Berkeley Deep Drive-X (eXplanation) is a dataset is composed of over 77 hours of driving within 6,970 videos. The videos are taken in diverse driving conditions, e.g. day/night, highway/city/countryside, summer/
If you find this dataset useful, please cite this paper (and refer the data as Berkeley DeepDrive eXplanation or BDD-X dataset): @article{kim2018textual, title={Textual Explanations for Self-Driving Vehicles}, author={Kim, Jinkyu and Rohrbach, Anna and Darrell, Trevor and Canny, John and ...
据报道,到 2024 年,全球自动驾驶汽车市场的 CAGR(年均复合增长率) 预计将加速增长 62.86%,达到 412.4 亿美元。 如果你也一直在关注自动驾驶技术,那么就不要错过我们今天要介绍的数据集——BDD100K Dataset。 10w 个视频、图片+超全标注 BDD100K 数据集,是加州大学伯克利分校 AI 实验室(BAIR)于 2018 年发布的,...
We used a combination of processing and manual labeling to identify maneuvers (lane changes and turns) and intersections for each route. This information has been added to the vehicle data for each video in every dataset. We converted the vehicle information in BDD-A to match the format of DR...
f = open("/home/violet/Documents/dataset/bdd100k/label/train/bdd100k_labels_images_train.json") # json文件的绝对路径,换成自己的 info = json.load(f) objects = info n = len(objects) # 将左上右下坐标转换成 中心x,y以及w h def bboxtrans(box_x_min, box_y_min, box_x_max, box_y...
1. 首先,确保你已经安装了`mmsegmentation`库。如果没有安装,可以使用以下命令进行安装: ```bash pip install mmsegmentation ``` 2. 然后,创建一个名为`convert_bdd100k_to_mmseg.py`的Python脚本,并在其中编写以下代码: ```python import os import shutil from mmseg.datasets import CustomDataset from mmseg...
(x2-x1)*(y2-y1) } annotations.append(annotation) ann_id += 1 img_id += 1 tid_cnt += read_tid_num_per_video(os.path.join(mot_labels_path, s, v_label[:-5]+'.txt')) dataset_dict = {} dataset_dict["images"] = images dataset_dict["annotations"] = annotations dataset_dict...
However, by analyzing the particular class set, the image quality and the topicality from each one, we chose BDD100K to train the algorithms: a huge, complete dataset including different weather conditions, places and times of the day, and a wide range of light conditions, occlusion, and ...
existing works for FedSSL rely on a closed-world assumption that all local training data and global testing data are from seen classes observed in the labeled dataset. It is crucial to go one step further: adapting FL models to an open-worl...
= DataSet11.Tables("COMM.USERS").Rows[0]["User_Name"].tostring()LabelText = DataSet11.Tables("COMM.USERS").Rows(0).Item("user_name")Label.Text=ds.Tables(0).Rows(i).Item( "列名 ")Label.Text=ds.tables[0].rows[0][ "filed "]Label.Text=ds.Tables[0].Rows[x][y]