Online Tutorial | Tsinghua University Strongly Recommends! YOLOv10 Achieves More Efficient Target Detection
In the past few years, YOLO has become the dominant paradigm in real-time object detection due to its effective balance between computational cost and detection performance. However, YOLO relies on non-maximum suppression (NMS) for post-processing, which hinders the end-to-end deployment of YOLO and has an adverse impact on inference latency.
YOLOv10 is a real-time target detection method developed by researchers from Tsinghua University based on the Ultralytics Python package.Designed to address the deficiencies of previous YOLO versions in post-processing and model architecture, YOLOv10 achieves state-of-the-art performance while significantly reducing computational overhead by eliminating non-maximum suppression (NMS) and optimizing various model components.
The HyperAI official website has now launched the "YOLOv10 Real-time End-to-End Object Detection" tutorial. You can start object detection immediately by cloning it with one click without entering any commands.
Tutorial address:
Demo Run
1. Log in to hyper.ai, on the Tutorial page, select YOLOv10 Real-time End-to-End Object Detection, and click Run this tutorial online.
2. After the page jumps, click "Clone" in the upper right corner to clone the tutorial into your own container.
3. Click "Next: Select Hashrate" in the lower right corner.
4. After the page jumps, select "NVIDIA GeForce RTX 4090" and click "Next: Review".New users can register using the invitation link below to get 4 hours RTX 4090 + 5 hours of free CPU!
HyperAI exclusive invitation link (copy and open in browser):
https://openbayes.com/console/signup?r=6bJ0ljLFsFh_Vvej
5. Click "Continue" and wait for resources to be allocated. The first clone takes about 2 minutes. When the status changes to "Running", click the jump arrow next to "API Address" to jump to the "YOLOv10 Real-time End-to-End Object Detection" page.Please note that users must complete real-name authentication before using the API address access function.
If the issue persists for more than 10 minutes and remains in the "Allocating resources" state, try stopping and restarting the container. If restarting still does not resolve the issue, please contact the platform customer service on the official website.
Effect Demonstration
1. Open the YOLOv10 Real-time End-to-End Object Detection Demo page, upload a photo, click Detect Objects, and wait a moment for the results to be output. You can see that it successfully identifies the kitten and puppy in the picture.
Finally, I recommend an online academic sharing activity. Interested friends can scan the QR code to participate!
