yolov8_test/ultralytics/models

Models

Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (*.yamls) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.

These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.

To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've selected a model, you can use the provided *.yaml file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics Docs, and if you need help or have any questions, feel free to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!

Usage

Model *.yaml files may be used directly in the Command Line Interface (CLI) with a yolo command:

yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100

They may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

from ultralytics import YOLO

model = YOLO("model.yaml")  # build a YOLOv8n model from scratch
# YOLO("model.pt")  use pre-trained model if available
model.info()  # display model information
model.train(data="coco128.yaml", epochs=100)  # train the model

Pre-trained Model Architectures

Ultralytics supports many model architectures. Visit models page to view detailed information and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.

What to add your model architecture? Here's how you can contribute

1. YOLOv8

About - Cutting edge Detection, Segmentation and Classification models developed by Ultralytics.
Citation - Available Models:

• Detection - yolov8n, yolov8s, yolov8m, yolov8l, yolov8x
• Instance Segmentation - yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg
• Classification - yolov8n-cls, yolov8s-cls, yolov8m-cls, yolov8l-cls, yolov8x-cls

Performance

Detection

Model	size (pixels)	mAPval 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n	640	37.3	80.4	0.99	3.2	8.7
YOLOv8s	640	44.9	128.4	1.20	11.2	28.6
YOLOv8m	640	50.2	234.7	1.83	25.9	78.9
YOLOv8l	640	52.9	375.2	2.39	43.7	165.2
YOLOv8x	640	53.9	479.1	3.53	68.2	257.8

Segmentation

Model	size (pixels)	mAPbox 50-95	mAPmask 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-seg	640	36.7	30.5	96.1	1.21	3.4	12.6
YOLOv8s-seg	640	44.6	36.8	155.7	1.47	11.8	42.6
YOLOv8m-seg	640	49.9	40.8	317.0	2.18	27.3	110.2
YOLOv8l-seg	640	52.3	42.6	572.4	2.79	46.0	220.5
YOLOv8x-seg	640	53.4	43.4	712.1	4.02	71.8	344.1

Classification

Model	size (pixels)	acc top1	acc top5	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B) at 640
YOLOv8n-cls	224	66.6	87.0	12.9	0.31	2.7	4.3
YOLOv8s-cls	224	72.3	91.1	23.4	0.35	6.4	13.5
YOLOv8m-cls	224	76.4	93.2	85.4	0.62	17.0	42.7
YOLOv8l-cls	224	78.0	94.1	163.0	0.87	37.5	99.7
YOLOv8x-cls	224	78.4	94.3	232.0	1.01	57.4	154.8

2. YOLOv5u

About - Anchor-free YOLOv5 models with new detection head and better speed-accuracy tradeoff
Citation - Available Models:

• Detection - yolov5nu, yolov5su, yolov5mu, yolov5lu, yolov5xu

Performance

Detection

Model	size (pixels)	mAPval 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv5nu	640	34.3	73.6	1.06	2.6	7.7
YOLOv5su	640	43.0	120.7	1.27	9.1	24.0
YOLOv5mu	640	49.0	233.9	1.86	25.1	64.2
YOLOv5lu	640	52.2	408.4	2.50	53.2	135.0
YOLOv5xu	640	53.2	763.2	3.81	97.2	246.4