Rico Vit Gpt2 Finetuned

rico-vit-gpt2-finetuned

Model Details

Model Description

rico-vit-gpt2-finetuned is a multimodal vision-language model fine-tuned on the RICO Screen2Words dataset to generate natural language captions for mobile app UI screenshots.

Given a screenshot of an Android app screen — such as a gallery page, login form, social profile, or settings menu — the model generates a short human-readable description of the interface.

The model is built by fine-tuning a VisionEncoderDecoder architecture combining a Vision Transformer (ViT) encoder and a GPT-2 decoder.

The project demonstrates multimodal fine-tuning using a Small Language Model (SLM) that runs efficiently on T4 GPU compute.

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Model Information

• Developed by: Priyanka S, Padarthi Neha Sai
• Institution: PES University
• Model type: Vision-Language Model
• Architecture: VisionEncoderDecoder (ViT + GPT-2)
• Base model: nlpconnect/vit-gpt2-image-captioning
• Task: Image Captioning
• Language: English
• Framework: Hugging Face Transformers

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Model Sources

• Dataset: https://huggingface.co/datasets/rootsautomation/RICO-Screen2Words
• Base Model: https://huggingface.co/nlpconnect/vit-gpt2-image-captioning

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Uses

Direct Use

The model generates textual descriptions of mobile UI screenshots.

Example input:

• Screenshot of a mobile application screen

Example output:

display of screen shows images on gallery page of app

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Downstream Use

Potential applications include:

• Accessibility tools that describe app screens for visually impaired users
• UI/UX testing pipelines that generate readable descriptions of app states
• Semantic search over mobile UI screenshot datasets
• Automated annotation for UI research datasets

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Out-of-Scope Use

This model should not be used for safety-critical systems or decision-making systems.

Limitations include:

• captions may be overly generic
• small UI elements may be omitted
• similar UI layouts may be confused

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Bias, Risks, and Limitations

The model inherits limitations from both:

• the base ViT-GPT2 architecture
• the RICO Screen2Words dataset

Potential issues include:

• captions missing small UI details
• generic predictions for visually similar screens
• difficulty distinguishing context-dependent screens

Because the model was trained on only 500 samples, it may struggle with rare UI layouts.

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Recommendations

Users should treat generated captions as approximate descriptions rather than exact representations of the interface.

Improving performance may require:

• larger training datasets
• longer training schedules
• larger vision-language models

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How to Get Started with the Model

Example usage:

from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
from PIL import Image
import torch

model_name = "pes1ug23am219/rico-vit-gpt2-finetuned"

model = VisionEncoderDecoderModel.from_pretrained(model_name)
feature_extractor = ViTImageProcessor.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

image = Image.open("example_ui.png").convert("RGB")

pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values

output = model.generate(pixel_values, max_length=32)

caption = tokenizer.decode(output[0], skip_special_tokens=True)

print(caption)

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Training Details

Training Data

The model was fine-tuned using the RICO Screen2Words dataset.

RICO (Rico Is a Collection of UIs) is a large-scale dataset of Android UI screenshots paired with natural language descriptions.

Dataset properties:

Property	Value
Total images	~22,000
Captions per image	5
Caption style	Short UI descriptions
Subset used	500 samples

Dataset split used for this project:

Split	Samples
Training	400
Validation	100

The subset was used to ensure training runs within T4 GPU compute limits.

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Training Procedure

Preprocessing

Each example contains:

• Image: processed using ViTImageProcessor
• Caption: tokenized using GPT-2 tokenizer

Images are resized to 224×224 and normalized using ImageNet statistics.

Captions are padded or truncated to a maximum length of 32 tokens.

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Training Hyperparameters

Parameter	Value
Epochs	5
Batch size	4
Learning rate	3e-5
Warmup steps	20
Max caption length	32
Precision	FP16 mixed precision

Training was performed using the Hugging Face Trainer API on a Tesla T4 GPU.

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Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluation was conducted on a held-out validation set of 100 UI screenshots from the RICO Screen2Words dataset.

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Metrics

The model was evaluated using BLEU-4 (Bilingual Evaluation Understudy).

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

This metric is widely used for:

• machine translation
• image captioning
• text generation tasks

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Results

Metric	Score
BLEU-4	0.1722

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Summary

The model achieved a BLEU score of 0.1722 on the validation set.

Given the small training subset (500 samples), the model demonstrates a reasonable ability to produce fluent captions in the style of Screen2Words descriptions.

Performance could be improved by training on the full dataset.

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Model Architecture and Objective

The model uses the VisionEncoderDecoder architecture:

Component	Role
Vision Transformer (ViT)	Extracts visual features from screenshots
GPT-2 Decoder	Generates natural language captions

The encoder processes the image and produces embeddings that the decoder uses to generate captions autoregressively.

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Compute Infrastructure

Hardware

• GPU: NVIDIA Tesla T4 (16GB VRAM)

Software

• Python
• PyTorch
• Hugging Face Transformers
• Hugging Face Datasets

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Environmental Impact

Training was performed on a single T4 GPU for approximately 10 minutes.

Given the short training duration and small dataset size, the environmental impact is minimal.

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Model Card Authors

Priyanka S Padarthi Neha Sai PES University

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