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2.NEU-DET数据集介绍 NEU-DET钢材表面缺陷共有六大类,一共1800张, 类别分别为:'crazing','inclusion','patches','pitted_surface','rolled-in_scale','scratches' 2.1数据集划分 通过split_train_val.py得到trainval.txt、val.txt、test.txt 代码语言:python 代码运行次数:1 复制 Cloud Studio代码运行 # ...
The first to adopt the YOLOF model on a metal defect NEU-DET dataset. Using several image preprocessing methods, including Edge Detection, Denoising, Sharpening, and SR, were experimented on the metal defect NEU-DET dataset. In the proposed optimized batch size, backbone, soft efficient non-maxi...
重装后没选择CUDA版本导致我训练的时候用CPU跑半小时一轮,要注意安装CUDA版本并且在训练中调用好batch参数,我的笔记本1050ti,默认8的话只能利用50%把batch改成16后就可以跑满了) 注意事项:去东北大学宋克臣老师主页下载点击该链接NEU-DET
东北大学钢材检测数据集NEU-DET 喜爱 0 由东北大学(NEU)发布的表面缺陷数据库,收集了热轧钢带的六种典型表面缺陷,即轧制氧化皮(RS),斑块(Pa),开裂(Cr),点蚀表面( PS),内含物(In)和划痕(Sc)。该数据库包括1,800个灰度图像:六种不同类型的典型表面缺陷,每一类缺陷包含300个样本。对于缺陷检测任务,数据集提供...
公共数据集> NEU-DETNEU-DET 2 钢材表面缺陷检测 Z Zcy0829 1枚 CC0 机器学习计算机视觉 1 10 2024-07-23 详情 相关项目 评论(0) 创建项目 文件列表 images.zip images.zip (25.03M) 下载 File Name Size Update Time images/crazing_10.jpg 21377 2016-12-11 10:14:44 images/crazing_100.jpg 2183...
Karikari TK, Pascoal TA, Ashton NJ, Janelidze S, Benedet AL, Rodriguez JL, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 2020;19(5):422–33. Arti...
NEU-DET arrow_right folder ANNOTATIONS arrow_right folder IMAGES Summary arrow_right folder 3600 files lightbulb See what others are saying about this dataset What have you used this dataset for? Learning 0Research 0Application 0LLM Fine-Tuning 0 ...
Kitti DatasetPretrained models could be download from HereCarPedVanCyclistMean Success 71.5 55.3 42.3 68.7 61.8 Precision 82.1 82.5 50.2 92.0 79.7SetupInstallationconda create -n smat python=3.8 -y conda activate smat pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -...
B. „InputDataset“. Dadurch wird ein Pipelineparameter für das Eingabedataset erstellt. Beim Aufrufen des Pipelineendpunkts für das Trainieren können Sie ein neues Dataset zum erneuten Trainieren des Modells angeben. Wählen Sie Veröffentlichen aus. Aufrufen des Pipelineendpunkts über ...