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README.md
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* Captchot Images
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** Captcha Images Prediction
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This model is the images version of the Captchot project, a from-scratch experimental project containing two models to predict image based captchas and text-to-image based captchas
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The dataset used is a dataset of 300k+ images taken from real-world captcha dumps (mainly reCaptcha 1 and 2 and hCaptcha plus others found to be used online) divided in categories (such as trains, cars, stairs...).
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The complete list of labels can be found in Structure.png.
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You can find the training overall informations in training_info.png, the .zip structure in Structure.png and the demo webapp screenshot in Webapp.png.
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Captchot_Images.zip contains the exported models for different frameworks: tensorflow, onnx, keras, a webapp demo, a python ready to use script and coreml .
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The model has been trained with EfficientNet at 1000 iterations and early stop to avoid overfitting. The full network has been trained.
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The training has been done on a M1 Pro Apple Silicon chip and took approximately 20h to fully train (plus the dataset importing phase).
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* Captchot Images
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** Captcha Images Prediction **
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This model is the images version of the Captchot project, a from-scratch experimental project containing two models to predict image based captchas and text-to-image based captchas
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** Dataset **
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The dataset used is a dataset of 300k+ images taken from real-world captcha dumps (mainly reCaptcha 1 and 2 and hCaptcha plus others found to be used online) divided in categories (such as trains, cars, stairs...).
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The complete list of labels can be found in Structure.png.
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** Files **
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You can find the training overall informations in training_info.png, the .zip structure in Structure.png and the demo webapp screenshot in Webapp.png.
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Captchot_Images.zip contains the exported models for different frameworks: tensorflow, onnx, keras, a webapp demo, a python ready to use script and coreml .
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** Model **
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The model has been trained with EfficientNet at 1000 iterations and early stop to avoid overfitting. The full network has been trained.
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** Useless info **
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The training has been done on a M1 Pro Apple Silicon chip and took approximately 20h to fully train (plus the dataset importing phase).
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