Metadata-Version: 2.4
Name: luxonis-train
Version: 0.3.11
Summary: Luxonis training framework for seamless training of various neural networks.
Author-email: Luxonis <support@luxonis.com>
Maintainer-email: Luxonis <support@luxonis.com>
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: repository, https://github.com/luxonis/luxonis-train
Project-URL: issues, https://github.com/luxonis/luxonis-train/issues
Keywords: ml,training,luxonis,oak
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: blobconverter~=1.4
Requires-Dist: cyclopts~=4.2
Requires-Dist: faster-coco-eval~=1.7
Requires-Dist: grad-cam~=1.5
Requires-Dist: lightning~=2.5
Requires-Dist: luxonis-ml[data,gcs,roboflow,s3,tracker]==0.8.0
Requires-Dist: mlflow~=3.6
Requires-Dist: onnxruntime~=1.23
Requires-Dist: onnxscript~=0.5
Requires-Dist: onnxsim~=0.4
Requires-Dist: onnx~=1.19
Requires-Dist: optuna-integration~=4.6
Requires-Dist: optuna~=4.6
Requires-Dist: psutil~=7.1
Requires-Dist: pynvim~=0.6
Requires-Dist: pytorch_metric_learning~=2.9
Requires-Dist: seaborn~=0.13
Requires-Dist: semver~=3.0
Requires-Dist: tabulate~=0.9
Requires-Dist: tensorboard~=2.20
Requires-Dist: termcolor~=3.2
Requires-Dist: torchmetrics~=1.8
Requires-Dist: torchvision~=0.24
Provides-Extra: dev
Requires-Dist: gdown>=4.2.0; extra == "dev"
Requires-Dist: pre-commit<4.0.0,>=3.2.1; extra == "dev"
Requires-Dist: opencv-stubs>=0.0.8; extra == "dev"
Requires-Dist: pytest-cov>=4.1.0; extra == "dev"
Requires-Dist: pytest-subtests>=0.12.1; extra == "dev"
Requires-Dist: pytest-order>=1.3.0; extra == "dev"
Requires-Dist: pytest-timeout>=2.4.0; extra == "dev"
Dynamic: license-file

# Luxonis Training Framework

![Ubuntu](https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white)
![Windows](https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white)
![MacOS](https://img.shields.io/badge/mac%20os-000000?style=for-the-badge&logo=apple&logoColor=white)

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
![PyBadge](https://img.shields.io/pypi/pyversions/luxonis-train?logo=data:image/svg+xml%3Bbase64,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)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
![CI](https://github.com/luxonis/luxonis-train/actions/workflows/ci.yaml/badge.svg)
[![codecov](https://codecov.io/gh/luxonis/luxonis-train/graph/badge.svg?token=647MTHBYD5)](https://codecov.io/gh/luxonis/luxonis-train)

<a name="overview"></a>

## 🌟 Overview

`LuxonisTrain` is a user-friendly tool designed to streamline the training of deep learning models, especially for edge devices. Built on top of `PyTorch Lightning`, it simplifies the process of training, testing, and exporting models with minimal coding required.

![Title Image](media/example_viz/title.png)

### ✨ Key Features

- **No Coding Required**: Define your training pipeline entirely through a single `YAML` configuration file.
- **Predefined Configurations**: Utilize ready-made configs for common computer vision tasks to start quickly.
- **Customizable**: Extend functionality with custom components using an intuitive Python API.
- **Edge Optimized**: Focus on models optimized for deployment on edge devices with limited compute resources.

> [!WARNING]
> **The project is in a beta state and might be unstable or contain bugs - please report any feedback.**

<a name="quick-start"></a>

## 🚀 Quick Start

Get started with `LuxonisTrain` in just a few steps:

1. **Install `LuxonisTrain`**

   ```bash
   pip install luxonis-train
   ```

   This will create the `luxonis_train` executable in your `PATH`.

1. **Use the provided `configs/detection_light_model.yaml` configuration file**

   You can download the file by executing the following command:

   ```bash
   wget https://raw.githubusercontent.com/luxonis/luxonis-train/main/configs/detection_light_model.yaml
   ```

1. **Find a suitable dataset for your task**

   We will use a sample COCO dataset from `RoboFlow` in this example.

1. **Start training**

   ```bash
   luxonis_train train                   \
     --config detection_light_model.yaml \
     loader.params.dataset_dir "roboflow://team-roboflow/coco-128/2/coco"
   ```

1. **Monitor progress with `TensorBoard`**

   ```bash
   tensorboard --logdir output/tensorboard_logs
   ```

   Open the provided URL in your browser to visualize the training progress

> [!NOTE]
> For hands-on examples of how to prepare data with `LuxonisML` and train AI models using `LuxonisTrain`, check out [this guide](https://github.com/luxonis/ai-tutorials/tree/main/training#-luxonis-train-tutorials).

## 📜 Table Of Contents

- [🌟 Overview](#overview)
  - [✨ Key Features](#key-features)
- [🚀 Quick Start](#quick-start)
- [🛠️ Installation](#installation)
- [📝 Usage](#usage)
  - [💻 CLI](#cli)
- [⚙️ Configuration](#configuration)
- [🗃️ Data Preparation](#data-preparation)
  - [📂 Data Directory](#data-directory)
  - [💾 `LuxonisDataset`](#luxonis-dataset)
- [🏋️‍♂️Training](#training)
- [✍ Testing](#testing)
- [🧠 Inference](#inference)
- [🤖 Exporting](#exporting)
- [🗂️ NN Archive](#nn-archive)
- [🔬 Tuning](#tuning)
- [🎨 Customizations](#customizations)
- [📚 Tutorials and Examples](#tutorials-and-examples)
- [🔑 Credentials](#credentials)
- [🤝 Contributing](#contributing)

<a name="installation"></a>

## 🛠️ Installation

`LuxonisTrain` requires **Python 3.10** or higher. We recommend using a virtual environment to manage dependencies.

**Install via `pip`**:

```bash
pip install luxonis-train
```

This will also install the `luxonis_train` CLI. For more information on how to use it, see [CLI Usage](#cli).

<a name="usage"></a>

## 📝 Usage

You can use `LuxonisTrain` either from the **command line** or via the **Python API**.
We will demonstrate both ways in the following sections.

<a name="cli"></a>

### 💻 CLI

The CLI is the most straightforward way how to use `LuxonisTrain`. The CLI provides several commands for training, testing, tuning, exporting and more.

**Available commands:**

- `train` - Start the training process
- `test` - Test the model on a specific dataset view
- `infer` - Run inference on a dataset, image directory, or a video file.
- `export` - Export the model to either `ONNX` or `BLOB` format that can be run on edge devices
- `archive` - Create an `NN Archive` file that can be used with our `DepthAI` API (coming soon)
- `tune` - Tune the hyperparameters of the model for better performance
- `inspect` - Inspect the dataset you are using and visualize the annotations
- `annotate` - Annotate a directory using the model’s predictions and generate a new LDF.

**To get help on any command:**

```bash
luxonis_train <command> --help
```

Specific usage examples can be found in the respective sections below.

<a name="configuration"></a>

## ⚙️ Configuration

`LuxonisTrain` uses `YAML` configuration files to define the training pipeline. Here's a breakdown of the key sections:

```yaml
model:
  name: model_name

  # Use a predefined detection model instead of defining
  # the model architecture manually
  predefined_model:
    name: DetectionModel
    params:
      variant: light

# Download and parse the coco dataset from RoboFlow.
# Save it internally as `coco_test` dataset for future reference.
loader:
  params:
    dataset_name: coco_test
    dataset_dir: "roboflow://team-roboflow/coco-128/2/coco"

trainer:
  batch_size: 8
  epochs: 200
  n_workers: 8
  validation_interval: 10

  preprocessing:
    train_image_size: [384, 384]

    # Uses the imagenet normalization by default
    normalize:
      active: true

    # Augmentations are powered by Albumentations
    augmentations:
      - name: Defocus
      - name: Sharpen
      - name: HorizontalFlip

  callbacks:
    - name: ExportOnTrainEnd
    - name: ArchiveOnTrainEnd
    - name: TestOnTrainEnd

  optimizer:
    name: SGD
    params:
      lr: 0.02

  scheduler:
    name: ConstantLR
```

### 📚 Configuration Reference

**For a complete reference of all available configuration options, see our [Configuration Documentation](configs/README.md).**

> [!TIP]
> We provide a set of predefined configuration files for common computer vision tasks in the `configs` directory.
> These are great starting points that you can customize for your specific needs.

<a name="data-preparation"></a>

## 🗃️ Data Preparation

`LuxonisTrain` supports several ways of loading data:

- using a data directory in one of the supported formats
- using an already existing dataset in our custom `LuxonisDataset` format
- using a custom loader
  - to learn how to implement and use custom loaders, see [Customizations](#customizations)

<a name="data-directory"></a>

### 📂 Data Directory

The easiest way to load data is to use a directory with the dataset in one of the supported formats.

**Supported formats:**

- `COCO` - We support COCO JSON format in two variants:
  - [`RoboFlow`](https://roboflow.com/formats/coco-json)
  - [`FiftyOne`](https://docs.voxel51.com/user_guide/export_datasets.html#cocodetectiondataset-export)
- [`Pascal VOC XML`](https://roboflow.com/formats/pascal-voc-xml)
- [`YOLO Darknet TXT`](https://roboflow.com/formats/yolo-darknet-txt)
- [`YOLOv4 PyTorch TXT`](https://roboflow.com/formats/yolov4-pytorch-txt)
- [`MT YOLOv6`](https://roboflow.com/formats/mt-yolov6)
- [`CreateML JSON`](https://roboflow.com/formats/createml-json)
- [`TensorFlow Object Detection CSV`](https://roboflow.com/formats/tensorflow-object-detection-csv)
- `Classification Directory` - A directory with subdirectories for each class
  ```plaintext
  dataset_dir/
  ├── train/
  │   ├── class1/
  │   │   ├── img1.jpg
  │   │   ├── img2.jpg
  │   │   └── ...
  │   ├── class2/
  │   └── ...
  ├── valid/
  └── test/
  ```
- `Segmentation Mask Directory` - A directory with images and corresponding masks.
  ```plaintext
  dataset_dir/
  ├── train/
  │   ├── img1.jpg
  │   ├── img1_mask.png
  │   ├── ...
  │   └── _classes.csv
  ├── valid/
  └── test/
  ```
  The masks are stored as grayscale `PNG` images where each pixel value corresponds to a class.
  The mapping from pixel values to classes is defined in the `_classes.csv` file.
  ```csv
  Pixel Value, Class
  0, background
  1, class1
  2, class2
  3, class3
  ```

#### Preparing your Data

1. Organize your dataset into one of the supported formats.
1. Place your dataset in a directory accessible by the training script.
1. Update the `dataset_dir` parameter in the configuration file to point to the dataset directory.

**The `dataset_dir` can be one of the following:**

- Local path to the dataset directory
- URL to a remote dataset
  - The dataset will be downloaded to a `"data"` directory in the current working directory
  - **Supported URL protocols:**
    - `s3://bucket/path/to/directory` fo **AWS S3**
    - `gs://buclet/path/to/directory` for **Google Cloud Storage**
    - `roboflow://workspace/project/version/format` for **RoboFlow**
      - `workspace` - name of the workspace the dataset belongs to
      - `project` - name of the project the dataset belongs to
      - `version` - version of the dataset
      - `format` - one of `coco`, `darknet`, `voc`, `yolov4pytorch`, `mt-yolov6`, `createml`, `tensorflow`, `folder`, `png-mask-semantic`
      - **example:** `roboflow://team-roboflow/coco-128/2/coco`

**Example:**

```yaml
loader:
  params:
    dataset_name: "coco_test"
    dataset_dir: "roboflow://team-roboflow/coco-128/2/coco"
```

<a name="luxonis-dataset"></a>

### 💾 `LuxonisDataset`

`LuxonisDataset` is our custom dataset format designed for easy and efficient dataset management.
To learn more about how to create a dataset in this format from scratch, see the [Luxonis ML](https://github.com/luxonis/luxonis-ml) repository.

To use the `LuxonisDataset` as a source of the data, specify the following in the config file:

```yaml
loader:
  params:
    # name of the dataset
    dataset_name: "dataset_name"

    # one of local (default), s3, gcs
    bucket_storage: "local"
```

> [!TIP]
> To inspect the loader output, use the `luxonis_train inspect` command:
>
> ```bash
> luxonis_train inspect --config configs/detection_light_model.yaml
> ```
>
> **The `inspect` command is currently only available in the CLI**

For additional information about the shapes of Luxonis ML data that the loader returns, please refer to the [Loaders README](luxonis_train/loaders/README.md).

<a name="training"></a>

## 🏋️‍♂️ Training

Once your configuration file and dataset are ready, start the training process.

**CLI:**

```bash
luxonis_train train --config configs/detection_light_model.yaml
```

> [!TIP]
> To change a configuration parameter from the command line, use the following syntax:
>
> ```bash
> luxonis_train train                           \
>   --config configs/detection_light_model.yaml \
>   loader.params.dataset_dir "roboflow://team-roboflow/coco-128/2/coco"
> ```

**Python API:**

```python
from luxonis_train import LuxonisModel

model = LuxonisModel(
  "configs/detection_light_model.yaml",
  {"loader.params.dataset_dir": "roboflow://team-roboflow/coco-128/2/coco"}
)
model.train()
```

**Expected Output:**

```log
INFO     Using predefined model: `DetectionModel`
INFO     Main metric: `MeanAveragePrecision`
INFO     GPU available: True (cuda), used: True
INFO     TPU available: False, using: 0 TPU cores
INFO     HPU available: False, using: 0 HPUs
...
INFO     Training finished
INFO     Checkpoints saved in: output/1-coral-wren
```

**Monitoring with `TensorBoard`:**

If not explicitly disabled, the training process will be monitored by `TensorBoard`. To start the `TensorBoard` server, run:

```bash
tensorboard --logdir output/tensorboard_logs
```

Open the provided URL to visualize training metrics.

<a name="testing"></a>

## ✍ Testing

Evaluate your trained model on a specific dataset view (`train`, `val`, or `test`).

**CLI:**

```bash
luxonis_train test --config configs/detection_light_model.yaml \
                   --view val                                  \
                   --weights path/to/checkpoint.ckpt
```

**Python API:**

```python
from luxonis_train import LuxonisModel

model = LuxonisModel("configs/detection_light_model.yaml")
model.test(weights="path/to/checkpoint.ckpt")
```

The testing process can be started automatically at the end of the training by using the `TestOnTrainEnd` callback.
To learn more about callbacks, see [Callbacks](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/callbacks/README.md).

<a name="inference"></a>

## 🧠 Inference

Run inference on images, datasets, or videos.

**CLI:**

- **Inference on a Dataset View:**

```bash
luxonis_train infer --config configs/detection_light_model.yaml \
                    --view val                                  \
                    --weights path/to/checkpoint.ckpt
```

- **Inference on a Video File:**

```bash
luxonis_train infer --config configs/detection_light_model.yaml \
                    --weights path/to/checkpoint.ckpt           \
                    --source-path path/to/video.mp4
```

- **Inference on an Image Directory:**

```bash
luxonis_train infer --config configs/detection_light_model.yaml \
                    --weights path/to/checkpoint.ckpt           \
                    --source-path path/to/images                \
                    --save-dir path/to/save_directory
```

**Python API:**

```python
from luxonis_train import LuxonisModel

model = LuxonisModel("configs/detection_light_model.yaml")

# infer on a dataset view
model.infer(weights="path/to/checkpoint.ckpt", view="val")

# infer on a video file
model.infer(weights="path/to/checkpoint.ckpt", source_path="path/to/video.mp4")

# infer on an image directory and save the results
model.infer(
    weights="path/to/checkpoint.ckpt",
    source_path="path/to/images",
    save_dir="path/to/save_directory",
)
```

<a name="exporting"></a>

## 🤖 Exporting

Export your trained models to formats suitable for deployment on edge devices.

Supported formats:

- **ONNX**: Open Neural Network Exchange format.
- **BLOB**: Format compatible with OAK-D cameras.

To configure the exporter, you can specify the [exporter](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#exporter) section in the config file.

You can see an example export configuration [here](https://github.com/luxonis/luxonis-train/blob/main/configs/example_export.yaml).

**CLI:**

```bash
luxonis_train export --config configs/example_export.yaml --weights path/to/weights.ckpt
```

**Python API:**

```python
from luxonis_train import LuxonisModel

model = LuxonisModel("configs/example_export.yaml")
model.export(weights="path/to/weights.ckpt")
```

Model export can be run automatically at the end of the training by using the `ExportOnTrainEnd` callback.

The exported models are saved in the export directory within your `output` folder.

<a name="nn-archive"></a>

## 🗂️ NN Archive

Create an `NN Archive` file for easy deployment with the `DepthAI` API.

The archive contains the exported model together with all the metadata needed for running the model.

**CLI:**

```bash
luxonis_train archive                         \
  --config configs/detection_light_model.yaml \
  --weights path/to/checkpoint.ckpt
```

**Python API:**

```python
from luxonis_train import LuxonisModel

model = LuxonisModel("configs/detection_light_model.yaml")
model.archive(weights="path/to/checkpoint.ckpt")
```

The archive can be created automatically at the end of the training by using the `ArchiveOnTrainEnd` callback.

<a name="tuning"></a>

## 🔬 Tuning

Optimize your model's performance using hyperparameter tuning powered by [`Optuna`](https://optuna.org/).

**Configuration:**

Include a [`tuner`](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#tuner) section in your configuration file.

```yaml

tuner:
  study_name: det_study
  n_trials: 10
  storage:
    backend: sqlite
  params:
    trainer.optimizer.name_categorical: ["Adam", "SGD"]
    trainer.optimizer.params.lr_float: [0.0001, 0.001]
    trainer.batch_size_int: [4, 16, 4]
```

**CLI:**

```bash
luxonis_train tune --config configs/example_tuning.yaml
```

**Python API:**

```python
from luxonis_train import LuxonisModel

model = LuxonisModel("configs/example_tuning.yaml")
model.tune()
```

<a name="customizations"></a>

## 🎨 Customizations

`LuxonisTrain` is highly modular, allowing you to customize various components:

- [**Loaders**](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/loaders/README.md): Handles data loading and preprocessing.
- [**Nodes**](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/nodes/README.md): Represents computational units in the model architecture.
- [**Losses**](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/losses/README.md): Define the loss functions used to train the model.
- [**Metrics**](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/metrics/README.md): Measure the model's performance during training.
- [**Visualizers**](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/visualizers/README.md): Visualize the model's predictions during training.
- [**Callbacks**](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/callbacks/README.md): Allow custom code to be executed at different stages of training.
- [**Optimizers**](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#optimizer): Control how the model's weights are updated.
- [**Schedulers**](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#scheduler): Adjust the learning rate during training.
- [**Training Strategy**](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#training-strategy): Specify a custom combination of optimizer and scheduler to tailor the training process for specific use cases.

**Creating Custom Components:**

Implement custom components by subclassing the respective base classes and/or registering them.
Registered components can be referenced in the config file. Custom components need to inherit from their respective base classes:

- **Loaders** - [`BaseLoaderTorch`](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/loaders/base_loader.py)
- **Nodes** - [`BaseNode`](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/models/nodes/base_node.py)
- **Losses** - [`BaseLoss`](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/losses/base_loss.py)
- **Metrics** - [`BaseMetric`](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/metrics/base_metric.py)
- **Visualizers** - [`BaseVisualizer`](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/visualizers/base_visualizer.py)
- **Callbacks** - [`lightning.pytorch.callbacks.Callback`](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html), requires manual registration to the `CALLBACKS` registry
- **Optimizers** - [`torch.optim.Optimizer`](https://pytorch.org/docs/stable/optim.html#torch.optim.Optimizer), requires manual registration to the `OPTIMIZERS` registry
- **Schedulers** - [`torch.optim.lr_scheduler.LRScheduler`](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate), requires manual registration to the `SCHEDULERS` registry
- **Training Strategy** - [`BaseTrainingStrategy`](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/strategies/base_strategy.py)

**Examples:**

**Custom Callback:**

```python
import lightning.pytorch as pl

from luxonis_train import LuxonisLightningModule
from luxonis_train.registry import CALLBACKS


@CALLBACKS.register()
class CustomCallback(pl.Callback):
    def __init__(self, message: str, **kwargs):
        super().__init__(**kwargs)
        self.message = message

    # Will be called at the end of each training epoch.
    # Consult the PyTorch Lightning documentation for more callback methods.
    def on_train_epoch_end(
        self,
        trainer: pl.Trainer,
        pl_module: LuxonisLightningModule,
    ) -> None:
        print(self.message)
```

**Custom Loss:**

```python
from torch import Tensor

from luxonis_train import BaseLoss, Tasks

# Subclasses of `BaseNode`, `BaseLoss`, `BaseMetric`
# and `BaseVisualizer` are registered automatically.
class CustomLoss(BaseLoss):
    supported_tasks = [Tasks.CLASSIFICATION, Tasks.SEGMENTATION]

    def __init__(self, smoothing: float, **kwargs):
        super().__init__(**kwargs)
        self.smoothing = smoothing

    def forward(self, predictions: Tensor, targets: Tensor) -> Tensor:
        # Implement the actual loss logic here
        value = predictions.sum() * self.smoothing
        return value.abs()
```

For additional examples of creating custom components, please refer to the [examples section](examples/README.md).

**Using custom components in the configuration file:**

```yaml
model:
  nodes:
  - name: SegmentationHead
    losses:
    - name: CustomLoss
      params:
        smoothing: 0.0001

trainer:
  callbacks:
    - name: CustomCallback
      params:
        lr: "Hello from the custom callback!"
```

> [!NOTE]
> Files containing the custom components must be sourced before the training script is run.
> To do that in CLI, you can use the `--source` argument:
>
> ```bash
> luxonis_train --source custom_components.py train --config config.yaml
> ```

**Python API:**

You have to import the custom components before creating the `LuxonisModel` instance.

```python
from custom_components import *
from luxonis_train import LuxonisModel

model = LuxonisModel("config.yaml")
model.train()
```

For more information on how to define custom components, consult the respective in-source documentation.

<a name="tutorials-and-examples"></a>

## 📚 Tutorials and Examples

We are actively working on providing examples and tutorials for different parts of the library which will help you to start more easily. The tutorials can be found [here](https://github.com/luxonis/ai-tutorials/tree/main/training) and will be updated regularly.

<a name="credentials"></a>

## 🔑 Credentials

When using cloud services, avoid hard-coding credentials or placing them directly in your configuration files.
Instead:

- Use environment variables to store sensitive information.
- Use a `.env` file and load it securely, ensuring it's excluded from version control.

**Supported Cloud Services:**

- **AWS S3**, requires:
  - `AWS_ACCESS_KEY_ID`
  - `AWS_SECRET_ACCESS_KEY`
  - `AWS_S3_ENDPOINT_URL`
- **Google Cloud Storage**, requires:
  - `GOOGLE_APPLICATION_CREDENTIALS`
- **RoboFlow**, requires:
  - `ROBOFLOW_API_KEY`

**For logging and tracking, we support:**

- **MLFlow**, requires:
  - `MLFLOW_S3_BUCKET`
  - `MLFLOW_S3_ENDPOINT_URL`
  - `MLFLOW_TRACKING_URI`
- **WandB**, requires:
  - `WANDB_API_KEY`

**For remote database storage, we support:**

- `POSTGRES_PASSWORD`
- `POSTGRES_HOST`
- `POSTGRES_PORT`
- `POSTGRES_DB`

<a name="contributing"></a>

## 🤝 Contributing

We welcome contributions! Please read our [Contribution Guide](https://github.com/luxonis/luxonis-train/blob/main/CONTRIBUTING.md) to get started. Whether it's reporting bugs, improving documentation, or adding new features, your help is appreciated.
