README - Candle — ContextQMD

Using ONNX models in Candle

This example demonstrates how to run ONNX based models in Candle.

It contains small variants of two models, SqueezeNet (default) and EfficientNet.

You can run the examples with following commands:

bash

cargo run --example onnx --features=onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg

Use the --which flag to specify explicitly which network to use, i.e.

bash

$ cargo run --example onnx --features=onnx --release -- --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg

    Finished release [optimized] target(s) in 0.21s
     Running `target/release/examples/onnx --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
loaded image Tensor[dims 3, 224, 224; f32]
unicycle, monocycle                               : 83.23%
ballplayer, baseball player                       : 3.68%
bearskin, busby, shako                            : 1.54%
military uniform                                  : 0.78%
cowboy hat, ten-gallon hat                        : 0.76%

bash

$ cargo run --example onnx --features=onnx --release -- --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg

    Finished release [optimized] target(s) in 0.20s
     Running `target/release/examples/onnx --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
loaded image Tensor[dims 224, 224, 3; f32]
bicycle-built-for-two, tandem bicycle, tandem     : 99.16%
mountain bike, all-terrain bike, off-roader       : 0.60%
unicycle, monocycle                               : 0.17%
crash helmet                                      : 0.02%
alp                                               : 0.02%