given the following readme, expand on the other possible startegies (balancing the dataset, etc.). How could they be used exactly?

# Image-Caption Similarity Computation

## Overview

This project computes similarity metrics between images and their corresponding captions using OpenAI's CLIP (Contrastive Language-Image Pretraining) model. 


## Installation


Setup virtual environment
```bash
pip install virtualenv  # if you don't have it already
virtualenv venv
source venv/bin/activate
```
Install Dependencies
```bash
pip install -r requirements.txt
```

## Example Usage

```
python compute_similarity.py --csv challenge_set/challenge_set.csv --images_dir challenge_set --device cuda:0
```

Required args:
- **--csv**: Path to the input CSV file containing image_file and caption columns.

- **--images_dir**: Directory containing the image files.



## Design Considerations

Choice of Model:
CLIP (ViT-B/32): chosen for its state-of-the-art performance in correlating images with textual descriptions, providing high-quality embeddings for accurate similarity measurements.

Performance Metrics: Incorporates memory usage and runtime tracking, GPU utilization, per-batch processing times and statistics on the similarity scores obtained.

We also implemented checkpointing, to allow resuming experiments in case of interruptions and we support saving image and text feature embeddings to disk, with the function *save_embeddings*.

## Curating Data for Text-to-Image Model Training:

Similarity scores can enhance the quality of image-caption datasets by:

- Identifying high-quality pairs: Pairs with high similarity scores are likely to be well-matched.
- Filtering out weak pairs: The function *filter_high_quality_pairs* removes pairs with low similarity scores and saves the filtered data in a new CSV file.

In particular, I chose to remove the image/captions with CLIP score below 0.3 (30), because they seemed to have poor quality. With this choice 35/51 images were preserved.

Other possible strategies:
Balancing the Dataset: Ensures diversity and representativeness by analyzing similarity distributions.
Guiding Data Augmentation: Informs strategies to generate more relevant images or captions.
Establishing a Feedback Loop: Iteratively refines the dataset and model performance.
Enabling Targeted Training: Facilitates fine-tuning on specific subsets with desired characteristics.


## Optimization Considerations for Large-Scale Processing:

To improve scalability and efficiency, we could consider the following:

1. Quantization

Quantizing the model for faster inference, using lower precision (e.g., INT8) arithmetic operations.
However this would have an accuracy trade-off, as quantization may lead to a small drop in model accuracy. 

2. Mixed Precision

Another possibility is using mixed precision (combining FP16 and FP32). It helps reducing memory consumption and accelerating computation on GPUs equipped with Tensor Cores. This approach maintains acceptable levels of accuracy while optimizing speed.
3. Distributed Computing

In my implementation I used DataParallel to utilise multiple GPUs on a single machine. However, for better scalability and efficiency, DistributedDataParallel (DDP) can be used. DDP is more efficient and supports scaling across multiple nodes.

Alternatively, frameworks like Huggingface's Accelerate can help simplify distributed computing setups.