Instructions to use tcsenpai/Captchot-Images_Captcha_Images_Prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use tcsenpai/Captchot-Images_Captcha_Images_Prediction with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://tcsenpai/Captchot-Images_Captcha_Images_Prediction") - Notebooks
- Google Colab
- Kaggle
- Captchot Images
** Captcha Images Prediction **
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
** Dataset **
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...).
The complete list of labels can be found in Structure.png.
** Files **
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.
Captchot_Images.zip contains the exported models for different frameworks: tensorflow, onnx, keras, a webapp demo, a python ready to use script and coreml .
** Model **
The model has been trained with EfficientNet at 1000 iterations and early stop to avoid overfitting. The full network has been trained.
** Useless info **
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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