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* added resize to candle-onnx, not currently working * changed unreachable to bail, and bailed when both scales and sizes are set * cleanup and added other unused options for this op * cleanup * fixed image loading to make output work * cleanup and removed unused variables * removed path path creation code, and changed unwrap to ?
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:
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.
$ 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%
$ 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%