StephanAkkerman
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README.md
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Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model can be converted into a pipeline for image classification.
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```python
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model="StephanAkkerman/chart-recognizer",
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```
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## Citing & Authors
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Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model can be converted into a pipeline for image classification.
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```python
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import timm
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import torch
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from PIL import Image
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from timm.data import resolve_data_config, create_transform
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# Load and set model to eval mode
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model = timm.create_model("hf_hub:StephanAkkerman/chart-recognizer", pretrained=True)
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model.eval()
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# Create transform and get labels
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transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))
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labels = model.pretrained_cfg["label_names"]
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# Load and preprocess image
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image = Image.open("img/examples/tweet_example.png").convert("RGB")
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x = transform(image).unsqueeze(0)
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# Get model output and apply softmax
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probabilities = torch.nn.functional.softmax(model(x)[0], dim=0)
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# Map probabilities to labels
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output = {label: prob.item() for label, prob in zip(labels, probabilities)}
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# Print the predicted probabilities
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print(output)
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```
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## Citing & Authors
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