Try the Model

Use the widget below to experiment with YOLOv10. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

YOLOv10 is a real-time state-of-the art object detection model that claims to have “46% less latency on multiple models” (YOLOv10, 2024). Additionally, YOLOv10 introduced NMS-Free training, which reduces the latency and enhances efficiency. YOLOv10 also offers several models ranging from N, S, M, B, L, X.

Learn how to train YOLOv10.

YOLOv10 Performance: Benchmarked on COCO and FLOPs

Model	Test Size ^(pixels)	#Params ^(M)	FLOPs ^(G)	APval ^(%)	Latency ^(ms)
YOLOv10-N	640	2.3M	6.7G	38.5%	1.84ms
YOLOv10-S	640	7.2M	21.6G	46.3%	2.49ms
YOLOv10-M	640	15.4M	59.1G	51.1%	4.74ms
YOLOv10-B	640	19.1M	92.0G	52.5%	5.74ms
YOLOv10-L	640	24.4M	120.3G	53.2%	7.28ms
YOLOv10-X	640	29.5M	160.4G	54.4%	10.70ms

(Graph sourced from the official Yolov10 repository)

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Problems

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After some testing, YOLOv10 struggles on far distance objects, even with lowered confidence. In the following image, YOLOv10 (left) is compared to YOLOv8 (right) using security footage. YOLOv8 clearly performs better due to the limited range capabilities of YOLOv10.

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YOLOv10 License

YOLOv10

is licensed under a

GNU Affero General Public

license.

Performance

Deploy a YOLOv10 API

You can use Roboflow Inference to deploy a

YOLOv10

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

Label Data Automatically with YOLOv10

You can automatically label a dataset using YOLOv10 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLOv10 to train a computer vision model.

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