lily-ice's starred repositories
deep-learning-for-image-processing
deep learning for image processing including classification and object-detection etc.
ssd.pytorch
A PyTorch Implementation of Single Shot MultiBox Detector
SSD-Tensorflow
Single Shot MultiBox Detector in TensorFlow
tensorflow-yolov4-tflite
YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
android-demo-app
PyTorch android examples of usage in applications
voicefilter
Unofficial PyTorch implementation of Google AI's VoiceFilter system
DeepRL-TensorFlow2
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
Basic_CNNs_TensorFlow2
A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).
DeepLearning-TensorFlow2
本仓库我将使用谷歌TensorFlow2框架逐一复现经典的卷积神经网络：LeNet、AlexNet、VGG系列、GooLeNet、ResNet 系列、DenseNet 系列，以及现在比较经典的目标检测网络、语义分割网络等。
openvino2tensorflow
This script converts the ONNX/OpenVINO IR model to Tensorflow's saved_model, tflite, h5, tfjs, tftrt(TensorRT), CoreML, EdgeTPU, ONNX and pb. PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW). And the conversion from .pb to saved_model and from saved_model to .pb and from .pb to .tflite and saved_model to .tflite and saved_model to onnx. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support.
yolov5s_android
Run yolov5s on Android device!
VoiceSplit
VoiceSplit: Targeted Voice Separation by Speaker-Conditioned Spectrogram
facenet_mtcnn_to_mobile
convert facenet and mtcnn models from tensorflow to tensorflow lite and coreml （使用 TFLite 将 FaceNet 和 MTCNN 移植到移动端）
pytorch-onnx-tensorflow-pb
Converting A PyTorch Model to Tensorflow pb using ONNX
ClassificationForAndroid
在Android使用深度学习模型实现图像识别，本项目提供了多种使用方式，使用到的框架如下：Tensorflow Lite、Paddle Lite、MNN、TNN
Computation-offloading-based-on-DQN
Joint strategy design on edge computing offloading based on deep reinforcement learning
TensorFlow2.0-for-Deep-Reinforcement-Learning
TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
PaddlePaddle-SSD
基于PaddlePaddle实现的SSD，包括MobileNetSSD，MobileNetV2SSD，VGGSSD，ResNetSSD
DNN-Partition-demo
A DNN model partition demo
whitebook-shangmi
商用密码技术最佳实践白皮书
dnn_benchmark
Application to test inference frameworks for Android
java-word-template-filler
A Java based application to fill data of Word (docx) templates.
mz-office-document-api
Library to fill out Libre-/OpenOffice (odt) and Microsoft Word Documents (docx) containing place holders and nested Tables. The data model is independent of the resulting document format. The library is not intended to create documents from scratch, instead to work with templates with placeholders.
TFlite_android_test
Tensorflow-lite移动端测试自己的模型
early_exit_dnn_analysis
This code contains all code developed to analyze early-exit DNNs considering an edge-cloud architecture.
PytorchToTFLite
Tutorial on converting Pytorch model to TFLite for android usage