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stanford-cs-229-cn

A Chinese Translation of Stanford CS229 notes 斯坦福机器学习CS229课程讲义的中文翻译

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

stereoconvnet

surface-defect-detection

缺陷检测文献记录

surfacenet

2017 ICCV, SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis

surfacenet-plus

2020 TPAMI, SurfaceNet+ is a volumetric learning framework for the very sparse MVS. The sparse-MVS benchmark is maintained here. Authors: Mengqi Ji#, Jinzhi Zhang#, Qionghai Dai, Lu Fang.

swa

Stochastic Weight Averaging in PyTorch

swipenet

A novel neural network architecture for dealing with small object detection and noise data in the underwater scenes.

tensorflow_learn

share examples of tensorflow

text-detection-ctpn

text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network

tf-keras-thundernet

Object detection model thundernet tensorflow.keras implementation

tf-mobilenet-v2

Mobilenet V2(Inverted Residual) Implementation & Trained Weights Using Tensorflow

thesis-helper

毕业论文小助手：一个翻译英文并将中文结果显示在侧边的PDF阅读器

thundernet-review

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

tianchi_fabric_defects_detection

广东工业智造大赛--布匹瑕疵检测复赛代码

tiny-dsod

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usage

tinydsod_bb

Implementation of Tiny-DSOD backbone

trashnet

Dataset of images of trash; Torch-based CNN for garbage image classification

ultra-light-fast-generic-face-detector-1mb

💎1MB lightweight face detection model (1MB轻量级人脸检测模型)

unetplusplus

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

vizseq

An Analysis Toolkit for Natural Language Generation (Translation, Captioning, Summarization, etc.)

waste-sorter

word2vector

Self complemented word embedding methods using CBOW，skip-Gram，word2doc matrix , word2word matrix ,基于CBOW、skip-gram、词-文档矩阵、词-词矩阵四种方法的词向量生成

ws_dan

The official TensorFlow implementation of WS-DAN.

x-deeplearning

An industrial deep learning framework for high-dimension sparse data

xuelang-yolohasst

雪浪制造AI挑战赛—视觉计算辅助良品检测 test_a 952 test_b 953

yet-another-efficientdet-pytorch

The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.

yolo-lite

All the trained models used while developing YOLO-LITE

yolo_nano

Unofficial implementation of yolo nano

yolov3-nano

YOLOV3-NANO-Tensorflow