`ultralytics 8.0.116` NAS, DVC, YOLOv5u updates (#3124)

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

docs/models/yolo-nas.md

@@ -3,7 +3,7 @@ comments: true
 description: Dive into YOLO-NAS, Deci's next-generation object detection model, offering breakthroughs in speed and accuracy. Learn how to utilize pre-trained models using the Ultralytics Python API for various tasks.
 ---
 
-# Deci's YOLO-NAS
+# YOLO-NAS
 
 ## Overview
 

docs/models/yolov5.md

@@ -33,7 +33,7 @@ YOLOv5u is an enhanced version of the [YOLOv5](https://github.com/ultralytics/yo
 | Validation | :heavy_check_mark: |
 | Training   | :heavy_check_mark: |
 
-??? Performance
+!!! Performance
 
     === "Detection"
 
@@ -45,11 +45,11 @@ YOLOv5u is an enhanced version of the [YOLOv5](https://github.com/ultralytics/yo
         | [YOLOv5lu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5lu.pt)   | 640                   | 52.2                 | 408.4                          | 2.50                                | 53.2               | 135.0             |
         | [YOLOv5xu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5xu.pt)   | 640                   | 53.2                 | 763.2                          | 3.81                                | 97.2               | 246.4             |
         |                                                                                          |                       |                      |                                |                                     |                    |                   |
-        | [YOLOv5n6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | 1280                  | 42.1                 | -                              | -                                   | 4.3                | 7.8               |
+        | [YOLOv5n6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | 1280                  | 42.1                 | 211.0                          | 1.83                                | 4.3                | 7.8               |
-        | [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280                  | 48.6                 | -                              | -                                   | 15.3               | 24.6              |
+        | [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280                  | 48.6                 | 422.6                          | 2.34                                | 15.3               | 24.6              |
-        | [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280                  | 53.6                 | -                              | -                                   | 41.2               | 65.7              |
+        | [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280                  | 53.6                 | 810.9                          | 4.36                                | 41.2               | 65.7              |
-        | [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280                  | 55.7                 | -                              | -                                   | 86.1               | 137.4             |
+        | [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280                  | 55.7                 | 1470.9                         | 5.47                                | 86.1               | 137.4             |
-        | [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280                  | 56.8                 | -                              | -                                   | 155.4              | 250.7             |
+        | [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280                  | 56.8                 | 2436.5                         | 8.98                                | 155.4              | 250.7             |
 
 ## Usage
 

docs/models/yolov8.md

@@ -35,7 +35,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
 | Validation | :heavy_check_mark: |
 | Training   | :heavy_check_mark: |
 
-??? Performance
+!!! Performance
 
     === "Detection"
 

docs/modes/track.md

@@ -3,7 +3,7 @@ comments: true
 description: Explore YOLOv8n-based object tracking with Ultralytics' BoT-SORT and ByteTrack. Learn configuration, usage, and customization tips.
 ---
 
-<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
+<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png">
 
 Object tracking is a task that involves identifying the location and class of objects, then assigning a unique ID to
 that detection in video streams.

docs/yolov5/tutorials/roboflow_datasets_integration.md

@@ -6,7 +6,11 @@ description: Use Roboflow to organize, label, prepare, version & host datasets f
 # Roboflow Datasets
 
 You can now use Roboflow to organize, label, prepare, version, and host your datasets for training YOLOv5 🚀 models. Roboflow is free to use with YOLOv5 if you make your workspace public.  
-UPDATED 30 September 2021.
+UPDATED 7 June 2023.
+
+!!! warning
+
+    Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.
 
 ## Upload
 

docs/yolov5/tutorials/train_custom_data.md

@@ -4,7 +4,7 @@ description: Train your custom dataset with YOLOv5. Learn to collect, label and
 ---
 
 📚 This guide explains how to train your own **custom dataset** with [YOLOv5](https://github.com/ultralytics/yolov5) 🚀.  
-UPDATED 26 March 2023.
+UPDATED 7 June 2023.
 
 ## Before You Start
 
@@ -32,6 +32,10 @@ YOLOv5 models must be trained on labelled data in order to learn classes of obje
 <details markdown>
 <summary>Use <a href="https://roboflow.com/?ref=ultralytics">Roboflow</a> to create your dataset in YOLO format</summary>
 
+!!! warning
+
+    Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.
+
 ### 1.1 Collect Images
 
 Your model will learn by example. Training on images similar to the ones it will see in the wild is of the utmost importance. Ideally, you will collect a wide variety of images from the same configuration (camera, angle, lighting, etc.) as you will ultimately deploy your project.
@@ -200,6 +204,7 @@ Results file `results.csv` is updated after each epoch, and then plotted as `res
 
 ```python
 from utils.plots import plot_results
+
 plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'
 ```
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.115'
+__version__ = '8.0.116'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR

ultralytics/yolo/data/utils.py

@@ -138,7 +138,7 @@ def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
     """
     Args:
         imgsz (tuple): The image size.
-        polygons (np.ndarray): [N, M], N is the number of polygons, M is the number of points(Be divided by 2).
+        polygons (list[np.ndarray]): [N, M], N is the number of polygons, M is the number of points(Be divided by 2).
         color (int): color
         downsample_ratio (int): downsample ratio
     """

ultralytics/yolo/utils/__init__.py

@@ -224,6 +224,11 @@ def set_logging(name=LOGGING_NAME, verbose=True):
                 'propagate': False}}})
 
 
+def emojis(string=''):
+    """Return platform-dependent emoji-safe version of string."""
+    return string.encode().decode('ascii', 'ignore') if WINDOWS else string
+
+
 class EmojiFilter(logging.Filter):
     """
     A custom logging filter class for removing emojis in log messages.
@@ -533,6 +538,7 @@ def get_user_config_dir(sub_dir='Ultralytics'):
     # GCP and AWS lambda fix, only /tmp is writeable
     if not is_dir_writeable(str(path.parent)):
         path = Path('/tmp') / sub_dir
+        LOGGER.warning(f"WARNING ⚠️ user config directory is not writeable, defaulting to '{path}'.")
 
     # Create the subdirectory if it does not exist
     path.mkdir(parents=True, exist_ok=True)
@@ -544,11 +550,6 @@ USER_CONFIG_DIR = Path(os.getenv('YOLO_CONFIG_DIR', get_user_config_dir()))  # U
 SETTINGS_YAML = USER_CONFIG_DIR / 'settings.yaml'
 
 
-def emojis(string=''):
-    """Return platform-dependent emoji-safe version of string."""
-    return string.encode().decode('ascii', 'ignore') if WINDOWS else string
-
-
 def colorstr(*input):
     """Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')."""
     *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string

ultralytics/yolo/utils/benchmarks.py

@@ -90,7 +90,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
                 filename = model.ckpt_path or model.cfg
                 export = model  # PyTorch format
             else:
-                filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device)  # all others
+                filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device, verbose=False)
                 export = YOLO(filename, task=model.task)
                 assert suffix in str(filename), 'export failed'
             emoji = '❎'  # indicates export succeeded
@@ -196,8 +196,17 @@ class ProfileModels:
                 model.fuse()  # to report correct params and GFLOPs in model.info()
                 model_info = model.info()
                 if self.trt and self.device.type != 'cpu' and not engine_file.is_file():
-                    engine_file = model.export(format='engine', half=True, imgsz=self.imgsz, device=self.device)
+                    engine_file = model.export(format='engine',
-                onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=self.device)
+                                               half=True,
+                                               imgsz=self.imgsz,
+                                               device=self.device,
+                                               verbose=False)
+                onnx_file = model.export(format='onnx',
+                                         half=True,
+                                         imgsz=self.imgsz,
+                                         simplify=True,
+                                         device=self.device,
+                                         verbose=False)
             elif file.suffix == '.onnx':
                 model_info = self.get_onnx_model_info(file)
                 onnx_file = file
@@ -254,7 +263,7 @@ class ProfileModels:
         for _ in range(3):
             start_time = time.time()
             for _ in range(self.num_warmup_runs):
-                model(input_data, verbose=False)
+                model(input_data, imgsz=self.imgsz, verbose=False)
             elapsed = time.time() - start_time
 
         # Compute number of runs as higher of min_time or num_timed_runs
@@ -263,7 +272,7 @@ class ProfileModels:
         # Timed runs
         run_times = []
         for _ in tqdm(range(num_runs), desc=engine_file):
-            results = model(input_data, verbose=False)
+            results = model(input_data, imgsz=self.imgsz, verbose=False)
             run_times.append(results[0].speed['inference'])  # Convert to milliseconds
 
         run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3)  # sigma clipping

ultralytics/yolo/utils/callbacks/dvc.py

@@ -42,7 +42,7 @@ def _log_images(image_path, prefix=''):
 def _log_plots(plots, prefix=''):
     for name, params in plots.items():
         timestamp = params['timestamp']
-        if _processed_plots.get(name, None) != timestamp:
+        if _processed_plots.get(name) != timestamp:
             _log_images(name, prefix)
             _processed_plots[name] = timestamp
 

ultralytics/yolo/utils/checks.py

@@ -19,9 +19,9 @@ import requests
 import torch
 from matplotlib import font_manager
 
-from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, clean_url, colorstr,
+from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, RANK, ROOT, USER_CONFIG_DIR, TryExcept, clean_url,
-                                    downloads, emojis, is_colab, is_docker, is_kaggle, is_online, is_pip_package,
+                                    colorstr, downloads, emojis, is_colab, is_docker, is_kaggle, is_online,
-                                    url2file)
+                                    is_pip_package, url2file)
 
 
 def is_ascii(s) -> bool:
@@ -164,6 +164,9 @@ def check_font(font='Arial.ttf'):
     Returns:
         file (Path): Resolved font file path.
     """
+    from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
+
+    with torch_distributed_zero_first(RANK):
         name = Path(font).name
 
         # Check USER_CONFIG_DIR