---
license: apache-2.0
language:
- en
metrics:
- bleu
library_name: transformers
pipeline_tag: image-to-text
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is for the [Assurant Challenge 1](https://portal.hacklytics.io/assurant).


## Model Details

This is a BLIP Model that has been fine-tuned for 30 epochs using a custom data scrapped for web. It has been finetuned using a dataset which is a
collection of (text description of a scene, collection of images of that scene). The underlying application is to assist the insurance officer in 
verifying and approving the house rental damage claims raised by the user, and make predictions of future problems that might appear and general advice
on maintaining the house.

### Model Description

<!-- Provide a longer summary of what this model is. -->

The architecture is exactly the same as [BLIP](https://huggingface.co/Salesforce/blip-image-captioning-base).

- **Developed by:** Krishna Sri Ipsit Mantri, Varnica Chabria, Pavan Chaitanya Penagamuri, Kalyan Salkar
- **Funded by [optional]:** Used Intel Developer Cloud Credits provided for Hacklytics2024
- **Shared by [optional]:** 
- **Model type:** Fine-tuned image-to-text model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model [optional]:** BLIP

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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Should not be used for anything other than the challenge.

[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

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#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** Intel
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- 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 [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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