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https://github.com/Media-Smart/vedacls
A classification toolbox based on PyTorch
https://github.com/Media-Smart/vedacls
classification-toolbox pytorch
Last synced: 3 months ago
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A classification toolbox based on PyTorch
- Host: GitHub
- URL: https://github.com/Media-Smart/vedacls
- Owner: Media-Smart
- License: apache-2.0
- Created: 2020-05-22T09:50:20.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-02-04T09:04:05.000Z (11 months ago)
- Last Synced: 2024-08-01T03:44:08.866Z (5 months ago)
- Topics: classification-toolbox, pytorch
- Language: Python
- Homepage:
- Size: 95.7 KB
- Stars: 23
- Watchers: 4
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Introduction
vedacls is an open source classification toolbox based on PyTorch.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Installation
### Requirements
- Linux
- Python 3.6+
- PyTorch 1.4.0 or higher
- CUDA 9.0 or higher
We have tested the following versions of OS and softwares:
- OS: Ubuntu 16.04.6 LTS
- CUDA: 10.2
- PyTorch 1.4.0
- Python 3.6.9
### Install vedacls
1. Create a conda virtual environment and activate it.
```shell
conda create -n vedacls python=3.6.9 -y
conda activate vedacls
```
2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), *e.g.*,
```shell
conda install pytorch torchvision -c pytorch
```
3. Clone the vedacls repository.
```shell
git clone https://github.com/Media-Smart/vedacls.git
cd vedacls
vedacls_root=${PWD}
```
4. Install dependencies.
```shell
pip install -r requirements.txt
```
## Prepare data
The catalogue structure of dataset supported by vedacls toolbox is as follows:
```shell
data/
├── train
│ ├── 0
│ │ ├── XXX.jpg
│ │
│ ├── 1
│ ├── 2
│ ├── ...
│
├── val
│ ├── 0
│ ├── 1
│ ├── 2
│ ├── ...
│
├── test
├── 0
├── 1
├── 2
├── ...
```
## Train
1. Config
Modify some configuration accordingly in the config file like `configs/resnet18.py`
2. Run
```shell
python tools/train.py configs/resnet18.py
```
Snapshots and logs will be generated at `${vedacls_root}/workdir/resnet18`
## Test
1. Config
Modify some configuration accordingly in the config file like `configs/resnet18.py`
2. Run
```shell
python tools/test.py configs/resnet18.py checkpoint_path
```
## Inference
1. Config
Modify some configuration accordingly in the config file like `configs/resnet18.py`
2. Run
```shell
python tools/inference.py configs/resnet18.py checkpoint_path image_path
```
## Deploy
1. Install volksdep following the [official instructions](https://github.com/Media-Smart/volksdep)
2. Benchmark(optional)
```shell
python tools/deploy/benchmark.py configs/resnet18.py checkpoint_path image_path
```
More available arguments are detailed in [tools/deploy/benchmark.py](https://github.com/Media-Smart/vedacls/blob/master/tools/deploy/benchmark.py)
3. Export model as ONNX or TensorRT engine format
```shell
python tools/deploy/export.py configs/resnet18.py checkpoint_path image_path out_model_path
```
More available arguments are detailed in [tools/deploy/export.py](https://github.com/Media-Smart/vedacls/blob/master/tools/deploy/export.py)
4. Inference SDK
You can refer to [FlexInfer](https://github.com/Media-Smart/flexinfer/blob/master/examples/classifier.py) for details.
## Contact
This repository is currently maintained by Chenhao Wang ([@C-H-Wong](http://github.com/C-H-Wong)), Hongxiang Cai ([@hxcai](http://github.com/hxcai)), Yichao Xiong ([@mileistone](https://github.com/mileistone)).
## Credits
We got a lot of code from [mmcv](https://github.com/open-mmlab/mmcv) and [mmdetection](https://github.com/open-mmlab/mmdetection), thanks to [open-mmlab](https://github.com/open-mmlab).