deepfashion2_yolov8s-seg: 这个模型可能是基于DeepFashion2数据集训练的,用于时尚或服装相关的检测任务,并且带有分割功能。DeepFashion2是一个大型服装数据集,常用于服装识别、检索和分割等任务。 mediapipe_face_full, mediapipe_face_short, mediapipe_face_mesh: 这些模型与MediaPipe有关,MediaPipe是Google开发的一个跨...
求助,运行adeta..WARNING Unable to automatically guess model task, assuming 'task=detect'. Explicitly define
yolov8-lite-t64090.487.773.3-google yolov8-lite-s64093.491.278.6-google yolov8n64094.692.379.6-google yolov8s64096.194.283.1-- yolov8m64096.695.084.7-- yolov9c64095.694.183.2-- yolov9e640--- Releases No releases published Packages No packages published Languages...
yolov8-lite-s 640 93.4 91.1 77.7 - google yolov8n 640 94.5 92.2 79.0 - google yolov8s 640 96.0 94.2 82.6 - - yolov8m 640 96.6 95.0 84.1 - - yolov8n-face demo yolov8-face-landmarks-opencv-dnn References https://github.com/ultralytics/ultralytics https://github.com/deepcam-cn...
The mAP50-95 of DOLP-YOLOv5 reached 63.5%, with 3.08% and 4.44% improvements over the YOLOv8s and YOLOv9s, and achieved a response speed of 384.6f/s. This research not only demonstrates the superiority of DOLP-YOLOv5 in face mask wearing detection, but also has certain reference ...
[-] ADetailer: Failed to load model 'face_yolov8n.pt' from huggingface [-] ADetailer: Failed to load model 'face_yolov8s.pt' from huggingface [-] ADetailer: Failed to load model 分享21 stablediffusion吧 b194466 每到98%就容易崩,没有大佬解决这个bug吗?每次等了十几分钟就崩了,甚至几...
The average highest accuracy rate of the YOLOv8s model is 97.0%, and the average highest accuracy rate of the DSOTAs model is 97.4%. These promising results underscore the potential of our approach for practical applications and further exploration in the computer vision domain. 展开 ...
[-] ADetailer: Failed to load model 'face_yolov8n.pt' from huggingface [-] ADetailer: Failed to load model 'face_yolov8s.pt' from huggingface [-] ADetailer: Failed to load model 分享21 游天龙zd吧 游天龙Zd 游戏王DIY卡 日常开始尝试自己召教程弄,然而教程要么过于复杂,要么就只是单卡示范...
from ultralytics import YOLO def main(): # Load the YOLO model model = YOLO(model="yolov8s.pt") # model = YOLO(model="yolov8n.pt") # Set the device to GPU device = 'cuda' # Train the model on the GPU model.train( data="path_to_your_project\\facedetect-1\\data.yaml", ...
We tested YOLOv5s, YOLOv6, YOLOv7, and YOLOv8s models on self-made datasets. The results show that the QARepVGG-YOLOv7 model has the best accuracy compared with the most advanced YOLO model. Our model achieves a significantly improved mAP value of 0.946 and a faster fps of 263.2, ...