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ultralytics 8.0.186 add Open Images V7 models (#5070)

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8
docs/guides/conda-quickstart.md
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@ -6,14 +6,14 @@ keywords: Ultralytics, YOLO, Conda, environment setup, object detection, package
# Conda Quickstart Guide for Ultralytics
<p align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/266324397-32119e21-8c86-43e5-a00e-79827d303d10.png" alt="Ultralytics Conda Package Visual">
</p>
This guide provides a comprehensive introduction to setting up a Conda environment for your Ultralytics projects. Conda is an open-source package and environment management system that offers an excellent alternative to pip for installing packages and dependencies. Its isolated environments make it particularly well-suited for data science and machine learning endeavors. For more details, visit the Ultralytics Conda package on [Anaconda](https://anaconda.org/conda-forge/ultralytics) and check out the Ultralytics feedstock repository for package updates on [GitHub](https://github.com/conda-forge/ultralytics-feedstock/).
[![Conda Recipe](https://img.shields.io/badge/recipe-ultralytics-green.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics) [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/ultralytics.svg)](https://anaconda.org/conda-forge/ultralytics)
<p align="center">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/266324397-32119e21-8c86-43e5-a00e-79827d303d10.png" alt="Ultralytics Conda Package Visual">
</p>
## What You Will Learn
- Setting up a Conda environment

8
docs/guides/docker-quickstart.md
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@ -6,14 +6,14 @@ keywords: Ultralytics, YOLO, Docker, GPU, containerization, object detection, pa
# Docker Quickstart Guide for Ultralytics
This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. Docker is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics).
[![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
<p align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/270173601-fc7011bd-e67c-452f-a31a-aa047dcd2771.png" alt="Ultralytics Docker Package Visual">
</p>
This guide serves as a comprehensive introduction to setting up a Docker environment for your Ultralytics projects. [Docker](https://docker.com/) is a platform for developing, shipping, and running applications in containers. It is particularly beneficial for ensuring that the software will always run the same, regardless of where it's deployed. For more details, visit the Ultralytics Docker repository on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics).
[![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
## What You Will Learn
- Setting up Docker with NVIDIA support

20
docs/models/yolov8.md
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@ -38,7 +38,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
!!! Performance
=== "Detection"
=== "Detection (COCO)"
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@ -48,7 +48,19 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
=== "Segmentation"
=== "Detection (Open Images V7)"
See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/), which include 600 pre-trained classes.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
=== "Segmentation (COCO)"
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@ -58,7 +70,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
=== "Classification"
=== "Classification (ImageNet)"
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
@ -68,7 +80,7 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
=== "Pose"
=== "Pose (COCO)"
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |