If I understand correctly, it's OK to use a package with AGPL-3.0 License for commercial use, as long as I don't make any modifications or distribute my software over the internet.
Home - Ultralytics YOLO Docs - YOLO: A Brief History
YOLO (You Only Look Once), a popular object detection and image segmentation model, was developed by Joseph Redmon and Ali Farhadi at the University of Washington. Launched in 2015, YOLO quickly gained popularity for its high speed and accuracy.
I bet the researcher named the algorithm after YOLO (You Only Live Once).
As I checked the research paper on Google Scholar, the paper has been cited by 54035, meaning that the paper has a huge impact on that field. The paper consists of only 10 pages, so it would be better to read the paper later.
I stopped doing CV research because I saw the impact my work was having. I loved the work but the military applications and privacy concerns eventually became impossible to ignore.
It is kind of sad that he seems to be inactive on his career, but my guess is that his work had too much impact on the world for him to bear.
I might have digressed… the section contains a brief history of the YOLO series, starting from the research paper and having a variety of improvements made to it. It's interesting that the history has not only research papers such as YOLO9000: Better, Faster, Stronger but also improvements by a company.
We are thrilled to announce the official launch of YOLO11, the latest iteration of the Ultralytics YOLO series, bringing unparalleled advancements in real-time object detection, segmentation, pose estimation, and classification. Building upon the success of YOLOv8, YOLO11 delivers state-of-the-art performance across the board with significant improvements in both speed and accuracy.
🚀 Key Performance Improvements:
Accuracy Boost: YOLO11 achieves up to a 2% higher mAP (mean Average Precision) on COCO for object detection compared to YOLOv8.
Efficiency & Speed: It boasts up to 22% fewer parameters than YOLOv8 models while improving real-time inference speeds by up to 2% faster, making it perfect for edge applications and resource-constrained environments.
That looks wonderful! and as I don't see any caveats in the release, and they say it's an “official launch,” I think it is a stable release, not a beta or pre-release.
Pagination is only performed automatically if you're using the generic views or viewsets. If you’re using a regular APIView, you'll need to call into the pagination API yourself to ensure you return a paginated response.
The pagination style may be set globally, using the
DEFAULT_PAGINATION_CLASSandPAGE_SIZEsetting keys. For example, to use the built-in limit/offset pagination, you would do something like this:
REST_FRAMEWORK = {
'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',
'PAGE_SIZE': 100
}Note that you need to set both the pagination class, and the page
size that should be used. Both DEFAULT_PAGINATION_CLASS and
PAGE_SIZE are None by default.
Rice 800 Pancake & Sausage 500 Mashed potatoes 200 Sushi bowl 800 m&m's 250 Protein shake 150 Sushi 300 Protein shake 300
Total 3300 kcal I will be back to normal tomorrow
push-ups
MUST:
TODO: