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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: image-classification |
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datasets: |
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- DannyLuna/recaptcha-57k-images-dataset |
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tags: |
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- code |
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--- |
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# recaptcha-classification-57k |
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YOLO based image classification model for reCAPTCHA images. This |
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model is used by [`vision-ai-recaptcha-solver`](https://github.com/DannyLuna17/VisionAIRecaptchaSolver) to solve reCAPTCHA challenges. |
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## Model summary |
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- Task: image classification |
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- Format: ONNX (`recaptcha_classification_57k.onnx`) and PyTorch (`recaptcha_classification_57k.pt`) |
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- Architecture: Ultralytics YOLO classification |
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- Labels: 14 classes (13 target classes + `other`) |
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- Trained on a [57k images dataset](https://huggingface.co/datasets/DannyLuna/recaptcha-57k-images-dataset). |
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## Labels |
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Target classes in this model: |
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- bicycle |
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- bridge |
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- bus |
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- car |
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- chimney |
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- crosswalk |
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- fire hydrant |
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- motorcycle |
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- mountain |
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- palm tree |
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- stairs |
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- tractor |
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- traffic light |
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The `other` class represents non-target/background tiles and is intentionally |
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not treated as a match by the solver. |
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## Intended use |
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- Classifying individual reCAPTCHA tile images as part of research or testing |
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workflows. |
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- Integrated use with the [`vision-ai-recaptcha-solver`](https://github.com/DannyLuna17/VisionAIRecaptchaSolver) package. |
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## Training data |
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Trained on the dataset: |
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[`DannyLuna/recaptcha-57k-images-dataset`](https://huggingface.co/datasets/DannyLuna/recaptcha-57k-images-dataset). |
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## Training procedure |
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Trained with Ultralytics YOLO classification and exported to ONNX with dynamic |
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input shapes. |
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## How to use |
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### Ultralytics (recommended) |
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```python |
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from ultralytics import YOLO |
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model = YOLO("recaptcha_classification_57k.onnx", task="classify") |
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results = model("tile.jpg") |
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probs = results[0].probs |
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top1_idx = int(probs.top1) |
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print(model.names[top1_idx], float(probs.top1conf)) |
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``` |
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### vision-ai-recaptcha-solver |
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```python |
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from vision_ai_recaptcha_solver import RecaptchaSolver, SolverConfig |
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with RecaptchaSolver(SolverConfig()) as solver: |
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result = solver.solve( |
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website_key="your_site_key", |
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website_url="https://example.com/your-page-with-recaptcha", |
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) |
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print(result.token) |
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``` |
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## Model files |
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- `recaptcha_classification_57k.onnx` |
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- `recaptcha_classification_57k.pt` |
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## Limitations |
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- The reCAPTCHA system changes over time; accuracy may degrade on new variants. |
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- False positives are possible on visually similar objects. |
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## Ethical considerations |
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Use this model only for lawful, authorized purposes such as research, benchmarking, or |
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testing in controlled environments. |
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## License |
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MIT License. See the repository `LICENSE` file for details. |
