Stars
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
cherish24 / DocLayout-YOLO
Forked from opendatalab/DocLayout-YOLODocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
整理目前开源的最优表格识别模型，完善前后处理，模型转换为ONNX
RAFConv: Innovating Spatital Attention and Standard Convolutional Operation
Deep Splitting and Merging for Table Structure Decomposition
Tensorflow implementation of Swin Transformer model.
cherish24 / OSME_MAMC
Forked from hyao1/OSME_MAMCimplement of Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition with keras
ctcloss + centerloss crnn text recognition
Tensorflow ShuffleNet v2 implementation
the multi-GPUs implementation of mobilenet v3 in tensorflow with tf.layers
Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.
Change smooth L1 loss to GIoU loss for RetinaNet
PointRend for instance segmentation on TensorFlow
CenterNet (Objects as Points) implementation in Keras and Tensorflow
FCOS (Fully Convolutional One-Stage Object Detection) implementation in Keras and Tensorflow
Keras上700行代码复现YOLOv3！使用DIOU loss。支持将模型导出为pytorch模型。
Code for: S.R. Qasim, H. Mahmood, and F. Shafait, Rethinking Table Recognition using Graph Neural Networks (2019)
Keras Implementation of EfficientNets
String Distance using cython
超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M
This is a tensorflow re-implementation of Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks.
cherish24 / RetinaNet_Tensorflow_Rotation
Forked from DetectionTeamUCAS/RetinaNet_Tensorflow_RotationFocal Loss for Dense Rotation Object Detection
cherish24 / Attention-OCR
Forked from da03/Attention-OCRVisual Attention based OCR
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)
Table Detection from the Given Pictures or Files
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，近30万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06