This repository contains material to train YOLOv8 neural network on the LARD dataset, for both detection and segmentation tasks.
All the material is accessible from the LARD.ipynb notebook.
All model checkpoints, ONNX exports and generated figures are stored in the results folder.
| task | weights | epochs | mAPval 50 (%) |
params (M) |
FLOPs (B) |
latency (ms) |
|---|---|---|---|---|---|---|
| segment | pretrained | 20 | 99.1 | 3.26 | 12.0 | 15.5 |
| segment | scratch | 40 | 98.1 | 3.26 | 12.0 | 15.5 |
| detect | pretrained | 20 | 99.4 | 3.01 | 8.1 | 11.7 |
| detect | scratch | 40 | 98.5 | 3.01 | 8.1 | 11.7 |
Ultralytics YOLOv8, developed by Ultralytics, is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
To request an Enterprise License please complete the form at Ultralytics Licensing.
See below for a quickstart installation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment.
Install
Pip install the ultralytics package including all requirements.txt in a 3.10>=Python>=3.7 environment, including PyTorch>=1.7.
pip install ultralyticsUsage
YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:
yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"yolo can be used for a variety of tasks and modes and accepts additional arguments, i.e. imgsz=640. See the YOLOv8
CLI Docs for examples.
YOLOv8 may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = model.export(format="onnx") # export the model to ONNX formatModels download automatically from the latest Ultralytics release. See YOLOv8 Python Docs for more examples.
We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we will submit to arxiv.org once complete.
- TensorFlow exports
- DDP resume
- arxiv.org paper
All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset, while Classification models are pretrained on the ImageNet dataset.
Models download automatically from the latest Ultralytics release on first use.
Detection
See Detection Docs for usage examples with these models.
| 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 |
- mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce byyolo val detect data=coco.yaml device=0 - Speed averaged over COCO val images using an Amazon EC2 P4d
instance.
Reproduce byyolo val detect data=coco128.yaml batch=1 device=0/cpu
Segmentation
See Segmentation Docs for usage examples with these models.
| Model | size (pixels) |
mAPbox 50-95 |
mAPmask 50-95 |
Speed CPU ONNX (ms) |
Speed A100 TensorRT (ms) |
params (M) |
FLOPs (B) |
|---|---|---|---|---|---|---|---|
| YOLOv8n | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| YOLOv8s | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| YOLOv8m | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| YOLOv8l | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| YOLOv8x | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
- mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce byyolo val segment data=coco.yaml device=0 - Speed averaged over COCO val images using an Amazon EC2 P4d
instance.
Reproduce byyolo val segment data=coco128-seg.yaml batch=1 device=0/cpu
Classification
See Classification Docs for usage examples with these models.
| Model | size (pixels) |
acc top1 |
acc top5 |
Speed CPU ONNX (ms) |
Speed A100 TensorRT (ms) |
params (M) |
FLOPs (B) at 640 |
|---|---|---|---|---|---|---|---|
| YOLOv8n | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| YOLOv8s | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| YOLOv8m | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| YOLOv8l | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| YOLOv8x | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
- acc values are model accuracies on the ImageNet dataset validation set.
Reproduce byyolo val classify data=path/to/ImageNet device=0 - Speed averaged over ImageNet val images using an Amazon EC2 P4d
instance.
Reproduce byyolo val classify data=path/to/ImageNet batch=1 device=0/cpu
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
|---|---|---|---|
| Label and export your custom datasets directly to YOLOv8 for training with Roboflow | Automatically track, visualize and even remotely train YOLOv8 using ClearML (open-source!) | Free forever, Comet lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with Neural Magic DeepSparse |
Ultralytics HUB is our ⭐ NEW no-code solution to visualize datasets, train YOLOv8 🚀 models, and deploy to the real world in a seamless experience. Get started for Free now! Also run YOLOv8 models on your iOS or Android device by downloading the Ultralytics App!
We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our Contributing Guide to get started, and fill out our Survey to send us feedback on your experience. Thank you 🙏 to all our contributors!
YOLOv8 is available under two different licenses:
- GPL-3.0 License: See LICENSE file for details.
- Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at Ultralytics Licensing.
For YOLOv8 bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.