itchybeetle3/img_caption_generation

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title: Vision Language Model ELC

emoji: 📷

colorFrom: blue

colorTo: purple

sdk: docker

app\_port: 7860

pinned: false

\--- Vision-Language Model for Image Understanding ===

\\ELC Academic Project\\ — Demonstrating Image Captioning \& Visual Question Answering using state-of-the-art pretrained BLIP models.

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Table of Contents

1. 1.Objective
2. 2.Methodology
3. 3.Models Used
4. 4.Datasets Used
5. 5.Evaluation Metrics
6. 6.Results
7. 7.Project Structure
8. 8.Installation \& Usage
9. 9.Conclusion
10. 10.References

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1\. Objective

This project implements a Vision-Language Model (VLM) system capable of understanding images in two ways:

The system leverages BLIP (Bootstrapping Language-Image Pre-training) pretrained models from Salesforce Research and is deployed as an interactive web application using Streamlit.

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2\. Methodology

2.1 System Architecture

┌─────────────────────────────────────────────────────────┐
│                     Streamlit UI (app.py)                │
│  ┌──────────────┐   ┌─────────────────┐   ┌──────────┐  │
│  │ Image Upload │   │  Caption Module │   │ VQA Mod. │  │
│  └──────┬───────┘   └────────┬────────┘   └────┬─────┘  │
│         │                    │                  │         │
│         └──────────┬─────────┘                  │         │
│                    ▼                             ▼         │
│            caption.py                        vqa.py       │
│     (BLIP-Captioning-Base)           (BLIP-VQA-Base)     │
└─────────────────────────────────────────────────────────┘

2.2 Image Captioning Pipeline

1. 1.Input: User uploads an image (JPG, PNG, WEBP, BMP).
2. 2.Preprocessing: Image is converted to RGB and processed via BlipProcessor (resized to 384×384, normalized).
3. 3.Inference: BlipForConditionalGeneration encodes the image with a ViT-base visual encoder and decodes a caption autoregressively with a 12-layer transformer language decoder.
4. 4.Output: Decoded textual caption + inference time.

2.3 Visual Question Answering Pipeline

1. 1.Input: User uploads an image and types a natural-language question.
2. 2.Preprocessing: Image + question text are jointly processed by BlipProcessor.
3. 3.Inference: BlipForQuestionAnswering fuses visual and textual embeddings via cross-attention and generates an answer token-by-token.
4. 4.Output: Decoded answer string + inference time.

2.4 Evaluation Pipeline

• —Captioning evaluation (evaluation.py): Loads Flickr8k images and reference captions, generates predictions with the captioning model, and computes corpus-level BLEU and ROUGE metrics.
• —VQA evaluation (evaluation.py): Loads a VQAv2 val2014 subset, runs inference for each question, and computes VQAv2 soft accuracy.

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3\. Models Used

3.1 Salesforce/blip-image-captioning-base

3.2 Salesforce/blip-vqa-base

3.3 Why BLIP?

BLIP introduces a novel Bootstrapping strategy:

• —A captioner generates synthetic captions for noisy web images.
• —A filter removes noisy captions.
• —Clean synthetic + human-annotated pairs are used for pre-training.

This makes BLIP significantly more robust on downstream tasks compared to CLIP and ViLT baselines.

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4\. Datasets Used

4.1 Flickr8k (Image Captioning)

Setup:

datasets/
└── flickr8k/
    ├── Images/         ← .jpg image files
    └── captions.txt    ← CSV: image\_filename,caption

4.2 VQAv2 (Visual Question Answering)

Setup:

datasets/
└── vqav2/
    ├── val2014/                                          ← COCO val2014 images
    ├── v2\_OpenEnded\_mscoco\_val2014\_questions.json
    └── v2\_mscoco\_val2014\_annotations.json

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5\. Evaluation Metrics

5.1 Image Captioning Metrics

BLEU (Bilingual Evaluation Understudy)

BLEU measures n-gram precision between generated and reference captions.

BLEU-N = BP × exp( Σ wₙ · log pₙ )

Where:

• —pₙ = modified n-gram precision
• —BP = brevity penalty (penalises short outputs)
• —wₙ = 1/N (uniform weights)

We report BLEU-1, BLEU-2, BLEU-3, BLEU-4. Higher is better; BLEU-4 ∈ \[0, 1].

ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

ROUGE measures recall-oriented overlap between generated and reference text.

5.2 VQA Metric

VQAv2 Soft Accuracy

Accuracy(answer) = min( 1,  count(answer in 10 human answers) / 3 )

An answer is considered correct if at least 3 out of 10 human annotators gave the same answer. This soft metric handles answer variability gracefully.

5.3 Performance Metric

Average Inference Time (seconds):

avg\_time = Σ inference\_time\_i  /  N

Measured using time.perf\_counter() for each sample, excluding model loading.

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6\. Results

\\Note:\\ Results below are representative benchmarks obtained from the pretrained BLIP models on the respective evaluation subsets. Your results may vary slightly depending on hardware and library versions.

6.1 Image Captioning (Flickr8k, 100 images)

Example Captions:

6.2 Visual Question Answering (VQAv2, 500 questions)

Example VQA Pairs:

6.3 Performance Summary

GPU timings measured on NVIDIA RTX 3060; CPU timings on Intel Core i7-11th gen.

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7\. Project Structure

Vision\_Language\_Model\_ELC/
│
├── app.py              # Streamlit web application (main entry point)
├── caption.py          # Image Captioning module (model + inference)
├── vqa.py              # VQA module (model + inference)
├── evaluation.py       # Offline evaluation (BLEU, ROUGE, VQA Accuracy)
├── requirements.txt    # Python dependencies
├── README.md           # This file
│
├── datasets/
│   ├── flickr8k/       # Flickr8k images + captions (user downloads)
│   └── vqav2/          # VQAv2 val2014 images + JSON (user downloads)
│
├── sample\_images/      # Demo images for quick testing
│
└── results/
    ├── captioning\_results.json   # Generated by evaluation.py
    └── vqa\_results.json          # Generated by evaluation.py

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8\. Installation \& Usage

Prerequisites

• —Python 3.9 – 3.12
• —pip
• —(Optional) CUDA-compatible GPU for faster inference

Step 1 — Clone / Extract

unzip Vision\_Language\_Model\_ELC\_Submission.zip
cd Vision\_Language\_Model\_ELC

Step 2 — Install Dependencies

pip install -r requirements.txt

For GPU (CUDA 12.x):

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

Step 3 — Download NLTK Data (for BLEU evaluation)

import nltk
nltk.download('punkt')

Step 4 — Run the Application

streamlit run app.py

Open your browser at http://localhost:8501.

Step 5 — Run Offline Evaluation (Optional)

First set up datasets (see Section 4), then:

# Evaluate both tasks
python evaluation.py --task both --cap-samples 100 --vqa-samples 500

# Captioning only
python evaluation.py --task captioning --cap-samples 200

# VQA only
python evaluation.py --task vqa --vqa-samples 500

Results are saved to the results/ directory as JSON files.

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9\. Conclusion

This project successfully demonstrates a complete Vision-Language Model pipeline restricted to two well-defined tasks:

1. 1.Image Captioning using Salesforce/blip-image-captioning-base achieves competitive BLEU and ROUGE scores on Flickr8k without any fine-tuning, confirming the strong generalisation ability of BLIP's bootstrapped pre-training.
2. 2.Visual Question Answering using Salesforce/blip-vqa-base achieves approximately 63% soft accuracy on a VQAv2 subset, which is consistent with published zero-shot/fine-tuned BLIP results.

Key Observations:

• —BLIP models offer a favourable trade-off between model size (\~224–247 M params) and performance.
• —Inference is feasible on CPU (<0.5 s/sample) and fast on GPU (<0.1 s/sample).
• —The modular code design (caption.py, vqa.py, evaluation.py) allows each component to be tested and extended independently.
• —The Streamlit UI provides an intuitive, real-time demonstration suitable for academic presentation.

Limitations \& Future Work:

• —Captioning quality degrades for highly domain-specific images (medical, satellite imagery) not well-represented in pre-training data.
• —VQA accuracy on abstract or reasoning-heavy questions remains a challenge.
• —Future work could incorporate fine-tuning on domain-specific datasets or upgrading to BLIP-2 for enhanced performance.

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10\. References

1. 1.Li, J., Li, D., Xiong, C., \& Hoi, S. (2022). BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. ICML 2022. https://arxiv.org/abs/2201.12086
2. 2.Hodosh, M., Young, P., \& Hockenmaier, J. (2013). Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics. JAIR. (Flickr8k Dataset)
3. 3.Goyal, Y., et al. (2017). Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering. CVPR 2017. https://arxiv.org/abs/1612.00837
4. 4.Papineni, K., et al. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. ACL 2002.
5. 5.Lin, C. Y. (2004). ROUGE: A Package for Automatic Evaluation of Summaries. ACL Workshop.
6. 6.Wolf, T., et al. (2020). HuggingFace's Transformers: State-of-the-art NLP. EMNLP 2020. https://arxiv.org/abs/1910.03771

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Vision-Language Model for Image Understanding — ELC Academic Project