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--- |
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tags: |
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- image-classification |
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metrics: |
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- val_loss |
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model-index: |
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- name: Receptor |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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metrics: |
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- name: Validation Loss |
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type: val_loss |
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value: 0.001461497158743441 |
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license: mit |
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language: |
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- en |
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--- |
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# Receptor: The Dawn of Clarity (An Image Classification Model trained to classify documents). |
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In the meticulous domain of real estate, a realm filled with diverse documentation, emerges 'Receptor', a model designed to streamline the classification of |
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crucial documents. With its roots firmly embedded in robust algorithmic soil, 'Receptor' sets forth on a mission to declutter the digital documentation landscape, |
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making the management of real estate documents a breeze. |
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Armed with the prowess of Image Classification, 'Receptor' delves into piles of documents, categorizing them with precision. Each deed, lease agreement, and inspection |
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report is meticulously sorted, paving the way for a seamless documentation process. The essence of every document is respected and made easily accessible, echoing the |
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promise of efficiency and accuracy. |
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Under the mentorship of 'Perceptor', the model evolved, mastering the art of handling a wide array of document types prevalent in the real estate cosmos. |
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Every stride 'Receptor' took in the digital realm resonated with the promise of a well-structured documentation system. |
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Acknowledgements: |
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We express our sincere gratitude to Roboflow for providing the indispensable datasets that fueled 'Receptor's training journey. The achievement of a remarkable |
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Validation Loss value of 0.001461497158743441 stands as a testament to the quality of data and the efficacy of 'Receptor' in managing real estate documentation. |
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Licensing and Usage: |
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'Receptor: The Dawn of Clarity' is shared under the MIT license, encouraging enthusiasts and professionals to explore, adapt, and enhance this model for their |
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respective use cases. While 'Receptor' serves as a solid foundation, we emphasize the importance of fine-tuning to cater to the specific nuances of your domain, |
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ensuring optimum performance and accuracy. |
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Harnessing the power of Google's Vision Transformer (ViT) as a pre-trained model, 'Receptor' delves into the intricacies of real estate documents with a sharp focus |
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on identifying invoices and receipts at the outset. The model is crafted with a vision to expand its horizons, by adding more document types and fine-tuning its |
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capabilities to suit specific business needs and use cases. |
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As 'Receptor' unfolds the chapters of organized real estate documentation, we invite you on this journey towards a streamlined and efficient documentation process. |
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Explore 'Receptor', delve into its code, and let's together step towards a future where every document finds its rightful place in the digital realm, contributing |
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to the broader narrative of clarity and order in real estate documentation. |
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Your companion in this digital endeavor, |
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RAMA Nrusimhadri |
