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

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Glenn Jocher 2 years ago committed by GitHub
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2
docs/models/yolo-nas.md
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@ -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

12
docs/models/yolov5.md
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@ -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 |
| [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280 | 48.6 | - | - | 15.3 | 24.6 |
| [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280 | 53.6 | - | - | 41.2 | 65.7 |
| [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280 | 55.7 | - | - | 86.1 | 137.4 |
| [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280 | 56.8 | - | - | 155.4 | 250.7 |
| [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 | 422.6 | 2.34 | 15.3 | 24.6 |
| [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 | 1470.9 | 5.47 | 86.1 | 137.4 |
| [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

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docs/models/yolov8.md
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@ -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"

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docs/modes/track.md
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@ -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.

6
docs/yolov5/tutorials/roboflow_datasets_integration.md
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@ -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

7
docs/yolov5/tutorials/train_custom_data.md
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@ -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'
```

2
ultralytics/__init__.py
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@ -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

2
ultralytics/yolo/data/utils.py
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@ -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
"""

11
ultralytics/yolo/utils/__init__.py
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@ -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

19
ultralytics/yolo/utils/benchmarks.py
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@ -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)
onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=self.device)
engine_file = model.export(format='engine',
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

2
ultralytics/yolo/utils/callbacks/dvc.py
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@ -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

43
ultralytics/yolo/utils/checks.py
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@ -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,
downloads, emojis, is_colab, is_docker, is_kaggle, is_online, is_pip_package,
url2file)
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, RANK, ROOT, USER_CONFIG_DIR, TryExcept, clean_url,
colorstr, downloads, emojis, is_colab, is_docker, is_kaggle, is_online,
is_pip_package, url2file)
def is_ascii(s) -> bool:
@ -164,23 +164,26 @@ def check_font(font='Arial.ttf'):
Returns:
file (Path): Resolved font file path.
"""
name = Path(font).name
# Check USER_CONFIG_DIR
file = USER_CONFIG_DIR / name
if file.exists():
return file
# Check system fonts
matches = [s for s in font_manager.findSystemFonts() if font in s]
if any(matches):
return matches[0]
# Download to USER_CONFIG_DIR if missing
url = f'https://ultralytics.com/assets/{name}'
if downloads.is_url(url):
downloads.safe_download(url=url, file=file)
return file
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
file = USER_CONFIG_DIR / name
if file.exists():
return file
# Check system fonts
matches = [s for s in font_manager.findSystemFonts() if font in s]
if any(matches):
return matches[0]
# Download to USER_CONFIG_DIR if missing
url = f'https://ultralytics.com/assets/{name}'
if downloads.is_url(url):
downloads.safe_download(url=url, file=file)
return file
def check_python(minimum: str = '3.7.0') -> bool:

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