EfficientSAM + YOLO World base model for use with Autodistill. zero-shot-object-detectionzero-shot-segmentationyolo-worldefficientsam UpdatedFeb 21, 2024 Python Ziad-Algrafi/ODLabel Star7 Code Issues Pull requests ODLabel is a powerful tool for zero-shot object detection, labeling and visualization...
YoloV8改进策略:EfficientViT,高效的视觉transformer与级联组注意力提升YoloV8的速度和精度,打造高效的YoloV8 YoloV8改进策略:LSKNet加入到YoloV8中,打造更适合小目标的YoloV8 YoloV8改进策略:RepViT改进YoloV8,轻量级的Block助力YoloV8实现更好的移动性 YoloV8改进策略:FastVit与YoloV8完美融合,重参数重构YoloV8网络...
- model_name: "yolov8m-worldv2-r20240529" config_file: ":/yolov8m_worldv2.yaml" - model_name: "yolov8m-r20230520" config_file: ":/yolov8m.yaml" - model_name: "yolov8n_efficientvit_sam_l0_vit_h-r20231020" config_file: ":/yolov8n_efficientvit_sam_l0_vit_h.yaml" - model...
from ultralytics import FastSAM# Define an inference sourcesource="path/to/bus.jpg"# Create a FastSAM modelmodel = FastSAM("FastSAM-s.pt")# or FastSAM-x.pt# Run inference on an imageeverything_results = model(source, device="cpu", retina_masks=True, imgsz=1024, conf=0.4, iou=0.9...
Yolov8 源码解析(三十六) .\yolov8\ultralytics\models\yolo\pose\__init__.py # 导入模块 predict 中的 PosePredictor 类 # 导入模块 train 中的 PoseTrainer 类 # 导入模块 val 中的 PoseV
YoloV8改进策略：EfficientViT，高效的视觉transformer与级联组注意力提升YoloV8的速度和精度，打造高效的...
`🔥[2024-2-18]:` We thank [@Skalskip92](https://twitter.com/skalskip92) for developing the wonderful segmentation demo via connecting YOLO-World and EfficientSAM. You can try it now at the [🤗 HuggingFace Spaces](https://huggingface.co/spaces/SkalskiP/YOLO-World). @@ -51,8 +52...
首先,加载YOLO和SAM模型。 将输入的data路径转换为Path对象,以便于后续的路径操作。 如果没有指定输出目录,则创建一个新的目录,命名为{data.stem}_auto_annotate_labels,其中data.stem是data路径的基本名称。 使用YOLO模型对输入的图像进行目标检测,结果以流的方式返回。
Efficient algorithm for automatic road sign recognition and its hardware implementation. J. Real-Time Image Process. 2014, 9, 79–93. [Google Scholar] [CrossRef] Yu, L.; Xia, X.; Zhou, K. Traffic sign detection based on visual co-saliency in complex scenes. Appl. Intell. 2019, 49, ...
This iteration continues to extend the frontiers of object detection technology, positioning it as an optimal solution for tasks requiring real-time, efficient computing. YOLOv8 is disseminated in five distinct models, n, s, m, l, and x, with each model representing a progressive increase in ...