ultralytics 8.4.39 Use dashed incremented run paths (#24193)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/usage/cfg.md

@@ -168,7 +168,7 @@ Effective management of these aspects helps track progress and makes debugging a
 | Argument   | Default  | Description                                                                                                                                                                                                                                                                                               |
 | ---------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `project`  | `'runs'` | Specifies the root directory for saving training runs. Each run is saved in a separate subdirectory.                                                                                                                                                                                                      |
-| `name`     | `'exp'`  | Defines the experiment name. If unspecified, YOLO increments this name for each run (e.g., `exp`, `exp2`) to avoid overwriting.                                                                                                                                                                           |
+| `name`     | `'exp'`  | Defines the experiment name. If unspecified, YOLO increments this name for each run (e.g., `exp`, `exp-2`) to avoid overwriting.                                                                                                                                                                          |
 | `exist_ok` | `False`  | Determines whether to overwrite an existing experiment directory. `True` allows overwriting; `False` prevents it.                                                                                                                                                                                         |
 | `plots`    | `True`   | Controls the generation and saving of training and validation plots. Set to `True` to create plots like loss curves, [precision](https://www.ultralytics.com/glossary/precision)-[recall](https://www.ultralytics.com/glossary/recall) curves, and sample predictions for visual tracking of performance. |
 | `save`     | `True`   | Enables saving training checkpoints and final model weights. Set to `True` to save model states periodically, allowing training resumption or model deployment.                                                                                                                                           |

docs/en/yolov5/tutorials/model_ensembling.md

@@ -72,7 +72,7 @@ Multiple pretrained models can be ensembled together at test and inference time
 python val.py --weights yolov5x.pt yolov5l6.pt --data coco.yaml --img 640 --half
 ```
 
-You can list as many checkpoints as you would like, including custom weights such as `runs/train/exp5/weights/best.pt`. YOLOv5 will automatically run each model, align the predictions on a per-image basis, and average the outputs before performing NMS.
+You can list as many checkpoints as you would like, including custom weights such as `runs/train/exp-5/weights/best.pt`. YOLOv5 will automatically run each model, align the predictions on a per-image basis, and average the outputs before performing NMS.
 
 Output:
 
@@ -91,7 +91,7 @@ val: Scanning '../datasets/coco/val2017.cache' images and labels... 4952 found,
                  all       5000      36335      0.747      0.637      0.692      0.502
 Speed: 0.1ms pre-process, 39.5ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)  # <--- ensemble speed
 
-Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
+Evaluating pycocotools mAP... saving runs/val/exp-3/yolov5x_predictions.json...
 ...
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.515  # <--- ensemble mAP
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.699
@@ -128,7 +128,7 @@ Ensemble created with ['yolov5x.pt', 'yolov5l6.pt']
 
 image 1/2 /content/yolov5/data/images/bus.jpg: 640x512 4 persons, 1 bus, 1 tie, Done. (0.063s)
 image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 3 persons, 2 ties, Done. (0.056s)
-Results saved to runs/detect/exp2
+Results saved to runs/detect/exp-2
 Done. (0.223s)
 ```
 

docs/en/yolov5/tutorials/model_pruning_and_sparsity.md

@@ -50,7 +50,7 @@ val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 f
                  all       5000      36335      0.732      0.628      0.683      0.496
 Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640)  # <--- base speed
 
-Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json...
+Evaluating pycocotools mAP... saving runs/val/exp-2/yolov5x_predictions.json...
 ...
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.507  # <--- base mAP
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.689
@@ -87,7 +87,7 @@ val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 f
                  all       5000      36335      0.724      0.614      0.671      0.478
 Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640)  # <--- prune speed
 
-Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
+Evaluating pycocotools mAP... saving runs/val/exp-3/yolov5x_predictions.json...
 ...
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.489  # <--- prune mAP
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.677
@@ -101,7 +101,7 @@ Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.496
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803
-Results saved to runs/val/exp3
+Results saved to runs/val/exp-3
 ```
 
 ## Results Analysis

docs/en/yolov5/tutorials/test_time_augmentation.md

@@ -81,7 +81,7 @@ val: New cache created: ../datasets/coco/val2017.cache
                  all       5000      36335      0.718      0.656      0.695      0.503
 Speed: 0.2ms pre-process, 80.6ms inference, 2.7ms NMS per image at shape (32, 3, 832, 832)  # <--- TTA speed
 
-Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json...
+Evaluating pycocotools mAP... saving runs/val/exp-2/yolov5x_predictions.json...
 ...
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.516  # <--- TTA mAP
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.701

docs/en/yolov5/tutorials/train_custom_data.md

@@ -170,7 +170,7 @@ python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yo
 
     💡 Always train using datasets stored locally. Accessing data from network drives (like Google Drive) or remote storage can be significantly slower and impede training performance. Copying your dataset to a local SSD is often the best practice.
 
-All training outputs, including weights and logs, are saved in the `runs/train/` directory. Each training session creates a new subdirectory (e.g., `runs/train/exp`, `runs/train/exp2`, etc.). For an interactive, hands-on experience, explore the training section in our official tutorial notebooks: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+All training outputs, including weights and logs, are saved in the `runs/train/` directory. Each training session creates a new subdirectory (e.g., `runs/train/exp`, `runs/train/exp-2`, etc.). For an interactive, hands-on experience, explore the training section in our official tutorial notebooks: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
 
 ## 4. Visualize
 

tests/test_python.py

@@ -549,7 +549,7 @@ def test_utils_ops():
 
 def test_utils_files(tmp_path):
     """Test file handling utilities including file age, date, and paths with spaces."""
-    from ultralytics.utils.files import file_age, file_date, get_latest_run, spaces_in_path
+    from ultralytics.utils.files import file_age, file_date, get_latest_run, increment_path, spaces_in_path
 
     file_age(SOURCE)
     file_date(SOURCE)
@@ -560,6 +560,14 @@ def test_utils_files(tmp_path):
     with spaces_in_path(path) as new_path:
         print(new_path)
 
+    exp_dir = tmp_path / "runs" / "exp"
+    exp_dir.mkdir(parents=True)
+    assert increment_path(exp_dir) == tmp_path / "runs" / "exp-2"
+
+    results_file = exp_dir / "results.txt"
+    results_file.touch()
+    assert increment_path(results_file) == exp_dir / "results-2.txt"
+
 
 @pytest.mark.slow
 def test_utils_patches_torch_save(tmp_path):

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
 
-__version__ = "8.4.38"
+__version__ = "8.4.39"
 
 import importlib
 import os

ultralytics/utils/files.py

@@ -103,7 +103,7 @@ def spaces_in_path(path: str | Path):
         yield path
 
 
-def increment_path(path: str | Path, exist_ok: bool = False, sep: str = "", mkdir: bool = False) -> Path:
+def increment_path(path: str | Path, exist_ok: bool = False, sep: str = "-", mkdir: bool = False) -> Path:
     """Increment a file or directory path, i.e., runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
 
     If the path exists and `exist_ok` is not True, the path will be incremented by appending a number and `sep` to the
@@ -125,13 +125,13 @@ def increment_path(path: str | Path, exist_ok: bool = False, sep: str = "", mkdi
         >>> path = Path("runs/exp")
         >>> new_path = increment_path(path)
         >>> print(new_path)
-        runs/exp2
+        runs/exp-2
 
         Increment a file path:
         >>> path = Path("runs/exp/results.txt")
         >>> new_path = increment_path(path)
         >>> print(new_path)
-        runs/exp/results2.txt
+        runs/exp/results-2.txt
     """
     path = Path(path)  # os-agnostic
     if path.exists() and not exist_ok: