Add Classification model YAML support (#154)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/yolo/data/utils.py

@@ -255,12 +255,28 @@ def check_dataset_yaml(data, autodownload=True):
 
 
 def check_dataset(dataset: str):
-    data = Path.cwd() / "datasets" / dataset
-    data_dir = data if data.is_dir() else (Path.cwd() / data)
+    """
+    Check a classification dataset such as Imagenet.
+
+    Copy code
+    This function takes a `dataset` name as input and returns a dictionary containing information about the dataset.
+    If the dataset is not found, it attempts to download the dataset from the internet and save it to the local file system.
+
+    Args:
+        dataset (str): Name of the dataset.
+
+    Returns:
+        data (dict): A dictionary containing the following keys and values:
+            'train': Path object for the directory containing the training set of the dataset
+            'val': Path object for the directory containing the validation set of the dataset
+            'nc': Number of classes in the dataset
+            'names': List of class names in the dataset
+    """
+    data_dir = (Path.cwd() / "datasets" / dataset).resolve()
     if not data_dir.is_dir():
         LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
         t = time.time()
-        if str(data) == 'imagenet':
+        if dataset == 'imagenet':
             subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
         else:
             url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
@@ -271,5 +287,4 @@ def check_dataset(dataset: str):
     test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val'  # data/test or data/val
     nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
     names = [name for name in os.listdir(data_dir / 'train') if os.path.isdir(data_dir / 'train' / name)]
-    data = {"train": train_set, "val": test_set, "nc": nc, "names": names}
-    return data
+    return {"train": train_set, "val": test_set, "nc": nc, "names": names}

ultralytics/yolo/engine/model.py

@@ -103,13 +103,9 @@ class YOLO:
         Args:
             verbose (bool): Controls verbosity.
         """
-        if not self.model:
-            LOGGER.info("model not initialized!")
         self.model.info(verbose=verbose)
 
     def fuse(self):
-        if not self.model:
-            LOGGER.info("model not initialized!")
         self.model.fuse()
 
     @smart_inference_mode()
@@ -139,9 +135,6 @@ class YOLO:
             data (str): The dataset to validate on. Accepts all formats accepted by yolo
             **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
         """
-        if not self.model:
-            raise ModuleNotFoundError("model not initialized!")
-
         overrides = self.overrides.copy()
         overrides.update(kwargs)
         overrides["mode"] = "val"
@@ -177,8 +170,6 @@ class YOLO:
             **kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section.
                             You can pass all arguments as a yaml file in `cfg`. Other args are ignored if `cfg` file is passed
         """
-        if not self.model:
-            raise AttributeError("model not initialized. Use .new() or .load()")
         overrides = self.overrides.copy()
         overrides.update(kwargs)
         if kwargs.get("cfg"):
@@ -193,10 +184,8 @@ class YOLO:
 
         self.trainer = self.TrainerClass(overrides=overrides)
         if not overrides.get("resume"):  # manually set model only if not resuming
-            self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None,
-                                                        cfg=self.model.yaml if self.task != "classify" else None)
+            self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
             self.model = self.trainer.model
-
         self.trainer.train()
 
     def to(self, device):

ultralytics/yolo/v8/classify/train.py

@@ -1,5 +1,3 @@
-from pathlib import Path
-
 import hydra
 import torch
 import torchvision
@@ -13,7 +11,9 @@ from ultralytics.yolo.utils import DEFAULT_CONFIG
 
 class ClassificationTrainer(BaseTrainer):
 
-    def __init__(self, config=DEFAULT_CONFIG, overrides={}):
+    def __init__(self, config=DEFAULT_CONFIG, overrides=None):
+        if overrides is None:
+            overrides = {}
         overrides["task"] = "classify"
         super().__init__(config, overrides)
 
@@ -25,6 +25,10 @@ class ClassificationTrainer(BaseTrainer):
         if weights:
             model.load(weights)
 
+        # Update defaults
+        if self.args.imgsz == 640:
+            self.args.imgsz = 224
+
         return model
 
     def setup_model(self):
@@ -36,22 +40,17 @@ class ClassificationTrainer(BaseTrainer):
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return
 
-        model = self.model
-        pretrained = False
+        model = str(self.model)
         # Load a YOLO model locally, from torchvision, or from Ultralytics assets
         if model.endswith(".pt"):
-            model = model.split(".")[0]
-            pretrained = True
-        else:
+            self.model = attempt_load_weights(model, device='cpu')
+        elif model.endswith(".yaml"):
             self.model = self.get_model(cfg=model)
-
-        # order: check local file -> torchvision assets -> ultralytics asset
-        if Path(f"{model}.pt").is_file():  # local file
-            self.model = attempt_load_weights(f"{model}.pt", device='cpu')
         elif model in torchvision.models.__dict__:
+            pretrained = True
             self.model = torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
         else:
-            self.model = attempt_load_weights(f"{model}.pt", device='cpu')
+            FileNotFoundError(f'ERROR: model={model} not found locally or online. Please check model name.')
 
         return  # dont return ckpt. Classification doesn't support resume
 
@@ -66,6 +65,10 @@ class ClassificationTrainer(BaseTrainer):
         batch["cls"] = batch["cls"].to(self.device)
         return batch
 
+    def progress_string(self):
+        return ('\n' + '%11s' *
+                (4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
+
     def get_validator(self):
         return v8.classify.ClassificationValidator(self.test_loader, self.save_dir, logger=self.console)
 
@@ -73,9 +76,6 @@ class ClassificationTrainer(BaseTrainer):
         loss = torch.nn.functional.cross_entropy(preds, batch["cls"])
         return loss, loss
 
-    def check_resume(self):
-        pass
-
     def resume_training(self, ckpt):
         pass
 
@@ -85,10 +85,13 @@ class ClassificationTrainer(BaseTrainer):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "resnet18"
+    cfg.model = cfg.model or "yolov8n-cls.yaml"  # or "resnet18"
     cfg.data = cfg.data or "imagenette160"  # or yolo.ClassificationDataset("mnist")
-    trainer = ClassificationTrainer(cfg)
-    trainer.train()
+    # trainer = ClassificationTrainer(cfg)
+    # trainer.train()
+    from ultralytics import YOLO
+    model = YOLO(cfg.model)
+    model.train(**cfg)
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/models/cls/yolov8l-cls.yaml

@@ -0,0 +1,23 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 1000  # number of classes
+depth_multiple: 1.00  # scales module repeats
+width_multiple: 1.00  # scales convolution channels
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]]

ultralytics/yolo/v8/models/cls/yolov8m-cls.yaml

@@ -0,0 +1,23 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 1000  # number of classes
+depth_multiple: 0.67  # scales module repeats
+width_multiple: 0.75  # scales convolution channels
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]]

ultralytics/yolo/v8/models/cls/yolov8n-cls.yaml

@@ -0,0 +1,23 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 1000  # number of classes
+depth_multiple: 0.33  # scales module repeats
+width_multiple: 0.25  # scales convolution channels
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]]

ultralytics/yolo/v8/models/cls/yolov8s-cls.yaml

@@ -0,0 +1,23 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 1000  # number of classes
+depth_multiple: 0.33  # scales module repeats
+width_multiple: 0.50  # scales convolution channels
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]]

ultralytics/yolo/v8/models/cls/yolov8x-cls.yaml

@@ -0,0 +1,23 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 1000  # number of classes
+depth_multiple: 1.00  # scales module repeats
+width_multiple: 1.25  # scales convolution channels
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]]