visualdl>=2.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r /home/aistudio/work/PaddleDetection/requirements.txt (line 3)) (2.4.0) Requirement already satisfied: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from...
python-u tools/infer.py-c contrib/VehicleDetection/vehicle_yolov3_darknet.yml \-o weights=vehicle_yolov3_darknet \--infer_dir contrib/VehicleDetection/demo \--draw_threshold0.2\--output_dir contrib/VehicleDetection/demo/output 检测结果保存在 contrib\VehicleDetection\demo 目录下: 可以看到检测效果...
python setup.py install --user In [ ] # 切换到develop分支 !git checkout develop In [ ] # 安装`sahi`库 !pip install sahi 三、模型选型 我们使用RT-DETR-R50进行切图数据集训练,RT-DETR是第一个实时端到端目标检测器。通过高效的混合编码器,解耦尺度内交互和跨尺度融合来高效处理多尺度特征。此外,...
$ python -m cardetection.detection.kitti To generate a synthetic dataset: $ python -m cardetection.detection.syntheticdataset Tensorflow: Note: At present, the stable versions of TensorFlow (0.6.0) don't work with the code for this project, so it was installed from the source on the current...
在Jetson Nano上基于python部署Paddle Inference 飞桨官网教程 二、硬件准备 准备好一块新鲜出炉的Jetson Xavier NX,并配好基础的开发环境,当然Jetson系列的部署是基本一致的(出自某PPDE-高学长的说法),如果没有可以用Jetson nano代替。不过越贵的板子跑起来越轻松,我便白嫖的实验室小伙伴的硬件(毕竟没有这经济实力)...
Python Star52 This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples...
DCIC海上船舶智能检测: PaddleDetection 线上0.92 - 飞桨AI Studio - 人工智能学习与实训社区aistudio.baidu.com/aistudio/projectdetail/3483533?contributionType=1 赛题背景 海上船舶目标检测对于领海安全、渔业资源管理和海上运输与救援具有重要意义,但在天气和海浪等不可控的自然因素影响下,依靠派遣海警船或基于可...
Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Existing aut
These tests have not been included in the text for clarity, but can be found in the Jupyter project available. Finally, it is important to note that when a sampling rate of 1 in 1,000 packets is applied, most of the information is lost, and therefore fewer flows are generated. Figure ...
project page:http://www.ee.cuhk.edu.hk/˜wlouyang/projects/imagenetDeepId/index.html arxiv:http://arxiv.org/abs/1412.5661 Object Detectors Emerge in Deep Scene CNNs intro: ICLR 2015 arxiv:http://arxiv.org/abs/1412.6856 paper:https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15...