# onnx_runtime_cpp

**Repository Path**: tass2010/onnx_runtime_cpp

## Basic Information

- **Project Name**: onnx_runtime_cpp
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 2
- **Created**: 2020-12-05
- **Last Updated**: 2025-07-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# small c++ library to quickly use [onnxruntime](https://github.com/microsoft/onnxruntime) to deploy deep learning models #

## TODO

- [x] Support inference of multi-inputs, multi-outputs
- [x] Examples for famous models, like yolov3, mask-rcnn, [ultra-light-weight face detector](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB). Might consider supporting more if requested
- [ ] Batch-inference

## Installation ##
- build onnxruntime from source with the following script
```bash
    sudo bash ./scripts/install_onnx_runtime.sh
```

## How to build ##
***

```bash
make all

# build apps
make apps
```

## How to test apps ##
***

### Image Classification With Squeezenet ###
***

```bash
# after make apps
./build/examples/TestImageClassification ./data/squeezenet1.1.onnx ./data/images/dog.jpg
```
the following result can be obtained
```
264 : Cardigan, Cardigan Welsh corgi : 0.391365
263 : Pembroke, Pembroke Welsh corgi : 0.376214
227 : kelpie : 0.0314975
158 : toy terrier : 0.0223435
230 : Shetland sheepdog, Shetland sheep dog, Shetland : 0.020529
```

### Object Detection With Tiny-Yolov2 trained on VOC dataset (with 20 classes) ###
***
- Download model from onnx model zoo: [HERE](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/yolov2)

- The shape of the output would be
```text
    OUTPUT_FEATUREMAP_SIZE X OUTPUT_FEATUREMAP_SIZE * NUM_ANCHORS * (NUM_CLASSES + 4 + 1)
    where OUTPUT_FEATUREMAP_SIZE = 13; NUM_ANCHORS = 5; NUM_CLASSES = 20 for the tiny-yolov2 model from onnx model zoo
```
- Test tiny-yolov2 inference apps
```bash
# after make apps
./build/examples/tiny_yolo_v2 [path/to/tiny_yolov2/onnx/model] ./data/images/dog.jpg
```
- Test result

![tinyyolov2 test result](./data/images/result.jpg)

### Object Instance Segmentation With MaskRCNN trained on MS CoCo Dataset (80 + 1(background) clasess) ###
***
- Download model from onnx model zoo: [HERE](https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/mask-rcnn)

- As also stated in the url above, there are four outputs: boxes(nboxes x 4), labels(nboxes), scores(nboxes), masks(nboxesx1x28x28)
- Test mask-rcnn inference apps
```bash
# after make apps
./build/examples/mask_rcnn [path/to/mask_rcnn/onnx/model] ./data/images/dogs.jpg
```

- Test results:

![dogs maskrcnn result](./data/images/dogs_maskrcnn_result.jpg)

![indoor maskrcnn result](./data/images/indoor_maskrcnn_result.jpg)

### Bird Detection With Yolov3 trained on inhouse dataset ###
***

- Details about this personal repo for training this model will be updated later

- Test results:

![bird detection result](./data/images/bird_detection_result.jpg)

### [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) ###
***

- App to use onnx model trained with famous light-weight [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB)
- Sample weight has been saved [./data/version-RFB-640.onnx](./data/version-RFB-640.onnx)
- Test inference apps
```bash
# after make apps
./build/examples/ultra_light_face_dectector ./data/version-RFB-640.onnx ./data/images/endgame.jpg
```

- Test results:
![ultra light weight face result](./data/images/endgame_result.jpg)