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* added chatGLM readme * changed wording in readme * added readme for chinese-clip * added readme for convmixer * added readme for custom ops * added readme for efficientnet * added readme for llama * added readme to mnist-training * added readme to musicgen * added readme to quantized-phi * added readme to starcoder2 * added readme to whisper-microphone * added readme to yi * added readme to yolo-v3 * added readme to whisper-microphone * added space to example in glm4 readme * fixed mamba example readme to run mamba instead of mamba-minimal * removed slash escape character * changed moondream image to yolo-v8 example image * added procedure for making the reinforcement-learning example work with a virtual environment on my machine * added simple one line summaries to the example readmes without * changed non-existant image to yolo example's bike.jpg * added backslash to sam command * removed trailing - from siglip * added SoX to silero-vad example readme * replaced procedure for uv on mac with warning that uv isn't currently compatible with pyo3 * added example to falcon readme * added --which arg to stella-en-v5 readme * fixed image path in vgg readme * fixed the image path in the vit readme * Update README.md * Update README.md * Update README.md --------- Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
31 lines
1.1 KiB
Markdown
31 lines
1.1 KiB
Markdown
# candle-segformer
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- [HuggingFace Segformer Model Card][segformer]
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- [`mit-b0` - An encoder only pretrained model][encoder]
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- [`segformer-b0-finetuned-ade-512-512` - A fine tuned model for segmentation][ade512]
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## How to run the example
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If you want you can use the example images from this [pull request][pr], download them and supply the path to the image as an argument to the example.
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```bash
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# run the image classification task
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cargo run --example segformer classify candle-examples/examples/yolo-v8/assets/bike.jpg
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# run the segmentation task
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cargo run --example segformer segment candle-examples/examples/yolo-v8/assets/bike.jpg
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```
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Example output for classification:
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```text
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classification logits [3.275261e-5, 0.0008562019, 0.0008868563, 0.9977506, 0.0002465068, 0.0002241473, 2.846596e-6]
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label: hamburger
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```
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[pr]: https://github.com/huggingface/candle/pull/1617
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[segformer]: https://huggingface.co/docs/transformers/model_doc/segformer
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[encoder]: https://huggingface.co/nvidia/mit-b0
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[ade512]: https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512
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