Simplify argument names (#141)

docs/config.md

@@ -60,7 +60,7 @@ task.
 | device          | ''      | cuda device, i.e. 0 or 0,1,2,3 or cpu. `''` selects available cuda 0 device |
 | epochs          | 100     | Number of epochs to train                                                   |
 | workers         | 8       | Number of cpu workers used per process. Scales automatically with DDP       |
-| batch_size      | 16      | Batch size of the dataloader                                                |
+| batch           | 16      | Batch size of the dataloader                                                |
 | imgsz           | 640     | Image size of data in dataloader                                            |
 | optimizer       | SGD     | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp`           |
 | single_cls      | False   | Train on multi-class data as single-class                                   |
@@ -129,8 +129,8 @@ validation dataset and to detect and prevent overfitting.
 | noval       | `False` | ???                               |
 | save_json   | `False` |                                   |
 | save_hybrid | `False` |                                   |
-| conf_thres  | `0.001` | Confidence threshold              |
-| iou_thres   | `0.6`   | IoU threshold                     |
+| conf        | `0.001` | Confidence threshold              |
+| iou         | `0.6`   | IoU threshold                     |
 | max_det     | `300`   | Maximum number of detections      |
 | half        | `True`  | Use .half() mode.                 |
 | dnn         | `False` | Use OpenCV DNN for ONNX inference |

ultralytics/yolo/configs/default.yaml

@@ -9,7 +9,7 @@ model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
 data: null # i.e. coco128.yaml. Path to data file
 epochs: 100 # number of epochs to train for
 patience: 50  # TODO: epochs to wait for no observable improvement for early stopping of training
-batch_size: 16 # number of images per batch
+batch: 16 # number of images per batch
 imgsz: 640 # size of input images
 save: True # save checkpoints
 cache: False # True/ram, disk or False. Use cache for data loading
@@ -23,7 +23,6 @@ optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
 verbose: False # whether to print verbose output
 seed: 0 # random seed for reproducibility
 deterministic: True # whether to enable deterministic mode
-local_rank: -1 # local rank for distributed training
 single_cls: False # train multi-class data as single-class
 image_weights: False # use weighted image selection for training
 rect: False # support rectangular training
@@ -40,8 +39,8 @@ dropout: False # use dropout regularization
 val: True # validate/test during training
 save_json: False # save results to JSON file
 save_hybrid: False # save hybrid version of labels (labels + additional predictions)
-conf_thres: 0.001 # object confidence threshold for detection
-iou_thres: 0.7 # intersection over union threshold for NMS
+conf: 0.001 # object confidence threshold for detection
+iou: 0.7 # intersection over union (IoU) threshold for NMS
 max_det: 300 # maximum number of detections per image
 half: False # use half precision (FP16)
 dnn: False # use OpenCV DNN for ONNX inference
@@ -57,7 +56,6 @@ hide_labels: False # hide labels
 hide_conf: False # hide confidence scores
 vid_stride: 1 # video frame-rate stride
 line_thickness: 3 # bounding box thickness (pixels)
-update: False # Update all models
 visualize: False # visualize results
 augment: False # apply data augmentation to images
 agnostic_nms: False # class-agnostic NMS

ultralytics/yolo/engine/exporter.py

@@ -164,14 +164,14 @@ class Exporter:
             assert not self.args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic'
 
         # Checks
-        # if self.args.batch_size == model.args['batch_size']:  # user has not modified training batch_size
-        self.args.batch_size = 1
+        # if self.args.batch == model.args['batch_size']:  # user has not modified training batch_size
+        self.args.batch = 1
         self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
         if self.args.optimize:
             assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
 
         # Input
-        im = torch.zeros(self.args.batch_size, 3, *self.imgsz).to(self.device)
+        im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
         file = Path(getattr(model, 'pt_path', None) or model.yaml['yaml_file'])
         if file.suffix == '.yaml':
             file = Path(file.name)

ultralytics/yolo/engine/trainer.py

@@ -102,7 +102,7 @@ class BaseTrainer:
             yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True))  # save run args
         self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt'  # checkpoint paths
 
-        self.batch_size = self.args.batch_size
+        self.batch_size = self.args.batch
         self.epochs = self.args.epochs
         self.start_epoch = 0
         if RANK == -1:

ultralytics/yolo/engine/validator.py

@@ -87,18 +87,18 @@ class BaseValidator:
             callbacks.add_integration_callbacks(self)
             self.run_callbacks('on_val_start')
             assert model is not None, "Either trainer or model is needed for validation"
-            self.device = select_device(self.args.device, self.args.batch_size)
+            self.device = select_device(self.args.device, self.args.batch)
             self.args.half &= self.device.type != 'cpu'
             model = AutoBackend(model, device=self.device, dnn=self.args.dnn, fp16=self.args.half)
             self.model = model
             stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
             imgsz = check_imgsz(self.args.imgsz, stride=stride)
             if engine:
-                self.args.batch_size = model.batch_size
+                self.args.batch = model.batch_size
             else:
                 self.device = model.device
                 if not pt and not jit:
-                    self.args.batch_size = 1  # export.py models default to batch-size 1
+                    self.args.batch = 1  # export.py models default to batch-size 1
                     self.logger.info(
                         f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
 
@@ -110,7 +110,7 @@ class BaseValidator:
             if self.device.type == 'cpu':
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
             self.dataloader = self.dataloader or \
-                              self.get_dataloader(data.get("val") or data.set("test"), self.args.batch_size)
+                              self.get_dataloader(data.get("val") or data.set("test"), self.args.batch)
             self.data = data
 
         model.eval()

ultralytics/yolo/v8/detect/predict.py

@@ -20,8 +20,8 @@ class DetectionPredictor(BasePredictor):
 
     def postprocess(self, preds, img, orig_img):
         preds = ops.non_max_suppression(preds,
-                                        self.args.conf_thres,
-                                        self.args.iou_thres,
+                                        self.args.conf,
+                                        self.args.iou,
                                         agnostic=self.args.agnostic_nms,
                                         max_det=self.args.max_det)
 

ultralytics/yolo/v8/detect/val.py

@@ -58,8 +58,8 @@ class DetectionValidator(BaseValidator):
 
     def postprocess(self, preds):
         preds = ops.non_max_suppression(preds,
-                                        self.args.conf_thres,
-                                        self.args.iou_thres,
+                                        self.args.conf,
+                                        self.args.iou,
                                         labels=self.lb,
                                         multi_label=True,
                                         agnostic=self.args.single_cls,

ultralytics/yolo/v8/segment/predict.py

@@ -14,8 +14,8 @@ class SegmentationPredictor(DetectionPredictor):
         masks = []
         # TODO: filter by classes
         p = ops.non_max_suppression(preds[0],
-                                    self.args.conf_thres,
-                                    self.args.iou_thres,
+                                    self.args.conf,
+                                    self.args.iou,
                                     agnostic=self.args.agnostic_nms,
                                     max_det=self.args.max_det,
                                     nm=32)

ultralytics/yolo/v8/segment/val.py

@@ -60,8 +60,8 @@ class SegmentationValidator(DetectionValidator):
 
     def postprocess(self, preds):
         p = ops.non_max_suppression(preds[0],
-                                    self.args.conf_thres,
-                                    self.args.iou_thres,
+                                    self.args.conf,
+                                    self.args.iou,
                                     labels=self.lb,
                                     multi_label=True,
                                     agnostic=self.args.single_cls,