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a226a9736b
* Support different resolutions in load_image() * Added MobilenetV4 model. * Add MobileNetv4 to README
19 lines
733 B
Markdown
19 lines
733 B
Markdown
# candle-mobilenetv4
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[MobileNetV4 - Universal Models for the Mobile Ecosystem](https://arxiv.org/abs/2404.10518)
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This candle implementation uses pre-trained MobileNetV4 models from timm for inference.
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The classification head has been trained on the ImageNet dataset and returns the probabilities for the top-5 classes.
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## Running an example
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```
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$ cargo run --example mobilenetv4 --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which medium
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loaded image Tensor[dims 3, 256, 256; f32]
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model built
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unicycle, monocycle : 20.18%
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mountain bike, all-terrain bike, off-roader: 19.77%
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bicycle-built-for-two, tandem bicycle, tandem: 15.91%
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crash helmet : 1.15%
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tricycle, trike, velocipede: 0.67%
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```
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