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--- |
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license: unlicense |
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language: |
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- en |
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library_name: keras |
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tags: |
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- captcha |
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- keras |
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- ocr |
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- ai captcha solving |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Ashish Chaudhary aka lolcod] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [https://github.com/lol-cod/solvingcaptchakeras] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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Direct Use |
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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. |
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Downstream Use [optional] |
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[More Information Needed] |
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Out-of-Scope Use |
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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. |
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Bias, Risks, and Limitations |
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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. |
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Recommendations |
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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. |
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How to Get Started with the Model |
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To use the model, you can leverage the following code: |
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python |
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Copy code |
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# Sample code for using the captcha-solving model |
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import keras |
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from keras.models import load_model |
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from captcha_solver import solve_captcha |
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# Load the pre-trained model |
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model = load_model('captcha_model.h5') |
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# Provide the captcha image as input |
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captcha_image = 'path/to/your/captcha.png' |
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solution = solve_captcha(model, captcha_image) |
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# Print the solution |
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print('Captcha Solution:', solution) |
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[More Information Needed] |
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Training Details |
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Training Data |
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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. |
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[More Information Needed] |
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Training Procedure |
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Preprocessing [optional] |
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[More Information Needed] |
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Training Hyperparameters |
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Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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[More Information Needed] |
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Speeds, Sizes, Times [optional] |
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[More Information Needed] |
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Evaluation |
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[More Information Needed] |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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