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

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
2023-06-11 20:39:32 +02:00
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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

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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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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.

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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

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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'
```