[Docs]: Add customization tutorial and address feedback (#155)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/cli.md

@@ -3,10 +3,10 @@ If you want to train, validate or run inference on models and don't need to make
 
 !!! tip "Syntax"
     ```bash
-    yolo task=detect    mode=train  model=s.yaml    epochs=1 ...
+    yolo task=detect    mode=train    model=yolov8n.yaml    epochs=1 ...
                 ...           ...           ...
-              segment        infer        s-cls.pt
-              classify        val         s-seg.pt
+              segment        predict        yolov8n-seg.pt
+              classify        val           yolov8n-cls.pt
     ```
 
 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.
@@ -18,13 +18,13 @@ The experiment arguments can be overridden directly by pass `arg=val` covered in
 |  Instance Segment  |  `segment`  | <pre><code>yolo task=segment mode=train      </code></pre>  |
 |  Classification|  `classify` | <pre><code>yolo task=classify mode=train    </code></pre>   |
 
-=== "Inference"
+=== "Prediction"
 
 |                | `task`      | snippet                                                  |
 | -----------    | -------------   | ------------------------------------------------------------ |
-|  Detection     |  `detect`   | <pre><code>yolo task=detect mode=infer       </code></pre>   |
-|  Instance Segment  |  `segment`  | <pre><code>yolo task=segment mode=infer     </code></pre>|
-|  Classification|  `classify` | <pre><code>yolo task=classify mode=infer    </code></pre>|
+|  Detection     |  `detect`   | <pre><code>yolo task=detect mode=predict       </code></pre>   |
+|  Instance Segment  |  `segment`  | <pre><code>yolo task=segment mode=predict     </code></pre>|
+|  Classification|  `classify` | <pre><code>yolo task=classify mode=predict    </code></pre>|
 
 === "Validation"
 

docs/config.md

@@ -46,7 +46,7 @@ include train, val, and predict.
 | 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
 
 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
@@ -88,7 +88,7 @@ task.
 | mask_ratio      | 4       | **Segmentation**: Set mask downsampling                                     |
 | dropout         | `False` | **Classification**: Use dropout while training                              |
 
-### Prediction Settings
+### Prediction
 
 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
@@ -114,7 +114,7 @@ given task.
 | agnostic_nms   | `False`              | Class-agnostic NMS                              |
 | retina_masks   | `False`              | **Segmentation:** High resolution masks         |
 
-### Validation settings
+### Validation
 
 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
@@ -147,7 +147,7 @@ the specific task the model is being used for and the requirements or constraint
 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
 
 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

docs/engine.md

@@ -0,0 +1,66 @@
+Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine executors. Let's take a look at the Trainer engine.
+
+## BaseTrainer
+BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overidding the required functions or operations as long the as correct formats are followed. For example you can support your own custom model and dataloder by just overriding these functions:
+
+* `get_model(cfg, weights)` - The function that builds a the model to be trained
+* `get_dataloder()` - The function that builds the dataloder
+More details and source code can be found in [`BaseTrainer` Reference](../reference/base_trainer.md)
+
+## DetectionTrainer
+Here's how you can use the YOLOv8 `DetectionTrainer` and customize it.
+```python
+from Ultrlaytics.yolo.v8 import DetectionTrainer
+
+trainer = DetectionTrainer(overrides={...})
+trainer.train()
+trained_model = trainer.best # get best model
+```
+
+### Customizing the DetectionTrainer
+Let's customize the trainer **to train a custom detection model** that is not supported directly. You can do this by simply overloading the existing the `get_model` functionality:
+```python
+from Ultrlaytics.yolo.v8 import DetectionTrainer
+
+class CustomTrainer(DetectionTrainer):
+    def get_model(self, cfg, weights):
+        ...
+
+trainer = CustomTrainer(overrides={...})
+trainer.train()
+```
+You now realize that you need to customize the trainer further to:
+
+* Customize the `loss function`. 
+* Add `callback` that uploads model to your google drive after every 10 `epochs`
+Here's how you can do it:
+
+```python
+from Ultrlaytics.yolo.v8 import DetectionTrainer
+
+class CustomTrainer(DetectionTrainer):
+    def get_model(self, cfg, weights):
+        ...
+
+    def criterion(self, preds, batch):
+        # get ground truth
+        imgs = batch["imgs"]
+        bboxes = batch["bboxes"]
+        ...
+        return loss, loss_items # see Reference-> Trainer for details on the expected format
+
+# callback to upload model weights
+def log_model(trainer):
+    last_weight_path = trainer.last
+    ...
+
+trainer = CustomTrainer(overrides={...})
+trainer.add_callback("on_train_epoch_end", log_model) # Adds to existing callback
+trainer.train()
+```
+
+To know more about Callback triggering events and entry point,  checkout our Callbacks guide # TODO
+
+## Other engine components
+There are other componenets that can be customized similarly like `Validators` and `Predictiors`
+To know more about their implementation details, go to Reference

docs/index.md

@@ -49,7 +49,7 @@ For more information about the history and development of YOLO, you can refer to
   conference on computer vision and pattern recognition (pp. 779-788).
 - Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings
 
-### YOLOv8 by Ultralytics
+### Ultralytics YOLOv8
 
 YOLOv8 is the latest version of the YOLO object detection and image segmentation model developed by
 Ultralytics. YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO

docs/sdk.md

@@ -56,42 +56,36 @@ This is the simplest way of simply using yolo models in a python environment. It
 
     More functionality coming soon
 
-To know more about using `YOLO` models, refer Model class refernce
+To know more about using `YOLO` models, refer Model class Reference
 
 [Model reference](reference/model.md){ .md-button .md-button--primary}
 
 ---
-### Customizing Tasks with Trainers
+### Using Trainers
 `YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`. 
-You can easily cusotmize Trainers to support custom tasks or explore R&D ideas.
-
-!!! tip "Trainer Examples"
-    === "DetectionTrainer"
+!!! tip "Detection Trainer Example"
         ```python
-        from ultralytics import yolo
+        from ultralytics.yolo import v8 import DetectionTrainer, DetectionValidator, DetectionPredictor
 
-        trainer = yolo.DetectionTrainer(data=..., epochs=1) # override default configs
-        trainer = yolo.DetectionTrainer(data=..., epochs=1, device="1,2,3,4") # DDP
+        # trainer
+        trainer = DetectionTrainer(overrides={})
         trainer.train()
-        ```
+        trained_model = trainer.best
 
-    === "SegmentationTrainer"
-        ```python
-        from ultralytics import yolo
+        # Validator
+        val = DetectionValidator(args=...)
+        val(model=trained_model)
 
-        trainer = yolo.SegmentationTrainer(data=..., epochs=1) # override default configs
-        trainer = yolo.SegmentationTrainer(data=..., epochs=1, device="0,1,2,3") # DDP
-        trainer.train()
-        ```
-    === "ClassificationTrainer"
-        ```python
-        from ultralytics import yolo
+        # predictor
+        pred = DetectionPredictor(overrides={})
+        pred(source=SOURCE, model=trained_model)
 
-        trainer = yolo.ClassificationTrainer(data=..., epochs=1) # override default configs
-        trainer = yolo.ClassificationTrainer(data=..., epochs=1, device="0,1,2,3") # DDP
-        trainer.train()
-        ```
+        # resume from last weight
+        overrides["resume"] = trainer.last
+        trainer = detect.DetectionTrainer(overrides=overrides)
 
-Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section. More details about the base engine classes is available in the reference section.
+        ```
+You can easily customize Trainers to support custom tasks or explore R&D ideas.
+Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section.
 
-[Customization tutorials](#){ .md-button .md-button--primary}
+[Customization tutorials](engine.md){ .md-button .md-button--primary}

mkdocs.yml

@@ -81,14 +81,7 @@ nav:
   - CLI: cli.md
   - Python Interface: sdk.md
   - Configuration: config.md
-  - Tasks:
-      - Detection: tasks/detection.md
-      - Segmentation: tasks/segmentation.md
-      - Classification: tasks/classification.md
-  - Advanced Tutorials:
-      - Customize Trainer: customize/train.md
-      - Customize Validator: customize/val.md
-      - Customize Predictor: customize/predict.md
+  - Customization Guide: engine.md
   - Reference:
       - Python Model interface: reference/model.md
       - Engine:

ultralytics/yolo/engine/model.py

@@ -30,7 +30,7 @@ class YOLO:
 
     def __init__(self, model='yolov8n.yaml', type="v8") -> None:
         """
-        Initializes the YOLO object.
+        > Initializes the YOLO object.
 
         Args:
             model (str, Path): model to load or create
@@ -57,7 +57,7 @@ class YOLO:
 
     def _new(self, cfg: str, verbose=True):
         """
-        Initializes a new model and infers the task type from the model definitions.
+        > Initializes a new model and infers the task type from the model definitions.
 
         Args:
             cfg (str): model configuration file
@@ -73,7 +73,7 @@ class YOLO:
 
     def _load(self, weights: str):
         """
-        Initializes a new model and infers the task type from the model head
+        > Initializes a new model and infers the task type from the model head.
 
         Args:
             weights (str): model checkpoint to be loaded
@@ -88,7 +88,7 @@ class YOLO:
 
     def reset(self):
         """
-        Resets the model modules .
+        > Resets the model modules.
         """
         for m in self.model.modules():
             if hasattr(m, 'reset_parameters'):
@@ -98,7 +98,7 @@ class YOLO:
 
     def info(self, verbose=False):
         """
-        Logs model info
+        > Logs model info.
 
         Args:
             verbose (bool): Controls verbosity.
@@ -129,7 +129,7 @@ class YOLO:
     @smart_inference_mode()
     def val(self, data=None, **kwargs):
         """
-        Validate a model on a given dataset
+        > Validate a model on a given dataset .
 
         Args:
             data (str): The dataset to validate on. Accepts all formats accepted by yolo
@@ -148,7 +148,7 @@ class YOLO:
     @smart_inference_mode()
     def export(self, **kwargs):
         """
-        Export model.
+        > Export model.
 
         Args:
             **kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
@@ -164,7 +164,7 @@ class YOLO:
 
     def train(self, **kwargs):
         """
-        Trains the model on given dataset.
+        > Trains the model on a given dataset.
 
         Args:
             **kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section.
@@ -189,6 +189,12 @@ class YOLO:
         self.trainer.train()
 
     def to(self, device):
+        """
+        > Sends the model to the given device.
+
+        Args:
+            device (str): device
+        """
         self.model.to(device)
 
     def _guess_ops_from_task(self, task):

ultralytics/yolo/engine/trainer.py

@@ -39,7 +39,7 @@ class BaseTrainer:
     """
     BaseTrainer
 
-    A base class for creating trainers.
+    > A base class for creating trainers.
 
     Attributes:
         args (OmegaConf): Configuration for the trainer.
@@ -74,7 +74,7 @@ class BaseTrainer:
 
     def __init__(self, config=DEFAULT_CONFIG, overrides=None):
         """
-        Initializes the BaseTrainer class.
+        > Initializes the BaseTrainer class.
 
         Args:
             config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
@@ -148,13 +148,13 @@ class BaseTrainer:
 
     def add_callback(self, event: str, callback):
         """
-        Appends the given callback. TODO: unused, consider removing
+        > Appends the given callback.
         """
         self.callbacks[event].append(callback)
 
     def set_callback(self, event: str, callback):
         """
-        Overrides the existing callbacks with the given callback.  TODO: unused, consider removing
+        > Overrides the existing callbacks with the given callback.
         """
         self.callbacks[event] = [callback]
 
@@ -185,7 +185,7 @@ class BaseTrainer:
 
     def _setup_train(self, rank, world_size):
         """
-        Builds dataloaders and optimizer on correct rank process
+        > Builds dataloaders and optimizer on correct rank process.
         """
         # model
         self.run_callbacks("on_pretrain_routine_start")
@@ -373,13 +373,13 @@ class BaseTrainer:
 
     def get_dataset(self, data):
         """
-        Get train, val path from data dict if it exists. Returns None if data format is not recognized
+        > Get train, val path from data dict if it exists. Returns None if data format is not recognized.
         """
         return data["train"], data.get("val") or data.get("test")
 
     def setup_model(self):
         """
-        load/create/download model for any task
+        > load/create/download model for any task.
         """
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return
@@ -405,15 +405,13 @@ class BaseTrainer:
 
     def preprocess_batch(self, batch):
         """
-        Allows custom preprocessing model inputs and ground truths depending on task type
+        > Allows custom preprocessing model inputs and ground truths depending on task type.
         """
         return batch
 
     def validate(self):
         """
-        Runs validation on test set using self.validator.
-        # TODO: discuss validator class. Enforce that a validator metrics dict should contain
-        "fitness" metric.
+        > Runs validation on test set using self.validator. The returned dict is expected to contain "fitness" key.
         """
         metrics = self.validator(self)
         fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy())  # use loss as fitness measure if not found
@@ -423,9 +421,11 @@ class BaseTrainer:
 
     def log(self, text, rank=-1):
         """
-        Logs the given text to given ranks process if provided, otherwise logs to all ranks
-        :param text: text to log
-        :param rank: List[Int]
+        > Logs the given text to given ranks process if provided, otherwise logs to all ranks.
+
+        Args"
+            text (str): text to log
+            rank (List[Int]): process rank
 
         """
         if rank in {-1, 0}:
@@ -439,13 +439,13 @@ class BaseTrainer:
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0):
         """
-        Returns dataloader derived from torch.data.Dataloader
+        > Returns dataloader derived from torch.data.Dataloader.
         """
         raise NotImplementedError("get_dataloader function not implemented in trainer")
 
     def criterion(self, preds, batch):
         """
-        Returns loss and individual loss items as Tensor
+        > Returns loss and individual loss items as Tensor.
         """
         raise NotImplementedError("criterion function not implemented in trainer")
 
@@ -531,7 +531,7 @@ class BaseTrainer:
     @staticmethod
     def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
         """
-        Builds an optimizer with the specified parameters and parameter groups.
+        > Builds an optimizer with the specified parameters and parameter groups.
 
         Args:
             model (nn.Module): model to optimize