arxiv:1910.09700 arxiv:2201.12086 region:us
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# Model Card for SDSU AI Club Image Captioning Model

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

## Model Details

### Model Description
This image captioning model uses the transformers, datasets, and pytorch libraries to fine tune a pre-trained BLIP model with a subset of the Flickr 30k image dataset. The model then generates a new caption for the image.




- **Developed by:** Charisma Meyer, Daniel Aguilar, Erica Lee, Evan Tardiff, Steven Trujillo, Vincent Huynh
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Image Captioning
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Salesforce/blip-image-captioning-base

### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper:** "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation" by Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### 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.

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## Training Details

### Training Data

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### Training Procedure 

The training procedure was a process where we took a large dataset and a pre-trained model card from Hugging Face, and created a 
model that performs the image captioning function on this dataset. This generates a tailored caption given an input image. The biggest problem encountered was
that our computers could not train a model with the given size of the dataset as it took too much RAM, which crashed our computers. Figuring out to be able to 
train this model using the dataset given, our solution included creating a subset of the data provided in the original set as well as manipulating the batch size to increase the number of images being read at a time. We trained the model with this new dataset which allowed us to successfully train the model in a more reasonable amount of time.

#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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

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## 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:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation
@misc{li2022blip,
      title={BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, 
      author={Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi},
      year={2022},
      eprint={2201.12086},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{,
  author={younesbelkada, RocketKnight1},
  title={Fine-tune BLIP using Hugging Face transformers and datasets},
  year={2023},
  url={\url{https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb}}
}

## Glossary [optional]

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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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