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YOLOv8-16bit/docs/models/index.md

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Ultralytics supports many models and architectures with more to come in the future. What to add your model architecture? Here's how you can contribute

YOLO

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

YOLOv8

About

Supported Tasks

Model Type Pre-trained Weights Task
YOLOv8 yolov8n.pt, yolov8s.pt, yolov8m.pt, yolov8l.pt, yolov8x.pt Detection
YOLOv8-seg yolov8n-seg.pt, yolov8s-seg.pt, yolov8m-seg.pt, yolov8l-seg.pt, yolov8x-seg.pt Instance Segmentation
YOLOv8-pose yolov8n-pose.pt, yolov8s-pose.pt, yolov8m-pose.pt, yolov8l-pose.pt, yolov8x-pose.pt ,yolov8x-pose-p6 Pose/Keypoints
YOLOv8-cls yolov8n-cls.pt, yolov8s-cls.pt, yolov8m-cls.pt, yolov8l-cls.pt, yolov8x-cls.pt Classification

Supported Modes

Mode Supported
Inference ✔️
Validation ✔️
Training ✔️

??? Performance

=== "Detection"

    | 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.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
    | [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
    | [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
    | [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"

    | 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) |
    | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
    | [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
    | [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
    | [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
    | [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"

    | 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 |
    | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
    | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224                   | 66.6             | 87.0             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
    | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224                   | 72.3             | 91.1             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
    | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224                   | 76.4             | 93.2             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
    | [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"

    | 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) |
    | ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
    | [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |
    | [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |
    | [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
    | [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
    | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
    | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |

YOLOv5u

About

Anchor-free YOLOv5 models with improved accuracy-speed tradeoff.

Supported Tasks

Model Type Pre-trained Weights Task
YOLOv5u yolov5nu, yolov5su, yolov5mu, yolov5lu, yolov5xu, yolov5n6u, yolov5s6u, yolov5m6u, yolov5l6u, yolov5x6u Detection

Supported Modes

Mode Supported
Inference ✔️
Validation ✔️
Training ✔️

??? Performance

=== "Detection"

    | 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) |
    | ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
    | [YOLOv5nu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5nu.pt)   | 640                   | 34.3                 | 73.6                           | 1.06                                | 2.6                | 7.7               |
    | [YOLOv5su](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5su.pt)   | 640                   | 43.0                 | 120.7                          | 1.27                                | 9.1                | 24.0              |
    | [YOLOv5mu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5mu.pt)   | 640                   | 49.0                 | 233.9                          | 1.86                                | 25.1               | 64.2              |
    | [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             |

Vision Transformers

Vit models currently support Python environment:

from ultralytics.vit import SAM 
# from ultralytics.vit import MODEL_TYPe

model = SAM("sam_b.pt")
model.info()  # display model information
model.predict(...)  # train the model

Segment Anything

About

Supported Tasks

Model Type Pre-trained Weights Tasks Supported
sam base sam_b.pt Instance Segmentation
sam large sam_l.pt Instance Segmentation

Supported Modes

Mode Supported
Inference ✔️
Validation ❌
Training ❌