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  1. app.py +1 -1
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@@ -25,7 +25,7 @@ These classes represent common plant species in Danish agriculture at different
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  A pretrained ResNet34 model was fine-tuned using fastai vision library. The error rate achived on the validation dataset is 2.5% after 4 epochs.
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  Currently the model does not generalize well on the out-of-domain images. Probably because of the specificity of the dataset.
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  The images in the dataset are of a single plant taken from the top and on a single background of a grit surface. Some images have barcode labels that might be unique to the particular plant.
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- The last two images in the examples are not from the dataset. They are getting wrongly classified as <b>Common Chickweed</b> even though they are <b>Small-flowered Cranesbill</b> (compare with the second image in the examples)
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  <p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>
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  </div>"""
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  A pretrained ResNet34 model was fine-tuned using fastai vision library. The error rate achived on the validation dataset is 2.5% after 4 epochs.
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  Currently the model does not generalize well on the out-of-domain images. Probably because of the specificity of the dataset.
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  The images in the dataset are of a single plant taken from the top and on a single background of a grit surface. Some images have barcode labels that might be unique to the particular plant.
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+ The last two images in the examples are not from the dataset. They are getting wrongly classified as <b>Scentless Mayweed</b> even though they are <b>Small-flowered Cranesbill</b> (compare with the second image in the examples)
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  <p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>
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  </div>"""
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