Pinned Repositories
PaddleDetection
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
PaddleOCR
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
ACSTNet
ACSTNet: An improved YOLO X method for small object detection with pixel-level attention and parallel Swin Transformer
bloom
camera_calibration
用于求相机内参与外参的python代码
DailyUp
DEAM
Code resources for pre-published papers.
deep_sort_paddle
keras-unet-collection
The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.
OCR
Wei-JL's Repositories
Wei-JL/ACSTNet
ACSTNet: An improved YOLO X method for small object detection with pixel-level attention and parallel Swin Transformer
Wei-JL/bloom
Wei-JL/camera_calibration
用于求相机内参与外参的python代码
Wei-JL/DailyUp
Wei-JL/DEAM
Code resources for pre-published papers.
Wei-JL/deep_sort_paddle
Wei-JL/keras-unet-collection
The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.
Wei-JL/OCR
Wei-JL/PaddleOCR
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
Wei-JL/PaddleViT
:robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
Wei-JL/PPOCRLabel
PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PPOCR model to automatically detect and re-recognize data. It is written in python3 and pyqt5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models.
Wei-JL/ROOT
Test
Wei-JL/Segmentation-of-remote-sensing-image
详细说明及其更改
Wei-JL/Segmentation-of-remote-sensing-images
数据集包含2个子文件，分别为：训练数据集（原始图片）文件、训练数据集（标注图片）文件，详细介绍如下： 训练数据集（原始图片）文件名称：img_train 包含66,653张分辨率为2m/pixel，尺寸为256 * 256的JPG图片，每张图片的名称形如T000123.jpg。 训练数据集（标注图片）文件名称：lab_train 包含66,653张分辨率为2m/pixel，尺寸为256 * 256的PNG图片，每张图片的名称形如T000123.png。 备注： 全部PNG图片共包括4种分类，像素值分别为0、1、2、3。此外，像素值255为未标注区域，表示对应区域的所属类别并不确定，在评测中也不会考虑这部分区域。 测试数据集 测试数据集文件名称：img_test.zip，详细介绍如下： 包含4,609张分辨率为2m/pixel，尺寸为256 * 256的JPG图片，文件名称形如123.jpg。