• Support inference of multi-inputs, multi-outputs
• Examples for famous models, like yolov3, mask-rcnn. Might consider supporting more if requested
• Batch-inference

• build onnxruntime from source with the following script

    sudo bash ./scripts/install_onnx_runtime.sh

make all

# build apps
make apps

# 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

• Download model from onnx model zoo: HERE

• The shape of the output would be

    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

# after make apps
./build/examples/tiny_yolo_v2 [path/to/tiny_yolov2/onnx/model] ./data/images/dog.jpg

• Test result

• Download model from onnx model zoo: HERE

• 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

# after make apps
./build/examples/mask_rcnn [path/to/mask_rcnn/onnx/model] ./data/images/dogs.jpg

• Test results:

• Details about the my personal repo for training this model will be updated later

• Test results: