ultralytics 8.0.57 Comet, AMP, Classify, Docker updates (#1601)

Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = '8.0.56'
+__version__ = '8.0.57'
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils.checks import check_yolo as checks

ultralytics/yolo/cfg/default.yaml

@@ -1,8 +1,8 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 # Default training settings and hyperparameters for medium-augmentation COCO training
 
-task: detect  # inference task, i.e. detect, segment, classify
-mode: train  # YOLO mode, i.e. train, val, predict, export
+task: detect  # YOLO task, i.e. detect, segment, classify, pose
+mode: train  # YOLO mode, i.e. train, val, predict, export, track, benchmark
 
 # Train settings -------------------------------------------------------------------------------------------------------
 model:  # path to model file, i.e. yolov8n.pt, yolov8n.yaml
@@ -30,6 +30,7 @@ rect: False  # support rectangular training if mode='train', support rectangular
 cos_lr: False  # use cosine learning rate scheduler
 close_mosaic: 10  # disable mosaic augmentation for final 10 epochs
 resume: False  # resume training from last checkpoint
+amp: True  # Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
 # Segmentation
 overlap_mask: True  # masks should overlap during training (segment train only)
 mask_ratio: 4  # mask downsample ratio (segment train only)

ultralytics/yolo/data/utils.py

@@ -207,12 +207,20 @@ def check_det_dataset(dataset, autodownload=True):
         data = yaml_load(data, append_filename=True)  # dictionary
 
     # Checks
-    for k in 'train', 'val', 'names':
+    for k in 'train', 'val':
         if k not in data:
             raise SyntaxError(
-                emojis(f"{dataset} '{k}:' key missing ❌.\n'train', 'val' and 'names' are required in all data YAMLs."))
+                emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs."))
+    if 'names' not in data and 'nc' not in data:
+        raise SyntaxError(emojis(f"{dataset} key missing ❌.\n either 'names' or 'nc' are required in all data YAMLs."))
+    if 'names' in data and 'nc' in data and len(data['names']) != data['nc']:
+        raise SyntaxError(emojis(f"{dataset} 'names' length {len(data['names'])} and 'nc: {data['nc']}' must match."))
+    if 'names' not in data:
+        data['names'] = [f'class_{i}' for i in range(data['nc'])]
+    else:
+        data['nc'] = len(data['names'])
+
     data['names'] = check_class_names(data['names'])
-    data['nc'] = len(data['names'])
 
     # Resolve paths
     path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent)  # dataset root

ultralytics/yolo/engine/model.py

@@ -142,6 +142,7 @@ class YOLO:
             self.task = task or guess_model_task(weights)
             self.ckpt_path = weights
         self.overrides['model'] = weights
+        self.overrides['task'] = self.task
 
     def _check_is_pytorch_model(self):
         """

ultralytics/yolo/engine/trainer.py

@@ -203,8 +203,8 @@ class BaseTrainer:
         self.model = self.model.to(self.device)
         self.set_model_attributes()
         # Check AMP
-        self.amp = torch.tensor(True).to(self.device)
-        if RANK in (-1, 0):  # Single-GPU and DDP
+        self.amp = torch.tensor(self.args.amp).to(self.device)  # True or False
+        if self.amp and RANK in (-1, 0):  # Single-GPU and DDP
             callbacks_backup = callbacks.default_callbacks.copy()  # backup callbacks as check_amp() resets them
             self.amp = torch.tensor(check_amp(self.model), device=self.device)
             callbacks.default_callbacks = callbacks_backup  # restore callbacks

ultralytics/yolo/utils/callbacks/comet.py

@@ -14,6 +14,7 @@ except (ImportError, AssertionError):
 def on_pretrain_routine_start(trainer):
     try:
         experiment = comet_ml.Experiment(project_name=trainer.args.project or 'YOLOv8')
+        experiment.set_name(trainer.args.name)
         experiment.log_parameters(vars(trainer.args))
     except Exception as e:
         LOGGER.warning(f'WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}')