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README.md

@@ -222,7 +222,7 @@ Explore Ultralytics' key integrations with leading AI platforms. These collabora
 </div>
 
 |                                                         Ultralytics HUB 🚀                                                         |                                                                     W&B                                                                     |                                                                        Comet ⭐ NEW                                                                        |                                                  Neural Magic                                                   |
-| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
 | Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results seamlessly with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/). | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualize and debug predictions. | Run YOLOv5 inference up to 6x faster on CPUs with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic). |
 
 ## ⭐ Ultralytics HUB
@@ -257,7 +257,7 @@ YOLOv5 is designed for simplicity and ease of use. We prioritize real-world perf
 This table shows the performance metrics for various YOLOv5 models trained on the COCO dataset.
 
 | Model                                                                                                  | Size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | Params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
+| ------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
 | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)                     | 640                   | 28.0                 | 45.7              | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |
 | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)                     | 640                   | 37.4                 | 56.8              | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |
 | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)                     | 640                   | 45.4                 | 64.1              | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |
@@ -347,13 +347,11 @@ python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
 
 ```python
 # Load model from PyTorch Hub (Note: Inference support might vary)
-model = torch.hub.load(
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
-    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
-)
 ```
 
 | ![Zidane Segmentation Example](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![Bus Segmentation Example](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
-| :---------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------: |
+| :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
 
 ### Export
 

benchmarks.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Run YOLOv5 benchmarks on all supported export formats.
 

classify/predict.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
 

classify/train.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Train a YOLOv5 classifier model on a classification dataset.
 

classify/val.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Validate a trained YOLOv5 classification model on a classification dataset.
 

detect.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
 

export.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.
 

hubconf.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
 

models/common.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Common modules."""
 
 import ast

models/experimental.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Experimental modules."""
 
 import math

models/tf.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 TensorFlow, Keras and TFLite versions of YOLOv5
 Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127.

models/yolo.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 YOLO-specific modules.
 

segment/predict.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
 

segment/train.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5
 release.

segment/val.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Validate a trained YOLOv5 segment model on a segment dataset.
 

train.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release.
 

utils/__init__.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """utils/initialization."""
 
 import contextlib

utils/activations.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Activation functions."""
 
 import torch

utils/augmentations.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Image augmentation functions."""
 
 import math

utils/autoanchor.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """AutoAnchor utils."""
 
 import random

utils/autobatch.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Auto-batch utils."""
 
 from copy import deepcopy

utils/callbacks.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Callback utils."""
 
 import threading

utils/dataloaders.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Dataloaders and dataset utils."""
 
 import contextlib

utils/downloads.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Download utils."""
 
 import logging

utils/flask_rest_api/example_request.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Perform test request."""
 
 import pprint

utils/flask_rest_api/restapi.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Run a Flask REST API exposing one or more YOLOv5s models."""
 
 import argparse

utils/general.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """General utils."""
 
 import contextlib

utils/loggers/__init__.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Logging utils."""
 
 import json

utils/loggers/clearml/README.md

@@ -28,10 +28,12 @@ ClearML requires communication with a server to track experiments and data. You
 Follow these steps to get started:
 
 1.  Install the `clearml` Python package:
+
     ```bash
     pip install clearml
     ```
-    *Note: This package is included in the `requirements.txt` of YOLOv5.*
+
+    _Note: This package is included in the `requirements.txt` of YOLOv5._
 
 2.  Connect the ClearML SDK to your server. [Create credentials](https://app.clear.ml/settings/workspace-configuration) (Settings -> Workspace -> Create new credentials), then run the following command and follow the prompts:
     ```bash
@@ -133,7 +135,8 @@ clearml-data add --files .
 # Finalize and upload the dataset version
 clearml-data close
 ```
-*Tip: Use `--parent <parent_dataset_id>` with `clearml-data create` to link versions and avoid re-uploading unchanged files.*
+
+_Tip: Use `--parent <parent_dataset_id>` with `clearml-data create` to link versions and avoid re-uploading unchanged files._
 
 ### Run Training Using a ClearML Dataset
 

utils/loggers/clearml/clearml_utils.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Main Logger class for ClearML experiment tracking."""
 
 import glob

utils/loggers/comet/README.md

@@ -29,10 +29,12 @@ pip install comet_ml
 You can configure Comet in two ways:
 
 1.  **Environment Variables:** Set your credentials directly in your environment.
+
     ```shell
     export COMET_API_KEY=<Your Comet API Key>
     export COMET_PROJECT_NAME=<Your Comet Project Name> # Defaults to 'yolov5' if not set
     ```
+
     Find your API key in your [Comet Account Settings](https://www.comet.com/docs/v2/guides/getting-started/quickstart/#get-an-api-key?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github_readme).
 
 2.  **Configuration File:** Create a `.comet.config` file in your working directory with the following content:
@@ -259,8 +261,8 @@ python utils/loggers/comet/hpo.py \
 Execute multiple sweep trials concurrently using the `comet optimizer` command:
 
 ```shell
-comet optimizer -j <num_workers> utils/loggers/comet/hpo.py \
+comet optimizer -j \
-  utils/loggers/comet/optimizer_config.json
+  utils/loggers/comet/optimizer_config.json < num_workers > utils/loggers/comet/hpo.py
 ```
 
 Replace `<num_workers>` with the desired number of parallel processes.

utils/loss.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Loss functions."""
 
 import torch

utils/metrics.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Model validation metrics."""
 
 import math

utils/plots.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Plotting utils."""
 
 import contextlib

utils/segment/augmentations.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Image augmentation functions."""
 
 import math

utils/segment/dataloaders.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Dataloaders."""
 
 import os

utils/segment/metrics.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Model validation metrics."""
 
 import numpy as np

utils/torch_utils.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """PyTorch utils."""
 
 import math

utils/triton.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """Utils to interact with the Triton Inference Server."""
 
 import typing

val.py

@@ -1,5 +1,4 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
-
 """
 Validate a trained YOLOv5 detection model on a detection dataset.