Refactor code formatting in README.md and add train_model.md document…

…ation

README.md

@@ -51,6 +51,10 @@ Roadmap features:
 - Make available detection information for other plugins through settings
 - More real-time models choices
 
+## Train a custom model
+
+Follow the instructions in [docs/train_model.md](docs/train_model.md) to train your own custom model.
+
 ## Building
 
 The plugin was built and tested on Mac OSX  (Intel & Apple silicon), Windows and Linux.

docs/train_model.md

@@ -0,0 +1,119 @@
+# How to train your custom model for OBS Detect plugin
+
+OBS Detect is based on the [EdgeYOLO](https://github.com/LSH9832/edgeyolo) work.
+They provide a model training script that works with just setting some parameters.
+
+You need to get a dataset first. The supported dataset formats are mentiond in the [EdgeYOLO](https://github.com/LSH9832/edgeyolo?tab=readme-ov-file#train) readme: COCO, VOC, YOLO, and DOTA.
+
+In this example we will use a COCO dataset from Roboflow. You can get the dataset from [here](https://public.roboflow.com/object-detection/aquarium/2).
+
+The dataset is in the COCO format, so we can use it directly with the EdgeYOLO training script.
+
+## Step 1: Unpack the dataset
+
+Unzip the dataset to a folder. The dataset should have the following structure:
+
+```plaintext
+dataset_folder/
+    train/
+        _annotations.coco.json
+        image1.jpg
+        image2.jpg
+        ...
+    valid/
+        _annotations.coco.json
+        image1.jpg
+        image2.jpg
+        ...
+    test/
+        _annotations.coco.json
+        image1.jpg
+        image2.jpg
+        ...
+```
+
+## Step 2: Install the required packages
+
+You need to install the required packages to train the model. You can install them using the following command:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Step 3: Setup the parameters of the training script
+
+You need to set the parameters of the training script.
+Make a copy of the configuration file and set the parameters according to your needs.
+For example for my case, I will set the following parameters in the `params/train/train_coco_aquarium.yaml` file:
+
+```yaml
+# models & weights------------------------------------------------------------------------------------------------------
+model_cfg: "params/model/edgeyolo_tiny.yaml"         # model structure config file
+weights: "output/train/edgeyolo_tiny_coco_aquarium/last.pth"  # contains model_cfg, set null or a no-exist filename if not use it
+use_cfg: false                                       # force using model_cfg instead of cfg in weights to build model
+
+# output----------------------------------------------------------------------------------------------------------------
+output_dir: "output/train/edgeyolo_tiny_coco_aquarium"        # all train output file will save in this dir
+save_checkpoint_for_each_epoch: true                 # save models for each epoch (epoch_xxx.pth, not only best/last.pth)
+log_file: "log.txt"                                  # log file (in output_dir)
+
+# dataset & dataloader--------------------------------------------------------------------------------------------------
+dataset_cfg: "params/dataset/coco_aquarium.yaml"              # dataset config
+batch_size_per_gpu: 8                                # batch size for each GPU
+loader_num_workers: 4                                # number data loader workers for each GPU
+num_threads: 1                                       # pytorch threads number for each GPU
+
+# the rest--------------------------------------------------------------------------------------------------------------
+```
+
+You will also need to set up the dataset configuration file `params/dataset/coco_aquarium.yaml`:
+
+```yaml
+type: "coco"
+
+dataset_path: "<...>/Downloads/edgeyolo/Aquarium Combined.v2-raw-1024.coco"
+
+kwargs:
+  suffix: "jpg"
+  use_cache: true      # (test on i5-12490f) Actual time cost:  52s -> 10s(seg enabled) and 39s -> 4s (seg disabled)
+
+train:
+  image_dir: "<...>/Downloads/edgeyolo/Aquarium Combined.v2-raw-1024.coco/train"
+  label: "<...>/Downloads/edgeyolo/Aquarium Combined.v2-raw-1024.coco/train/_annotations.coco.json"
+
+val:
+  image_dir: "<...>/Downloads/edgeyolo/Aquarium Combined.v2-raw-1024.coco/valid"
+  label: "<...>/Downloads/edgeyolo/Aquarium Combined.v2-raw-1024.coco/valid/_annotations.coco.json"
+
+test:
+  test_dir: "test2017"
+
+segmentaion_enabled: false
+
+names: ["creatures", "fish", "jellyfish", "penguin", "puffin", "shark", "starfish", "stingray"]
+```
+
+## Step 4: Train the model
+
+You can train the model using the following command:
+
+```bash
+python train.py -c params/train/train_coco_aquarium.yaml
+```
+
+This may take some time depending on the dataset size and the model you are using.
+Best to have a GPU for training.
+
+## Step 5: Convert the model to ONNX
+
+After training the model, you can convert it to ONNX format using the `export.py` script from EdgeYOLO.
+
+```bash
+python export.py --weights output/train/edgeyolo_tiny_coco_aquarium/last.pth --onnx-only 
+```
+
+You will find the ONNX model in the `output/export/` folder.
+
+## Step 6: Use the model with OBS Detect
+
+TBD