||||
"Saving runs/detect/train/predictions.json...\n",
|
||||
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# 4. Export\n",
|
||||
"\n",
|
||||
"Export a YOLOv8 model to any format.\n",
|
||||
"\n",
|
||||
"Format | `format=...**bold text**` | Model\n",
|
||||
"--- | --- | ---\n",
|
||||
"PyTorch | - | yolov8n.pt\n",
|
||||
"TorchScript | `torchscript` | yolov8n.torchscript\n",
|
||||
"ONNX | `onnx` | yolov8n.onnx\n",
|
||||
"OpenVINO | `openvino` | yolov8n_openvino_model/\n",
|
||||
"TensorRT | `engine` | yolov8n.engine\n",
|
||||
"CoreML | `coreml` | yolov8n.mlmodel\n",
|
||||
"TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/\n",
|
||||
"TensorFlow GraphDef | `pb` | yolov8n.pb\n",
|
||||
"TensorFlow Lite | `tflite` | yolov8n.tflite\n",
|
||||
"TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite\n",
|
||||
"TensorFlow.js | `tfjs` | yolov8n_web_model/\n",
|
||||
"PaddlePaddle | `paddle` | yolov8n_paddle_model/"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "nPZZeNrLCQG6"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"!yolo mode=export model=yolov8n.pt format=torchscript"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "CYIjW4igCjqD",
|
||||
"outputId": "dd9e5aa6-8538-4403-c9d7-1548e8c4c39e"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Fusing layers... \n",
|
||||
"YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
"Ultralytics YOLO 🚀 0.0.59 Python-3.8.16 torch-1.13.0+cu116 CPU\n",
|
||||
"Fusing layers... \n",
|
||||
"YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from yolov8n.pt with output shape (1, 84, 8400) (6.2 MB)\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 1.13.0+cu116...\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 1.9s, saved as yolov8n.torchscript (12.4 MB)\n",
|
||||
"\n",
|
||||
"Export complete (3.2s)\n",
|
||||
"Results saved to \u001b[1m/content\u001b[0m\n",
|
||||
"Predict: yolo task=detect mode=predict model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
|
||||
"Validate: yolo task=detect mode=val model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
|
||||
"Visualize: https://netron.app\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# 5. Python Usage\n",
|
||||
"\n",
|
||||
"YOLOv8 was reimagined using python-first principles for the most seamless python YOLO experience ever."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "kUMOQ0OeDBJG"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"# Load a model\n",
|
||||
"model = YOLO('yolov8n.yaml') # build a new model from scratch\n",
|
||||
"model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)\n",
|
||||
"\n",
|
||||
"# Model usage\n",
|
||||
"results = model.train(data='coco128.yaml', epochs=3) # train the model\n",
|
||||
"results = model.val(data='coco128.yaml') # evaluate model performance on the validation set\n",
|
||||
"results = model.predict(source='https://ultralytics.com/images/bus.jpg') # predict on an image\n",
|
||||
"success = model.export(format='onnx') # export the model to ONNX format"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "bpF9-vS_DAaf"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# 6. Tasks\n",
|
||||
"\n",
|
||||
"YOLOv8 can train, val, predict and export models for the 3 primary tasks in vision AI: detection, segmentation and classification."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Phm9ccmOKye5"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## 1. Detection\n",
|
||||
"\n",
|
||||
"Detection models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt`"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "yq26lwpYK1lq"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Load a pretraind YOLOv8n detection model, train it on COCO128 for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n.pt') # load a pretrained YOLOv8n detection model\n",
|
||||
"model.train(data='coco128.yaml', epochs=3) # train the model\n",
|
||||
"model.predict(source='https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "8Go5qqS9LbC5"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## 2. Segmentation\n",
|
||||
"\n",
|
||||
"Segmentation models use the `-seg` suffix, i.e. `yolov8n-seg.pt`"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "7ZW58jUzK66B"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Load a pretraind YOLOv8n segmentation model, train it on COCO128-seg for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n-seg.pt') # load a pretrained YOLOv8n segmentation model\n",
|
||||
"model.train(data='coco128-seg.yaml', epochs=3) # train the model\n",
|
||||
"model.predict(source='https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "WFPJIQl_L5HT"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## 3. Classification\n",
|
||||
"\n",
|
||||
"Classification models use the `-cls` suffix, i.e. `yolov8n-cls.pt`"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ax3p94VNK9zR"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# Load a pretraind YOLOv8n classification model, train it on imagenette160 for 3 epochs and predict an image with it\n",
|
||||
"from ultralytics import YOLO\n",
|
||||
"\n",
|
||||
"model = YOLO('yolov8n-cls.pt') # load a pretrained YOLOv8n classification model\n",
|
||||
"model.train(data='imagenette160', epochs=3) # train the model\n",
|
||||
"model.predict(source='https://ultralytics.com/images/bus.jpg') # predict on an image"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "5q9Zu6zlL5rS"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "IEijrePND_2I"
|
||||
},
|
||||
"source": [
|
||||
"# Appendix\n",
|
||||
"\n",
|
||||
"Additional content below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "GMusP4OAxFu6"
|
||||
},
|
||||
"source": [
|
||||
"# Run YOLOv8 tests (git clone install only)\n",
|
||||
"!pytest ultralytics/tests"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -1,6 +1,9 @@
|
||||
__version__ = "8.0.0.dev0"
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
__version__ = "8.0.0"
|
||||
|
||||
from ultralytics.hub import checks
|
||||
from ultralytics.yolo.engine.model import YOLO
|
||||
from ultralytics.yolo.utils import ops
|
||||
|
||||
__all__ = ["__version__", "YOLO", "hub"] # allow simpler import
|
||||
__all__ = ["__version__", "YOLO", "hub", "checks"] # allow simpler import
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import os
|
||||
import shutil
|
||||
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import requests
|
||||
|
||||
from ultralytics.hub.config import HUB_API_ROOT
|
||||
from ultralytics.hub.utils import request_with_credentials
|
||||
from ultralytics.hub.utils import HUB_API_ROOT, request_with_credentials
|
||||
from ultralytics.yolo.utils import is_colab
|
||||
|
||||
API_KEY_PATH = "https://hub.ultralytics.com/settings?tab=api+keys"
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
import os
|
||||
|
||||
# Global variables
|
||||
REPO_URL = "https://github.com/ultralytics/yolov5.git"
|
||||
REPO_BRANCH = "ultralytics/HUB" # "master"
|
||||
|
||||
ENVIRONMENT = os.environ.get("ULTRALYTICS_ENV", "production")
|
||||
if ENVIRONMENT == 'production':
|
||||
HUB_API_ROOT = "https://api.ultralytics.com"
|
||||
else:
|
||||
HUB_API_ROOT = "http://127.0.0.1:8000"
|
||||
print(f'Connected to development server on {HUB_API_ROOT}')
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import signal
|
||||
import sys
|
||||
from pathlib import Path
|
||||
@ -6,8 +8,7 @@ from time import sleep
|
||||
import requests
|
||||
|
||||
from ultralytics import __version__
|
||||
from ultralytics.hub.config import HUB_API_ROOT
|
||||
from ultralytics.hub.utils import check_dataset_disk_space, smart_request
|
||||
from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_request
|
||||
from ultralytics.yolo.utils import LOGGER, is_colab, threaded
|
||||
|
||||
AGENT_NAME = f'python-{__version__}-colab' if is_colab() else f'python-{__version__}-local'
|
||||
|
||||
@ -1,14 +1,17 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import os
|
||||
import shutil
|
||||
import threading
|
||||
import time
|
||||
|
||||
import requests
|
||||
|
||||
from ultralytics.hub.config import HUB_API_ROOT
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG_DICT, LOGGER, RANK, SETTINGS, colorstr, emojis
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG_DICT, LOGGER, RANK, SETTINGS, TryExcept, colorstr, emojis
|
||||
|
||||
PREFIX = colorstr('Ultralytics: ')
|
||||
HELP_MSG = 'If this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.'
|
||||
HUB_API_ROOT = os.environ.get("ULTRALYTICS_HUB_API", "https://api.ultralytics.com")
|
||||
|
||||
|
||||
def check_dataset_disk_space(url='https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', sf=2.0):
|
||||
@ -90,7 +93,6 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
|
||||
requests.Response: The HTTP response object. If the request is executed in a separate thread, returns None.
|
||||
"""
|
||||
retry_codes = (408, 500) # retry only these codes
|
||||
methods = {'post': requests.post, 'get': requests.get} # request methods
|
||||
|
||||
def func(*func_args, **func_kwargs):
|
||||
r = None # response
|
||||
@ -98,7 +100,10 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
|
||||
for i in range(retry + 1):
|
||||
if (time.time() - t0) > timeout:
|
||||
break
|
||||
r = methods[method](*func_args, **func_kwargs) # i.e. post(url, data, json, files)
|
||||
if method == 'post':
|
||||
r = requests.post(*func_args, **func_kwargs) # i.e. post(url, data, json, files)
|
||||
elif method == 'get':
|
||||
r = requests.get(*func_args, **func_kwargs) # i.e. get(url, data, json, files)
|
||||
if r.status_code == 200:
|
||||
break
|
||||
try:
|
||||
@ -125,7 +130,8 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
|
||||
return func(*args, **kwargs)
|
||||
|
||||
|
||||
def sync_analytics(cfg, all_keys=False, enabled=False):
|
||||
@TryExcept()
|
||||
def sync_analytics(cfg, all_keys=False, enabled=True):
|
||||
"""
|
||||
Sync analytics data if enabled in the global settings
|
||||
|
||||
@ -137,8 +143,8 @@ def sync_analytics(cfg, all_keys=False, enabled=False):
|
||||
if SETTINGS['sync'] and RANK in {-1, 0} and enabled:
|
||||
cfg = dict(cfg) # convert type from DictConfig to dict
|
||||
if not all_keys:
|
||||
cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CONFIG_DICT[k]} # retain only non-default values
|
||||
cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CONFIG_DICT.get(k, None)} # retain non-default values
|
||||
cfg['uuid'] = SETTINGS['uuid'] # add the device UUID to the configuration data
|
||||
|
||||
# Send a request to the HUB API to sync the analytics data
|
||||
smart_request(f'{HUB_API_ROOT}/v1/usage/anonymous', data=cfg, headers=None, code=3, retry=0, verbose=False)
|
||||
# Send a request to the HUB API to sync analytics
|
||||
smart_request(f'{HUB_API_ROOT}/v1/usage/anonymous', json=cfg, headers=None, code=3, retry=0, verbose=False)
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import json
|
||||
import platform
|
||||
from collections import OrderedDict, namedtuple
|
||||
@ -65,6 +67,7 @@ class AutoBackend(nn.Module):
|
||||
names = model.module.names if hasattr(model, 'module') else model.names # get class names
|
||||
model.half() if fp16 else model.float()
|
||||
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
|
||||
pt = True
|
||||
elif pt: # PyTorch
|
||||
from ultralytics.nn.tasks import attempt_load_weights
|
||||
model = attempt_load_weights(weights if isinstance(weights, list) else w,
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Common modules
|
||||
"""
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
@ -1 +1,3 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from . import v8
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Dict, Union
|
||||
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLO 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# Default training settings and hyperparameters for medium-augmentation COCO training
|
||||
|
||||
task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import sys
|
||||
from difflib import get_close_matches
|
||||
from textwrap import dedent
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from .base import BaseDataset
|
||||
from .build import build_classification_dataloader, build_dataloader
|
||||
from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import math
|
||||
import random
|
||||
from copy import deepcopy
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import glob
|
||||
import math
|
||||
import os
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import os
|
||||
import random
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import glob
|
||||
import math
|
||||
import os
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Image augmentation functions
|
||||
"""
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Dataloaders and dataset utils
|
||||
"""
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from itertools import repeat
|
||||
from multiprocessing.pool import Pool
|
||||
from pathlib import Path
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import collections
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# COCO 2017 dataset http://cocodataset.org by Microsoft
|
||||
# Example usage: python train.py --data coco.yaml
|
||||
# parent
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Example usage: python train.py --data coco128.yaml
|
||||
# parent
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# Example usage: python train.py --data coco128.yaml
|
||||
# parent
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# Download latest models from https://github.com/ultralytics/yolov5/releases
|
||||
# Example usage: bash data/scripts/download_weights.sh
|
||||
# parent
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# Download COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: bash data/scripts/get_coco.sh
|
||||
# parent
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: bash data/scripts/get_coco128.sh
|
||||
# parent
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
#!/bin/bash
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
# Download ILSVRC2012 ImageNet dataset https://image-net.org
|
||||
# Example usage: bash data/scripts/get_imagenet.sh
|
||||
# parent
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import hashlib
|
||||
import os
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics import yolo # noqa
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# predictor engine by Ultralytics
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
Usage - sources:
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Simple training loop; Boilerplate that could apply to any arbitrary neural network,
|
||||
"""
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import json
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
@ -86,6 +88,7 @@ class BaseValidator:
|
||||
self.model = model
|
||||
self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device)
|
||||
self.args.plots = trainer.epoch == trainer.epochs - 1 # always plot final epoch
|
||||
model.eval()
|
||||
else:
|
||||
callbacks.add_integration_callbacks(self)
|
||||
self.run_callbacks('on_val_start')
|
||||
@ -106,17 +109,17 @@ class BaseValidator:
|
||||
f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
|
||||
|
||||
if isinstance(self.args.data, str) and self.args.data.endswith(".yaml"):
|
||||
data = check_dataset_yaml(self.args.data)
|
||||
self.data = check_dataset_yaml(self.args.data)
|
||||
else:
|
||||
data = check_dataset(self.args.data)
|
||||
self.data = check_dataset(self.args.data)
|
||||
|
||||
if self.device.type == 'cpu':
|
||||
self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading
|
||||
self.dataloader = self.dataloader or \
|
||||
self.get_dataloader(data.get("val") or data.set("test"), self.args.batch)
|
||||
self.data = data
|
||||
self.get_dataloader(self.data.get("val") or self.data.set("test"), self.args.batch)
|
||||
|
||||
model.eval()
|
||||
model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup
|
||||
|
||||
dt = Profile(), Profile(), Profile(), Profile()
|
||||
n_batches = len(self.dataloader)
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import inspect
|
||||
import logging.config
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Auto-batch utils
|
||||
"""
|
||||
|
||||
@ -1,4 +1,7 @@
|
||||
# Ultralytics YOLO base callbacks
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Base callbacks
|
||||
"""
|
||||
|
||||
|
||||
# Trainer callbacks ----------------------------------------------------------------------------------------------------
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
|
||||
|
||||
try:
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
|
||||
|
||||
try:
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import json
|
||||
from time import time
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
writer = None # TensorBoard SummaryWriter instance
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
|
||||
|
||||
try:
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import glob
|
||||
import inspect
|
||||
import math
|
||||
@ -62,8 +64,7 @@ def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
|
||||
LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {stride}, updating to {sz}')
|
||||
|
||||
# Add missing dimensions if necessary
|
||||
if min_dim == 2 and len(sz) == 1:
|
||||
sz = [sz[0], sz[0]]
|
||||
sz = [sz[0], sz[0]] if min_dim == 2 and len(sz) == 1 else sz[0] if min_dim == 1 and len(sz) == 1 else sz
|
||||
|
||||
return sz
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import os
|
||||
import shutil
|
||||
import socket
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import glob
|
||||
import os
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from collections import abc
|
||||
from itertools import repeat
|
||||
from numbers import Number
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
"""
|
||||
Model validation metrics
|
||||
"""
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import math
|
||||
import re
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import contextlib
|
||||
import math
|
||||
from pathlib import Path
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
@ -59,7 +61,7 @@ def DDP_model(model):
|
||||
def select_device(device='', batch_size=0, newline=False):
|
||||
# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
|
||||
ver = git_describe() or ultralytics.__version__ # git commit or pip package version
|
||||
s = f'Ultralytics YOLO 🚀 {ver} Python-{platform.python_version()} torch-{torch.__version__} '
|
||||
s = f'Ultralytics YOLOv{ver} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
|
||||
device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
|
||||
cpu = device == 'cpu'
|
||||
mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from ultralytics.yolo.v8 import classify, detect, segment
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from ultralytics.yolo.v8.classify.predict import ClassificationPredictor, predict
|
||||
from ultralytics.yolo.v8.classify.train import ClassificationTrainer, train
|
||||
from ultralytics.yolo.v8.classify.val import ClassificationValidator, val
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import hydra
|
||||
import torch
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import hydra
|
||||
import torch
|
||||
import torchvision
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import hydra
|
||||
|
||||
from ultralytics.yolo.data import build_classification_dataloader
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from .predict import DetectionPredictor, predict
|
||||
from .train import DetectionTrainer, train
|
||||
from .val import DetectionValidator, val
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import hydra
|
||||
import torch
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from copy import copy
|
||||
|
||||
import hydra
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from .predict import SegmentationPredictor, predict
|
||||
from .train import SegmentationTrainer, train
|
||||
from .val import SegmentationValidator, val
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import hydra
|
||||
import torch
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
from copy import copy
|
||||
|
||||
import hydra
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# Ultralytics YOLO 🚀, GPL-3.0 license
|
||||
|
||||
import os
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user