transformers pytorch tensorboard vit image-classification real estate en doi:10.57967/hf/1255 license:mit model-index autotrain_compatible endpoints_compatible region:us
---
tags:
- image-classification
- real estate
metrics:
- accuracy
model-index:
- name: Rooms
  results:
  - task:
      name: Image Classification
      type: image-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.911522626876831
license: mit
language:
- en
---

# Model Name: RoomS

Model Description: The 'Room's model is an image classification model specifically developed for the real estate domain. 
It has been trained on the Indoors dataset provided by MIT, which contains a diverse range of indoor room images. 
The model leverages the power of pre-trained models from Hugging Face, which have been fine-tuned using Google Research Labs' vision transformer model.

Model Performance: The 'Room's model achieves state-of-the-art performance in classifying different types of rooms within real estate images. 
It has been extensively evaluated and validated on various metrics, including accuracy, precision, recall, and F1-score.

Model Limitations: While the 'Room's model demonstrates high accuracy in most scenarios, it may encounter challenges in cases where images have 
poor lighting conditions, occlusions, or uncommon room layouts. Additionally, the model's performance may vary for room types not present in the 
Indoors dataset.

Ethical Considerations: To ensure ethical usage, it is important to avoid biased or discriminatory training data. 
The 'Room's model has been trained on a diverse dataset to mitigate any potential biases. However, it is recommended to regularly evaluate and update 
the model to address any emerging biases.

Social Impact: The 'Room's model can significantly contribute to the real estate industry by automating room classification tasks, improving efficiency,
and enhancing user experiences. Its accurate predictions can assist in property search, virtual tours, and real estate marketing efforts.

Licenses and Terms of Use: The 'Room's model is subject to the licenses and terms of use provided by the original dataset creators, MIT and 
Google Research Labs. It is crucial to comply with these licenses and terms when using the model.

Acknowledgments: We are grateful to our friends at Hugging Face for providing the pre-trained models and Google Research Labs for sharing the 
fine-tuned vision transformer model. Additionally, we extend our thanks to MIT for providing the Indoors dataset, which forms the foundation of our model.

Disclaimer: The 'Room's model is a product of machine learning algorithms and, like any AI model, it may not be entirely perfect. It is recommended 
to exercise caution and human judgment when interpreting the model's outputs or making decisions based on them.

Please note that this Model Card serves as a brief summary of the 'Room's model and is not an exhaustive documentation. For detailed information, 
please refer to the respective licenses, terms of use, and documentation provided by the dataset creators and model providers.


Note: The 'Room's model is made freely available to the public for download and use in any useful use cases. We encourage users to explore and 
leverage the model's capabilities to enhance real estate applications and contribute to the advancement of the industry.

' - RAMA nrusimhadri' 🤗.

## Example Images


#### bathroom

![bathroom](NEW/Rooms/bathroom.jpg)

#### bedroom

![bedroom](NEW/Rooms/bedroom.jpg)

#### children_room

![children_room](NEW/Rooms/children_room.jpg)

#### closet

![closet](NEW/Rooms/closet.jpg)

#### dining_room

![dining_room](NEW/Rooms/dining_room.jpg)

#### kitchen

![kitchen](NEW/Rooms/kitchen.jpg)

#### livingroom

![livingroom](NEW/Rooms/livingroom.jpg)

#### pantry

![pantry](NEW/Rooms/pantry.jpg)

#### stairscase

![stairscase](NEW/Rooms/stairscase.jpg)

#### winecellar

![winecellar](NEW/Rooms/winecellar.jpg)