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* Add RepVGG model. * Add RepVGG README * Extract var to top level * Replace hashmap with a match * Add a variant for the model kind + avoid some unnecessary config cloning. --------- Co-authored-by: Laurent <laurent.mazare@gmail.com>
21 lines
625 B
Markdown
21 lines
625 B
Markdown
# candle-repvgg
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A candle implementation of inference using a pre-trained [repvgg](https://arxiv.org/abs/2101.03697).
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This uses a classification head trained on the ImageNet dataset and returns the
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probabilities for the top-5 classes.
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## Running an example
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```
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$ cargo run --example repvgg --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
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loaded image Tensor[dims 3, 224, 224; f32]
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model built
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mountain bike, all-terrain bike, off-roader: 61.70%
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bicycle-built-for-two, tandem bicycle, tandem: 33.14%
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unicycle, monocycle : 4.88%
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crash helmet : 0.15%
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moped : 0.04%
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```
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