退购1.1定位算法

docs/usage/engine.md

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+---
+comments: true
+description: Learn how to train and customize your models fast with the Ultralytics YOLO 'DetectionTrainer' and 'CustomTrainer'. Read more here!
+---
+
+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 overriding
+the required functions or operations as long the as correct formats are followed. For example, you can support your own
+custom model and dataloader by just overriding these functions:
+
+* `get_model(cfg, weights)` - The function that builds the model to be trained
+* `get_dataloder()` - The function that builds the dataloader
+  More details and source code can be found in [`BaseTrainer` Reference](../reference/yolo/engine/trainer.md)
+
+## DetectionTrainer
+
+Here's how you can use the YOLOv8 `DetectionTrainer` and customize it.
+
+```python
+from ultralytics.yolo.v8.detect 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 ultralytics.yolo.v8.detect 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 ultralytics.yolo.v8.detect 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](callbacks.md)
+
+## Other engine components
+
+There are other components that can be customized similarly like `Validators` and `Predictors`
+See Reference section for more information on these.