Model Card for Model ID

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Model Details

Model Description

Developed by: [Ashish Chaudhary aka lolcod]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [https://github.com/lol-cod/solvingcaptchakeras]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Direct Use The model is designed for solving 4-lettered captchas with an 80% accuracy rate. It can be directly employed for captcha-solving tasks without the need for fine-tuning or integration into a larger ecosystem or application.

Downstream Use [optional] [More Information Needed]

Out-of-Scope Use The model is not intended for tasks beyond solving 4-lettered captchas. It may not perform well on captchas with a different format or on tasks unrelated to captcha-solving.

Bias, Risks, and Limitations The model's performance may vary based on the complexity and variability of captchas. It may not generalize well to captchas with different characteristics or lengths. Additionally, there is a risk of misclassification, leading to incorrect solutions. The model might be sensitive to changes in background, font styles, or other captcha variations.

Recommendations Users, both direct and downstream, should be aware of the model's limitations and potential biases. It is recommended to assess the performance on a diverse set of captchas to understand the model's capabilities and shortcomings.

How to Get Started with the Model To use the model, you can leverage the following code:

python Copy code

Sample code for using the captcha-solving model

import keras from keras.models import load_model from captcha_solver import solve_captcha

Load the pre-trained model

model = load_model('captcha_model.h5')

Provide the captcha image as input

captcha_image = 'path/to/your/captcha.png' solution = solve_captcha(model, captcha_image)

Print the solution

print('Captcha Solution:', solution) [More Information Needed]

Training Details Training Data The model was trained on a dataset of 4-lettered captchas. For more detailed information about the training data, refer to the accompanying Dataset Card.

[More Information Needed]

Training Procedure Preprocessing [optional] [More Information Needed]

Training Hyperparameters Training regime: [More Information Needed] [More Information Needed]

Speeds, Sizes, Times [optional] [More Information Needed]

Evaluation [More Information Needed]

Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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