TianzhongSong/Model-Compression-Keras

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21
Rank 1,077,981 (Top 22 %)
Language
Python
License
MIT License
Created about 6 years ago
Updated over 2 years ago

TianzhongSong/Model-Compression-Keras

TianzhongSong

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cnn compression for keras

Real-Time-Action-Recognition

Real-time pose estimation and action recognition

awesome-SynthText

A curated list of awesome synthetic data for text location and recognition

Person-Segmentation-Keras

Person segmentation with Keras (SegNet, Unet, etc.)

C3D-keras

train C3D with keras for action recognition

Tensorflow-quantization-test

Tensorflow quantization (float32-->int8) inference test

keras-FP16-test

Evaluating deep learning models with float16 dtype in Keras, float16 inference

3D-ConvNets-for-Action-Recognition

3D ConvNets for Action Recognition with Keras (3d ResNet, 3d DenseNet, 3d Inception, C3D, 3d dense resnet)

Multi-3DCNN-for-action-recognition

Multi 3DCNN for action recognition using global and local information

Shanghai-House-Hunting

利用Python爬虫+高德api选择租房

PointNet-Keras

Implementating PointNet with Keras

Dataset-maker-for-action-recognition

generate action data using openpose or SSD

Unet-for-Person-Segmentation

Person segmentation using Unet

text2image

生成用于训练CRNN的图片数据

3D-Dense-Residual-Network-for-Action-Recognition

3D Dense Residual Network for Action Recognition

Network-Slimming-Keras

Implementation of "Learning Efficient Convolutional Networks through Network Slimming" (ICCV2017)

OCR_REST_api

深度学习OCR REST api (Flask+Redis+Keras)

PSMNet-TensorFlow

A TensorFlow port of Pyramid Stereo Matching Network (CVPR2018)

Python3-Spiders

一些python爬虫程序

Residual-Networks-with-Dense-Block

ResNets with Dense Block

caffe_kld_loss

caffe KL Divergence Loss layer for matching prob distribution

Incremental-object-recognition

利用inception v4 提取特征，构建了一个简易增量式图片识别程序

Multi-Person-Tracking-Using-SSD-Detector

Multi-Person-Tracking-Using-SSD-Detector

Awesome-Model-Compression

A curated list of awesome model compression method for CNNs

AocNet

Build CNN only use 1*1 conv