Awesome
OneFlow-Models
Models and examples implement with OneFlow(version >= 0.5.0).
Introduction
English | 简体中文
OneFlow-Models is an open source repo which contains official implementation of different models built on OneFlow. In each model, we provide at least two scripts train.sh
and infer.sh
for a quick start. For each model, we provide a detailed README
to introduce the usage of this model.
Features
- various models and pretrained weight
- easy use for beginners
Quick Start
Please check our the following demos for a quick start
- image classification quick start lenet demo
- speaker recognition speaker identification demo
Model List
<details> <summary> <b> Image Classification </b> </summary>- Lenet
- Alexnet
- VGG16/19
- Resnet50
- InceptionV3
- Densenet
- Resnext50_32x4d
- Shufflenetv2
- MobilenetV2
- mobilenetv3
- Ghostnet
- RepVGG
- DLA
- PoseNet
- Scnet
- Mnasnet
- ViT
- SincNet
- Wav2Letter
- AM_MobileNet1D
- Speech-Emotion-Analyer
- Speech-Transformer
- CycleGAN-VC2
- MaskCycleGAN-VC
- StarGAN-VC
- Adaptive_Voice_Conversion
Installation and Environment setup
Install Oneflow
https://github.com/Oneflow-Inc/oneflow#install-with-pip-package
Build custom ops from source
In the root directory, run:
mkdir build
cd build
cmake ..
make -j$(nrpoc)
Example of using ops:
from ops import RoIAlign
pooler = RoIAlign(output_size=(14, 14), spatial_scale=2.0, sampling_ratio=2)