Feat: Added new rule in config auto-population and updated documentation.

configs/README.md

@@ -223,10 +223,8 @@ Here you can change everything related to actual training of the model.
 | `verbose`                 | `bool`                                         | `True`        | Print all intermediate results to console                                                                                                        |
 | `pin_memory`              | `bool`                                         | `True`        | Whether to pin memory in the `DataLoader`                                                                                                        |
 | `save_top_k`              | `-1 \| NonNegativeInt`                         | `3`           | Save top K checkpoints based on validation loss when training                                                                                    |
-| `smart_cfg_auto_populate` | `bool`                                         | `True`        | Automatically populate sensible default values for missing config fields and log warnings                                                        |
 | `n_validation_batches`    | `PositiveInt \| None`                          | `None`        | Limits the number of validation/test batches and makes the val/test loaders deterministic                                                        |
-
-**Example:**
+| `smart_cfg_auto_populate` | `bool`                                         | `True`        | Automatically populate sensible default values for missing config fields and log warnings                                                        |
 
 ```yaml
 
@@ -247,8 +245,33 @@ trainer:
   skip_last_batch: true
   log_sub_losses: true
   save_top_k: 3
+  smart_cfg_auto_populate: true
 ```
 
+### Smart Configuration Auto-population
+
+When setting `trainer.smart_cfg_auto_populate = True`, the following set of rules will be applied:
+
+#### Auto-population Rules
+
+1. **Default Optimizer and Scheduler:**
+
+   - If `training_strategy` is not defined and neither `optimizer` nor `scheduler` is set, the following defaults are applied:
+     - Optimizer: `Adam`
+     - Scheduler: `ConstantLR`
+
+1. **CosineAnnealingLR Adjustment:**
+
+   - If the `CosineAnnealingLR` scheduler is used and `T_max` is not set, it is automatically set to the number of epochs.
+
+1. **Mosaic4 Augmentation:**
+
+   - If `Mosaic4` augmentation is used without `out_width` and `out_height` parameters, they are set to match the training image size.
+
+1. **Validation/Test Views:**
+
+   - If `train_view`, `val_view`, and `test_view` are the same, and `n_validation_batches` is not explicitly set, it defaults to `10` to prevent validation/testing on the entire training set.
+
 ### Preprocessing
 
 We use [`Albumentations`](https://albumentations.ai/docs/) library for `augmentations`. [Here](https://albumentations.ai/docs/api_reference/full_reference/#pixel-level-transforms) you can see a list of all pixel level augmentations supported, and [here](https://albumentations.ai/docs/api_reference/full_reference/#spatial-level-transforms) you see all spatial level transformations. In the configuration you can specify any augmentation from these lists and their parameters.

configs/detection_heavy_model.yaml

@@ -6,6 +6,11 @@ model:
     name: DetectionModel
     params:
       variant: heavy
+      loss_params:
+        iou_type: "siou"
+        n_warmup_epochs: 0 # No assigner warmup
+        iou_loss_weight: 20 # Should be 2.5 * accumulate_grad_batches for best results
+        class_loss_weight: 8 # Should be 1 * accumulate_grad_batches for best results
 
 loader:
   params:
@@ -19,27 +24,29 @@ trainer:
       active: true
 
   batch_size: 8
-  epochs: &epochs 200
-  n_workers: 4
+  epochs: &epochs 300
+  accumulate_grad_batches: 8 # For best results, always accumulate gradients to effectively use 64 batch size
+  n_workers: 8
   validation_interval: 10
   n_log_images: 8
 
   callbacks:
+    - name: EMACallback
+      params:
+        decay: 0.9999 
+        use_dynamic_decay: True 
+        decay_tau: 2000
     - name: ExportOnTrainEnd
     - name: TestOnTrainEnd
 
-  optimizer:
-    name: SGD
-    params:
+  training_strategy: # Training from scratch params
+    name: "TripleLRSGDStrategy"
+    params: 
+      warmup_epochs: 3
+      warmup_bias_lr: 0.1
+      warmup_momentum: 0.8
       lr: 0.01
-      momentum: 0.937
-      weight_decay: 0.0005
-      dampening: 0.0
-      nesterov: true
-
-  scheduler:
-    name: CosineAnnealingLR
-    params:
-      T_max: *epochs
-      eta_min: 0.0001
-      last_epoch: -1
+      lre: 0.0001
+      momentum: 0.937     
+      weight_decay: 0.0005     
+      nesterov: True

configs/detection_light_model.yaml

@@ -7,6 +7,11 @@ model:
     name: DetectionModel
     params:
       variant: light
+      loss_params:
+        iou_type: "siou"
+        n_warmup_epochs: 0 # No assigner warmup
+        iou_loss_weight: 20 # Should be 2.5 * accumulate_grad_batches for best results
+        class_loss_weight: 8 # Should be 1 * accumulate_grad_batches for best results
 
 loader:
   params:
@@ -20,7 +25,8 @@ trainer:
       active: true
 
   batch_size: 8
-  epochs: &epochs 200
+  epochs: &epochs 300
+  accumulate_grad_batches: 8 # For best results, always accumulate gradients to effectively use 64 batch size
   n_workers: 8
   validation_interval: 10
   n_log_images: 8
@@ -34,17 +40,14 @@ trainer:
     - name: ExportOnTrainEnd
     - name: TestOnTrainEnd
 
-  optimizer:
-    name: SGD
-    params:
-      lr: 0.0032           
-      momentum: 0.843      
-      weight_decay: 0.00036 
-      dampening: 0.0        
-      nesterov: true    
-
-  scheduler:
-    name: CosineAnnealingLR
-    params:
-      T_max: *epochs         
-      eta_min: 0.000384
+  training_strategy: # Fine tuning params
+    name: "TripleLRSGDStrategy"
+    params: 
+      warmup_epochs: 2
+      warmup_bias_lr: 0.05
+      warmup_momentum: 0.5
+      lr: 0.0032
+      lre: 0.000384
+      momentum: 0.843     
+      weight_decay: 0.00036   
+      nesterov: True

luxonis_train/config/config.py

@@ -580,6 +580,19 @@ def smart_auto_populate(cls, instance: "Config") -> None:
                     "`Mosaic4` augmentation detected. Automatically set `out_width` and `out_height` to match `train_image_size`."
                 )
 
+        # Rule: If train, val, and test views are the same, set n_validation_batches
+        if (
+            instance.loader.train_view
+            == instance.loader.val_view
+            == instance.loader.test_view
+            and instance.trainer.n_validation_batches is None
+        ):
+            instance.trainer.n_validation_batches = 10
+            logger.warning(
+                "Train, validation, and test views are the same. Automatically set `n_validation_batches` to 10 to prevent validation/testing on the full train set. "
+                "If this behavior is not desired, set `smart_cfg_auto_populate` to `False`."
+            )
+
 
 def is_acyclic(graph: dict[str, list[str]]) -> bool:
     """Tests if graph is acyclic.

luxonis_train/config/predefined_models/README.md

@@ -60,6 +60,10 @@ The `DetectionModel` allows for both `"light"` and `"heavy"` variants, where the
 
 See an example configuration file using this predefined model [here](../../../configs/detection_light_model.yaml) for the `"light"` variant, and [here](../../../configs/detection_heavy_model.yaml) for the `"heavy"` variant.
 
+This detection model is based on [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/pdf/2209.02976.pdf). The pretrained `"light"` model on COCOtrain2017 dataset achieves **36.1% mAP** on the COCOval2017 dataset.
+
+**Note**: To align with the evaluation procedure used by other state-of-the-art (SOTA) models, we adopted a small `conf_thresh` (e.g., `0.03`) and a high `iou_thresh` (e.g., `0.65`) during validation.
+
 **Components:**
 
 | Name                                                                                     | Alias                    | Function                                                                                                 |