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Add Dockerfiles and update Docs README (#124)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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.github/workflows/docker.yaml vendored
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Builds ultralytics/ultralytics:latest images on DockerHub https://hub.docker.com/r/ultralytics
name: Publish Docker Images
on:
push:
branches: [ none ] # TODO: replace with main
jobs:
docker:
if: github.repository == 'ultralytics/ultralytics'
name: Push Docker image to Docker Hub
runs-on: ubuntu-latest
steps:
- name: Checkout repo
uses: actions/checkout@v3
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push arm64 image
uses: docker/build-push-action@v3
continue-on-error: true
with:
context: .
platforms: linux/arm64
file: docker/Dockerfile-arm64
push: true
tags: ultralytics/ultralytics:latest-arm64
- name: Build and push CPU image
uses: docker/build-push-action@v3
continue-on-error: true
with:
context: .
file: docker/Dockerfile-cpu
push: true
tags: ultralytics/ultralytics:latest-cpu
- name: Build and push GPU image
uses: docker/build-push-action@v3
continue-on-error: true
with:
context: .
file: docker/Dockerfile
push: true
tags: ultralytics/ultralytics:latest

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.pre-commit-config.yaml
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additional_dependencies:
- mdformat-gfm
- mdformat-black
exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
# exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4

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README.md
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```bash
pip install ultralytics
```
Development
```
git clone https://github.com/ultralytics/ultralytics
cd ultralytics
@ -13,25 +15,34 @@ pip install -e .
```
## Usage
### 1. CLI
To simply use the latest Ultralytics YOLO models
```bash
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
```python
from ultralytics import YOLO
model = YOLO.new('yolov8n.yaml') # create a new model from scratch
model = YOLO.load('yolov8n.pt') # load a pretrained model (recommended for best training results)
model = YOLO.new("yolov8n.yaml") # create a new model from scratch
model = YOLO.load(
"yolov8n.pt"
) # load a pretrained model (recommended for best training results)
results = model.train(data='coco128.yaml', epochs=100, imgsz=640, ...)
results = model.train(data="coco128.yaml", epochs=100, imgsz=640, ...)
results = model.val()
results = model.predict(source='bus.jpg')
success = model.export(format='onnx')
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.
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.

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docker/Dockerfile
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
FROM nvcr.io/nvidia/pytorch:22.12-py3
RUN rm -rf /opt/pytorch # remove 1.2GB dir
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
RUN apt update && apt install --no-install-recommends -y zip htop screen libgl1-mesa-glx
# Create working directory
RUN mkdir -p /usr/src/ultralytics
WORKDIR /usr/src/ultralytics
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
# Install pip packages
RUN python -m pip install --upgrade pip wheel
RUN pip uninstall -y Pillow torchtext # torch torchvision
RUN pip install --no-cache ultralytics albumentations comet gsutil notebook Pillow>=9.1.0 \
'opencv-python<4.6.0.66' \
--extra-index-url https://download.pytorch.org/whl/cu113
# Set environment variables
ENV OMP_NUM_THREADS=1
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/ultralytics:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
# Pull and Run with local directory access
# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
# Kill all
# sudo docker kill $(sudo docker ps -q)
# Kill all image-based
# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/ultralytics:latest)
# DockerHub tag update
# t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
# Clean up
# docker system prune -a --volumes
# Update Ubuntu drivers
# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
# DDP test
# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
# GCP VM from Image
# docker.io/ultralytics/ultralytics:latest

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docker/Dockerfile-arm64
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM arm64v8/ubuntu:20.04
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
ENV DEBIAN_FRONTEND noninteractive
RUN apt update
RUN TZ=Etc/UTC apt install -y tzdata
RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev
# RUN alias python=python3
# Create working directory
RUN mkdir -p /usr/src/ultralytics
WORKDIR /usr/src/ultralytics
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache ultralytics gsutil notebook \
tensorflow-aarch64
# tensorflowjs \
# onnx onnx-simplifier onnxruntime \
# coremltools openvino-dev \
# Cleanup
ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

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docker/Dockerfile-cpu
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM ubuntu:20.04
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
ENV DEBIAN_FRONTEND noninteractive
RUN apt update
RUN TZ=Etc/UTC apt install -y tzdata
RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
# RUN alias python=python3
# Create working directory
RUN mkdir -p /usr/src/ultralytics
WORKDIR /usr/src/ultralytics
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache ultralytics albumentations gsutil notebook \
coremltools onnx onnx-simplifier onnxruntime tensorflow-cpu tensorflowjs \
# openvino-dev \
--extra-index-url https://download.pytorch.org/whl/cpu
# Cleanup
ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

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docs/README.md
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## To serve docs
* Install ultralytics repo in Dev mode:
# Ultralytics Docs
Deployed to https://docs.ultralytics.com
### Install Ultralytics package
To install the ultralytics package in developer mode, you will need to have Git and Python 3 installed on your system.
Then, follow these steps:
1. Clone the ultralytics repository to your local machine using Git:
```bash
git clone https://github.com/ultralytics/ultralytics.git
```
2. Navigate to the root directory of the repository:
```bash
cd ultralytics
```
3. Install the package in developer mode using pip:
```bash
pip install -e '.[dev]'
```
* Run `mkdocs serve`
This will install the ultralytics package and its dependencies in developer mode, allowing you to make changes to the
package code and have them reflected immediately in your Python environment.
Note that you may need to use the pip3 command instead of pip if you have multiple versions of Python installed on your
system.
### Building and Serving Locally
The `mkdocs serve` command is used to build and serve a local version of the MkDocs documentation site. It is typically
used during the development and testing phase of a documentation project.
```bash
mkdocs serve
```
Here is a breakdown of what this command does:
- `mkdocs`: This is the command-line interface (CLI) for the MkDocs static site generator. It is used to build and serve
MkDocs sites.
- `serve`: This is a subcommand of the `mkdocs` CLI that tells it to build and serve the documentation site locally.
- `-a`: This flag specifies the hostname and port number to bind the server to. The default value is `localhost:8000`.
- `-t`: This flag specifies the theme to use for the documentation site. The default value is `mkdocs`.
- `-s`: This flag tells the `serve` command to serve the site in silent mode, which means it will not display any log
messages or progress updates.
When you run the `mkdocs serve` command, it will build the documentation site using the files in the `docs/` directory
and serve it at the specified hostname and port number. You can then view the site by going to the URL in your web
browser.
While the site is being served, you can make changes to the documentation files and see them reflected in the live site
immediately. This is useful for testing and debugging your documentation before deploying it to a live server.
To stop the serve command and terminate the local server, you can use the `CTRL+C` keyboard shortcut.
### Deploying Your Documentation Site
To deploy your MkDocs documentation site, you will need to choose a hosting provider and a deployment method. Some
popular options include GitHub Pages, GitLab Pages, and Amazon S3.
Before you can deploy your site, you will need to configure your `mkdocs.yml` file to specify the remote host and any
other necessary deployment settings.
Once you have configured your `mkdocs.yml` file, you can use the `mkdocs deploy` command to build and deploy your site.
This command will build the documentation site using the files in the `docs/` directory and the specified configuration
file and theme, and then deploy the site to the specified remote host.
For example, to deploy your site to GitHub Pages using the gh-deploy plugin, you can use the following command:
```bash
mkdocs gh-deploy
```
If you are using GitHub Pages, you can set a custom domain for your documentation site by going to the "Settings" page
for your repository and updating the "Custom domain" field in the "GitHub Pages" section.
![196814117-fc16e711-d2be-4722-9536-b7c6d78fd167](https://user-images.githubusercontent.com/26833433/210150206-9e86dcd7-10af-43e4-9eb2-9518b3799eac.png)
For more information on deploying your MkDocs documentation site, see
the [MkDocs documentation](https://www.mkdocs.org/user-guide/deploying-your-docs/).

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## Ultralytics YOLO
Default training settings and hyperparameters for medium-augmentation COCO training
### Setting the operation type
???+ note "Operation"
| Key | Value | Description |
|--------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify`.<br> - `init` is a special case that creates a copy of default.yaml configs to the current working dir |
| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict` |
| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name`|
### Training settings
??? note "Train"
| Key | Value | Description |
|------------------|--------|---------------------------------------------------------------------------------|
| device | '' | cuda device, i.e. 0 or 0,1,2,3 or cpu. `''` selects available cuda 0 device |
| epochs | 100 | Number of epochs to train |
| workers | 8 | Number of cpu workers used per process. Scales automatically with DDP |
| batch_size | 16 | Batch size of the dataloader |
| imgsz | 640 | Image size of data in dataloader |
| optimizer | SGD | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp` |
| single_cls | False | Train on multi-class data as single-class |
| image_weights | False | Use weighted image selection for training |
| rect | False | Enable rectangular training |
| cos_lr | False | Use cosine LR scheduler |
| lr0 | 0.01 | Initial learning rate |
| lrf | 0.01 | Final OneCycleLR learning rate |
| momentum | 0.937 | Use as `momentum` for SGD and `beta1` for Adam |
| weight_decay | 0.0005 | Optimizer weight decay |
| warmup_epochs | 3.0 | Warmup epochs. Fractions are ok. |
| warmup_momentum | 0.8 | Warmup initial momentum |
| warmup_bias_lr | 0.1 | Warmup initial bias lr |
| box | 0.05 | Box loss gain |
| cls | 0.5 | cls loss gain |
| cls_pw | 1.0 | cls BCELoss positive_weight |
| obj | 1.0 | bj loss gain (scale with pixels) |
| obj_pw | 1.0 | obj BCELoss positive_weight |
| iou_t | 0.20 | IOU training threshold |
| anchor_t | 4.0 | anchor-multiple threshold |
| fl_gamma | 0.0 | focal loss gamma |
| label_smoothing | 0.0 | |
| nbs | 64 | nominal batch size |
| overlap_mask | `True` | **Segmentation**: Use mask overlapping during training |
| mask_ratio | 4 | **Segmentation**: Set mask downsampling |
| dropout | `False`| **Classification**: Use dropout while training |
### Prediction Settings
??? note "Prediction"
| Key | Value | Description |
|----------------|----------------------|----------------------------------------------------|
| source | `ultralytics/assets` | Input source. Accepts image, folder, video, url |
| view_img | `False` | View the prediction images |
| save_txt | `False` | Save the results in a txt file |
| save_conf | `False` | Save the condidence scores |
| save_crop | `Fasle` | |
| hide_labels | `False` | Hide the labels |
| hide_conf | `False` | Hide the confidence scores |
| vid_stride | `False` | Input video frame-rate stride |
| line_thickness | `3` | Bounding-box thickness (pixels) |
| visualize | `False` | Visualize model features |
| augment | `False` | Augmented inference |
| agnostic_nms | `False` | Class-agnostic NMS |
| retina_masks | `False` | **Segmentation:** High resolution masks |
### Validation settings
??? note "Validation"
| Key | Value | Description |
|-------------|---------|-----------------------------------|
| noval | `False` | ??? |
| save_json | `False` | |
| save_hybrid | `False` | |
| conf_thres | `0.001` | Confidence threshold |
| iou_thres | `0.6` | IoU threshold |
| max_det | `300` | Maximum number of detections |
| half | `True` | Use .half() mode. |
| dnn | `False` | Use OpenCV DNN for ONNX inference |
| plots | `False` | |
### Augmentation settings
??? note "Augmentation"
| hsv_h | 0.015 | Image HSV-Hue augmentation (fraction) |
|-------------|-------|-------------------------------------------------|
| hsv_s | 0.7 | Image HSV-Saturation augmentation (fraction) |
| hsv_v | 0.4 | Image HSV-Value augmentation (fraction) |
| degrees | 0.0 | Image rotation (+/- deg) |
| translate | 0.1 | Image translation (+/- fraction) |
| scale | 0.5 | Image scale (+/- gain) |
| shear | 0.0 | Image shear (+/- deg) |
| perspective | 0.0 | Image perspective (+/- fraction), range 0-0.001 |
| flipud | 0.0 | Image flip up-down (probability) |
| fliplr | 0.5 | Image flip left-right (probability) |
| mosaic | 1.0 | Image mosaic (probability) |
| mixup | 0.0 | Image mixup (probability) |
| copy_paste | 0.0 | Segment copy-paste (probability) |
### Logging, checkpoints, plotting and file management
??? note "files"
| Key | Value | Description |
|-----------|---------|---------------------------------------------------------------------------------------------|
| project: | 'runs' | The project name |
| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
| exist_ok: | `False` | ??? |
| plots | `False` | **Validation**: Save plots while validation |
| nosave | `False` | Don't save any plots, models or files |

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YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
and hyperparameters can affect the model's behavior at various stages of the model development process, including
training, validation, and prediction.
Properly setting and tuning these parameters can have a significant impact on the model's ability to learn effectively
from the training data and generalize to new data. For example, choosing an appropriate learning rate, batch size, and
optimization algorithm can greatly affect the model's convergence speed and accuracy. Similarly, setting the correct
confidence threshold and non-maximum suppression (NMS) threshold can affect the model's performance on detection tasks.
It is important to carefully consider and experiment with these settings and hyperparameters to achieve the best
possible performance for a given task. This can involve trial and error, as well as using techniques such as
hyperparameter optimization to search for the optimal set of parameters.
In summary, YOLO settings and hyperparameters are a key factor in the success of a YOLO model, and it is important to
pay careful attention to them to achieve the desired results.
### Setting the operation type
YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks
differ in the type of output they produce and the specific problem they are designed to solve.
- Detection: Detection tasks involve identifying and localizing objects or regions of interest in an image or video.
YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an
image.
- Segmentation: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to
different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for
each pixel in an image.
- Classification: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO
models can be used for image classification tasks by predicting the class label of an input image.
YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
include train, val, and predict.
- Train: The train mode is used to train the model on a dataset. This mode is typically used during the development and
testing phase of a model.
- Val: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used to
tune the model's hyperparameters and detect overfitting.
- Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in
production or when deploying the model to users.
| Key | Value | Description |
|--------|----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify`.<br> - `init` is a special case that creates a copy of default.yaml configs to the current working dir |
| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict` |
| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
### Training settings
Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a
dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings
include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process
include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It
is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
task.
| Key | Value | Description |
|-----------------|---------|-----------------------------------------------------------------------------|
| device | '' | cuda device, i.e. 0 or 0,1,2,3 or cpu. `''` selects available cuda 0 device |
| epochs | 100 | Number of epochs to train |
| workers | 8 | Number of cpu workers used per process. Scales automatically with DDP |
| batch_size | 16 | Batch size of the dataloader |
| imgsz | 640 | Image size of data in dataloader |
| optimizer | SGD | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp` |
| single_cls | False | Train on multi-class data as single-class |
| image_weights | False | Use weighted image selection for training |
| rect | False | Enable rectangular training |
| cos_lr | False | Use cosine LR scheduler |
| lr0 | 0.01 | Initial learning rate |
| lrf | 0.01 | Final OneCycleLR learning rate |
| momentum | 0.937 | Use as `momentum` for SGD and `beta1` for Adam |
| weight_decay | 0.0005 | Optimizer weight decay |
| warmup_epochs | 3.0 | Warmup epochs. Fractions are ok. |
| warmup_momentum | 0.8 | Warmup initial momentum |
| warmup_bias_lr | 0.1 | Warmup initial bias lr |
| box | 0.05 | Box loss gain |
| cls | 0.5 | cls loss gain |
| cls_pw | 1.0 | cls BCELoss positive_weight |
| obj | 1.0 | bj loss gain (scale with pixels) |
| obj_pw | 1.0 | obj BCELoss positive_weight |
| iou_t | 0.20 | IOU training threshold |
| anchor_t | 4.0 | anchor-multiple threshold |
| fl_gamma | 0.0 | focal loss gamma |
| label_smoothing | 0.0 | |
| nbs | 64 | nominal batch size |
| overlap_mask | `True` | **Segmentation**: Use mask overlapping during training |
| mask_ratio | 4 | **Segmentation**: Set mask downsampling |
| dropout | `False` | **Classification**: Use dropout while training |
### Prediction Settings
Prediction settings for YOLO models refer to the various hyperparameters and configurations used to make predictions
with the model on new data. These settings can affect the model's performance, speed, and accuracy. Some common YOLO
prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes
to consider. Other factors that may affect the prediction process include the size and format of the input data, the
presence of additional features such as masks or multiple labels per box, and the specific task the model is being used
for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a
given task.
| Key | Value | Description |
|----------------|----------------------|-------------------------------------------------|
| source | `ultralytics/assets` | Input source. Accepts image, folder, video, url |
| view_img | `False` | View the prediction images |
| save_txt | `False` | Save the results in a txt file |
| save_conf | `False` | Save the condidence scores |
| save_crop | `Fasle` | |
| hide_labels | `False` | Hide the labels |
| hide_conf | `False` | Hide the confidence scores |
| vid_stride | `False` | Input video frame-rate stride |
| line_thickness | `3` | Bounding-box thickness (pixels) |
| visualize | `False` | Visualize model features |
| augment | `False` | Augmented inference |
| agnostic_nms | `False` | Class-agnostic NMS |
| retina_masks | `False` | **Segmentation:** High resolution masks |
### Validation settings
Validation settings for YOLO models refer to the various hyperparameters and configurations used to
evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
process include the size and composition of the validation dataset and the specific task the model is being used for. It
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
validation dataset and to detect and prevent overfitting.
| Key | Value | Description |
|-------------|---------|-----------------------------------|
| noval | `False` | ??? |
| save_json | `False` | |
| save_hybrid | `False` | |
| conf_thres | `0.001` | Confidence threshold |
| iou_thres | `0.6` | IoU threshold |
| max_det | `300` | Maximum number of detections |
| half | `True` | Use .half() mode. |
| dnn | `False` | Use OpenCV DNN for ONNX inference |
| plots | `False` | |
### Export settings
Export settings for YOLO models refer to the various configurations and options used to save or
export the model for use in other environments or platforms. These settings can affect the model's performance, size,
and compatibility with different systems. Some common YOLO export settings include the format of the exported model
file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of
additional features such as masks or multiple labels per box. Other factors that may affect the export process include
the specific task the model is being used for and the requirements or constraints of the target environment or platform.
It is important to carefully consider and configure these settings to ensure that the exported model is optimized for
the intended use case and can be used effectively in the target environment.
### Augmentation settings
Augmentation settings for YOLO models refer to the various transformations and modifications
applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's
performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the
transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each
transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other
factors that may affect the augmentation process include the size and composition of the original dataset and the
specific task the model is being used for. It is important to carefully tune and experiment with these settings to
ensure that the augmented dataset is diverse and representative enough to train a high-performing model.
| hsv_h | 0.015 | Image HSV-Hue augmentation (fraction) |
|-------------|-------|-------------------------------------------------|
| hsv_s | 0.7 | Image HSV-Saturation augmentation (fraction) |
| hsv_v | 0.4 | Image HSV-Value augmentation (fraction) |
| degrees | 0.0 | Image rotation (+/- deg) |
| translate | 0.1 | Image translation (+/- fraction) |
| scale | 0.5 | Image scale (+/- gain) |
| shear | 0.0 | Image shear (+/- deg) |
| perspective | 0.0 | Image perspective (+/- fraction), range 0-0.001 |
| flipud | 0.0 | Image flip up-down (probability) |
| fliplr | 0.5 | Image flip left-right (probability) |
| mosaic | 1.0 | Image mosaic (probability) |
| mixup | 0.0 | Image mixup (probability) |
| copy_paste | 0.0 | Segment copy-paste (probability) |
### Logging, checkpoints, plotting and file management
Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model.
- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and
diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log
messages to a file.
- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows
you to resume training from a previous point if the training process is interrupted or if you want to experiment with
different training configurations.
- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is
behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by
generating plots using a logging library such as TensorBoard.
- File management: Managing the various files generated during the training process, such as model checkpoints, log
files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of
these files and make it easy to access and analyze them as needed.
Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make
it easier to debug and optimize the training process.
| Key | Value | Description |
|-----------|---------|---------------------------------------------------------------------------------------------|
| project: | 'runs' | The project name |
| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
| exist_ok: | `False` | ??? |
| plots | `False` | **Validation**: Save plots while validation |
| nosave | `False` | Don't save any plots, models or files |

39
docs/index.md
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@ -1,3 +1,40 @@
# Welcome to Ultralytics YOLO
TODO
Welcome to the Ultralytics YOLO documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
Only Look Once) object detection and image segmentation model developed by Ultralytics. This page serves as the starting
point for exploring the various resources available to help you get started with YOLOv8 and understand its features and
capabilities.
The YOLOv8 model is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of
object detection and image segmentation tasks. It can be trained on large datasets and is capable of running on a
variety of hardware platforms, from CPUs to GPUs.
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 documentation and reach out to us with any
questions or feedback.
### A Brief History of YOLO
YOLO (You Only Look Once) is a popular object detection and image segmentation model developed by Joseph Redmon and Ali
Farhadi at the University of Washington. The first version of YOLO was released in 2015 and quickly gained popularity
due to its high speed and accuracy.
YOLOv2 was released in 2016 and improved upon the original model by incorporating batch normalization, anchor boxes, and
dimension clusters. YOLOv3 was released in 2018 and further improved the model's performance by using a more efficient
backbone network, adding a feature pyramid, and making use of focal loss.
In 2020, YOLOv4 was released which introduced a number of innovations such as the use of Mosaic data augmentation, a new
anchor-free detection head, and a new loss function.
In 2021, Ultralytics released YOLOv5, which further improved the model's performance and added new features such as
support for panoptic segmentation and object tracking.
YOLO has been widely used in a variety of applications, including autonomous vehicles, security and surveillance, and
medical imaging. It has also been used to win several competitions, such as the COCO Object Detection Challenge and the
DOTA Object Detection Challenge.
For more information about the history and development of YOLO, you can refer to the following references:
- Redmon, J., & Farhadi, A. (2015). You only look once: Unified, real-time object detection. In Proceedings of the IEEE
conference on computer vision and pattern recognition (pp. 779-788).
- Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings

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All task Predictors are inherited from `BasePredictors` class that contains the model validation routine boilerplate. You can override any function of these Trainers to suit your needs.
---
### BasePredictor API Reference
:::ultralytics.yolo.engine.predictor.BasePredictor

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docs/reference/base_val.md
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@ -0,0 +1,5 @@
All task Validators are inherited from `BaseValidator` class that contains the model validation routine boilerplate. You can override any function of these Trainers to suit your needs.
---
### BaseValidator API Reference
:::ultralytics.yolo.engine.validator.BaseValidator

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docs/reference/exporter.md
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@ -0,0 +1,2 @@
### Exporter API Reference
:::ultralytics.yolo.engine.exporter.Exporter

18
mkdocs.yml
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@ -57,15 +57,15 @@ markdown_extensions:
- pymdownx.inlinehilite
- pymdownx.snippets
# button
# Button
- attr_list
# content tabs
# Content tabs
- pymdownx.superfences
- pymdownx.tabbed:
alternate_style: true
# highlight
# Highlight
- pymdownx.critic
- pymdownx.caret
- pymdownx.keys
@ -74,12 +74,12 @@ markdown_extensions:
plugins:
- mkdocstrings
# primary navigation
# Primary navigation
nav:
- Quickstart: quickstart.md
- CLI: cli.md
- Python Interface: sdk.md
- Configuration: conf.md
- Configuration: config.md
- Tasks:
- Detection: tasks/detection.md
- Segmentation: tasks/segmentation.md
@ -90,6 +90,8 @@ nav:
- Customize Predictor: customize/predict.md
- Reference:
- YOLO Models: reference/model.md
- Trainer :
- BaseTrainer: reference/base_trainer.md
- Engine:
- Trainer: reference/base_trainer.md
- Validator: reference/base_val.md
- Predictor: reference/base_pred.md
- Exporter: reference/exporter.md

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ultralytics/yolo/engine/exporter.py
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@ -131,7 +131,7 @@ class Exporter:
Initializes the Exporter class.
Args:
cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:

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ultralytics/yolo/engine/model.py
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@ -80,7 +80,6 @@ class YOLO:
Args:
weights (str): model checkpoint to be loaded
"""
obj = cls(init_key=cls.__init_key)
obj.ckpt = torch.load(weights, map_location="cpu")
@ -128,7 +127,7 @@ class YOLO:
Args:
source (str): Accepts all source types accepted by yolo
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in the docs
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
"""
overrides = self.overrides.copy()
overrides.update(kwargs)
@ -146,7 +145,7 @@ class YOLO:
Args:
data (str): The dataset to validate on. Accepts all formats accepted by yolo
kwargs: Any other args accepted by the validators. To see all args check 'configuration' section in the docs
**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
"""
if not self.model:
raise ModuleNotFoundError("model not initialized!")
@ -167,8 +166,7 @@ class YOLO:
Export model.
Args:
format (str): Export format
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in the docs
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
"""
overrides = self.overrides.copy()

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ultralytics/yolo/engine/trainer.py
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@ -519,7 +519,7 @@ class BaseTrainer:
decay (float): weight decay
Returns:
torch.optim.Optimizer: the built optimizer
optimizer (torch.optim.Optimizer): the built optimizer
"""
g = [], [], [] # optimizer parameter groups
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()

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