diff --git a/.github/workflows/docker.yaml b/.github/workflows/docker.yaml new file mode 100644 index 0000000..ca3cc85 --- /dev/null +++ b/.github/workflows/docker.yaml @@ -0,0 +1,57 @@ +# 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 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 2ebe53e..480127f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,7 +51,7 @@ repos: 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 diff --git a/README.md b/README.md index 17148b2..1dcfe92 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,9 @@ ```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. diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..41d71e6 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,64 @@ +# 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 diff --git a/docker/Dockerfile-arm64 b/docker/Dockerfile-arm64 new file mode 100644 index 0000000..d44e6f3 --- /dev/null +++ b/docker/Dockerfile-arm64 @@ -0,0 +1,45 @@ +# 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 diff --git a/docker/Dockerfile-cpu b/docker/Dockerfile-cpu new file mode 100644 index 0000000..ba9441e --- /dev/null +++ b/docker/Dockerfile-cpu @@ -0,0 +1,44 @@ +# 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 diff --git a/docs/README.md b/docs/README.md index 2236b87..8c8981b 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,7 +1,85 @@ -## 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/). diff --git a/docs/cli.md b/docs/cli.md index 8b8f41b..ac6981f 100644 --- a/docs/cli.md +++ b/docs/cli.md @@ -12,27 +12,27 @@ If you want to train, validate or run inference on models and don't need to make The experiment arguments can be overridden directly by pass `arg=val` covered in the next section. You can run any supported task by setting `task` and `mode` in cli. === "Training" - | | `task` | snippet | - | ----------- | ------------- | ----------------------------------------------------------- | - | Detection | `detect` | 
yolo task=detect mode=train
| - | Instance Segment | `segment` | 
yolo task=segment mode=train
| - | Classification | `classify` | 
yolo task=classify mode=train
| +| | `task` | snippet | +| ----------- | ------------- | ----------------------------------------------------------- | +| Detection | `detect` | 
yolo task=detect mode=train
| +| Instance Segment | `segment` | 
yolo task=segment mode=train
| +| Classification| `classify` | 
yolo task=classify mode=train
| === "Inference" - | | `task` | snippet | - | ----------- | ------------- | ------------------------------------------------------------ | - | Detection | `detect` | 
yolo task=detect mode=infer
| - | Instance Segment | `segment` | 
yolo task=segment mode=infer
| - | Classification | `classify` | 
yolo task=classify mode=infer
| +| | `task` | snippet | +| ----------- | ------------- | ------------------------------------------------------------ | +| Detection | `detect` | 
yolo task=detect mode=infer
| +| Instance Segment | `segment` | 
yolo task=segment mode=infer
| +| Classification| `classify` | 
yolo task=classify mode=infer
| === "Validation" - | | `task` | snippet | - | ----------- | ------------- | ------------------------------------------------------------- | - | Detection | `detect` | 
yolo task=detect mode=val
| - | Instance Segment | `segment` | 
yolo task=segment mode=val
| - | Classification | `classify` | 
yolo task=classify mode=val
| +| | `task` | snippet | +| ----------- | ------------- | ------------------------------------------------------------- | +| Detection | `detect` | 
yolo task=detect mode=val
| +| Instance Segment | `segment` | 
yolo task=segment mode=val
| +| Classification| `classify` | 
yolo task=classify mode=val
| !!! note "" Note: The arguments don't require `'--'` prefix. These are reserved for special commands covered later diff --git a/docs/conf.md b/docs/conf.md deleted file mode 100644 index 687f5c6..0000000 --- a/docs/conf.md +++ /dev/null @@ -1,109 +0,0 @@ -## 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`.
- `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`.
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 | \ No newline at end of file diff --git a/docs/config.md b/docs/config.md new file mode 100644 index 0000000..1cf42b5 --- /dev/null +++ b/docs/config.md @@ -0,0 +1,202 @@ +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`.
- `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`.
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 | \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 208b23c..03f2776 100644 --- a/docs/index.md +++ b/docs/index.md @@ -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 \ No newline at end of file diff --git a/docs/reference/base_pred.md b/docs/reference/base_pred.md new file mode 100644 index 0000000..f5449f1 --- /dev/null +++ b/docs/reference/base_pred.md @@ -0,0 +1,5 @@ +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 \ No newline at end of file diff --git a/docs/reference/base_val.md b/docs/reference/base_val.md new file mode 100644 index 0000000..e80b9c2 --- /dev/null +++ b/docs/reference/base_val.md @@ -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 \ No newline at end of file diff --git a/docs/reference/exporter.md b/docs/reference/exporter.md new file mode 100644 index 0000000..3b2590e --- /dev/null +++ b/docs/reference/exporter.md @@ -0,0 +1,2 @@ +### Exporter API Reference +:::ultralytics.yolo.engine.exporter.Exporter \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 7e0f50c..9e42752 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -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 diff --git a/ultralytics/yolo/engine/exporter.py b/ultralytics/yolo/engine/exporter.py index c20ba22..db480f3 100644 --- a/ultralytics/yolo/engine/exporter.py +++ b/ultralytics/yolo/engine/exporter.py @@ -2,20 +2,20 @@ """ Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit -Format | `format=argument` | Model ---- | --- | --- -PyTorch | - | yolov8n.pt -TorchScript | `torchscript` | yolov8n.torchscript -ONNX | `onnx` | yolov8n.onnx -OpenVINO | `openvino` | yolov8n_openvino_model/ -TensorRT | `engine` | yolov8n.engine -CoreML | `coreml` | yolov8n.mlmodel -TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/ -TensorFlow GraphDef | `pb` | yolov8n.pb -TensorFlow Lite | `tflite` | yolov8n.tflite -TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite -TensorFlow.js | `tfjs` | yolov8n_web_model/ -PaddlePaddle | `paddle` | yolov8n_paddle_model/ +Format | `format=argument` | Model +--- | --- | --- +PyTorch | - | yolov8n.pt +TorchScript | `torchscript` | yolov8n.torchscript +ONNX | `onnx` | yolov8n.onnx +OpenVINO | `openvino` | yolov8n_openvino_model/ +TensorRT | `engine` | yolov8n.engine +CoreML | `coreml` | yolov8n.mlmodel +TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/ +TensorFlow GraphDef | `pb` | yolov8n.pb +TensorFlow Lite | `tflite` | yolov8n.tflite +TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov8n_web_model/ +PaddlePaddle | `paddle` | yolov8n_paddle_model/ Requirements: $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU @@ -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: diff --git a/ultralytics/yolo/engine/model.py b/ultralytics/yolo/engine/model.py index ada350f..0735580 100644 --- a/ultralytics/yolo/engine/model.py +++ b/ultralytics/yolo/engine/model.py @@ -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") @@ -110,7 +109,7 @@ class YOLO: Logs model info Args: - verbose (bool): Controls verbosity. + verbose (bool): Controls verbosity. """ if not self.model: LOGGER.info("model not initialized!") @@ -127,8 +126,8 @@ class YOLO: Visualize prediction. 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 + 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 docs """ overrides = self.overrides.copy() overrides.update(kwargs) @@ -145,8 +144,8 @@ class YOLO: Validate a model on a given dataset 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 + 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 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() diff --git a/ultralytics/yolo/engine/trainer.py b/ultralytics/yolo/engine/trainer.py index 7f43554..7fb1c24 100644 --- a/ultralytics/yolo/engine/trainer.py +++ b/ultralytics/yolo/engine/trainer.py @@ -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()