yolov8

YOLOv8

Abstract

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.

Requirements

mindspore	ascend driver	firmware	cann toolkit/kernel
2.3.1	24.1.RC2	7.3.0.1.231	8.0.RC2.beta1

Quick Start

Please refer to the GETTING_STARTED in MindYOLO for details.

Training

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- Distributed Training

It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run

# distributed training on multiple Ascend devices
msrun --worker_num=8 --local_worker_num=8 --bind_core=True --log_dir=./yolov8_log python train.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend --is_parallel True

Note: For more information about msrun configuration, please refer to here.

For detailed illustration of all hyper-parameters, please refer to config.py.

Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.

- Standalone Training

If you want to train or finetune the model on a smaller dataset without distributed training, please run:

# standalone training on a CPU/Ascend device
python train.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend

Validation and Test

To validate the accuracy of the trained model, you can use test.py and parse the checkpoint path with --weight.

python test.py --config ./configs/yolov8/yolov8n.yaml --device_target Ascend --weight /PATH/TO/WEIGHT.ckpt

Performance

Detection

Experiments are tested on Ascend 910* with mindspore 2.3.1 graph mode.

model name	scale	cards	batch size	resolution	jit level	graph compile	ms/step	img/s	map	recipe	weight
YOLOv8	N	8	16	640x640	O2	145.89s	252.79	506.35	37.3%	yaml	weights
YOLOv8	S	8	16	640x640	O2	172.22s	251.30	509.35	44.7%	yaml	weights

Experiments are tested on Ascend 910 with mindspore 2.3.1 graph mode.

model name	scale	cards	batch size	resolution	jit level	graph compile	ms/step	img/s	map	recipe	weight
YOLOv8	N	8	16	640x640	O2	195.63s	265.13	482.78	37.2%	yaml	weights
YOLOv8	S	8	16	640x640	O2	115.60s	292.68	437.34	44.6%	yaml	weights
YOLOv8	M	8	16	640x640	O2	185.25s	383.72	333.58	50.5%	yaml	weights
YOLOv8	L	8	16	640x640	O2	175.08s	429.02	298.35	52.8%	yaml	weights
YOLOv8	X	8	16	640x640	O2	183.68s	521.97	245.22	53.7%	yaml	weights

Segmentation

Experiments are tested on Ascend 910 with mindspore 2.3.1 graph mode.

coming soon

Experiments are tested on Ascend 910 with mindspore 2.3.1 graph mode.

model Name	scale	cards	batch size	resolution	jit level	graph compile	ms/step	img/s	map	mask map	recipe	weight
YOLOv8-seg	X	8	16	640x640	O2	183.68s	641.25	199.61	52.5%	42.9%	yaml	weights

Notes

• map: Accuracy reported on the validation set.
• We refer to the official YOLOV8 to reproduce the P5 series model.

References

[1] Jocher Glenn. Ultralytics YOLOv8. https://github.com/ultralytics/ultralytics, 2023.