VisionGPT v3: Vision Language Model & ML Infrastructure Suite

VisionGPT v3 is a state-of-the-art production-grade Vision-Language Model (VLM) built in TensorFlow 2.x and Python 3.10+. It implements an end-to-end deep learning model alongside a robust set of infrastructure modules for dataset management, automated evaluation, hardware benchmarking, real-time training dashboards, and publication-ready architecture diagram generation.

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1. Project Architecture Overview

The VisionGPT model coordinates a visual encoder, a projections fusion layer, and an autoregressive text decoder to decode text tokens from fused visual features:

                  ┌───────────────┐
                  │  Input Image  │
                  └───────┬───────┘
                          │ (224x224x3 RGB)
                          ▼
            ┌───────────────────────────┐
            │  EfficientNet-B0 Encoder  │  <-- Frozen Feature Extractor
            └─────────────┬─────────────┘
                          │ (7x7x1280 Spatial Grid)
                          ▼
            ┌───────────────────────────┐
            │   Feature Fusion Layer    │  <-- Linear Dimension Projection
            └─────────────┬─────────────┘
                          │ (49x256 Token Sequence)
                          ▼
            ┌───────────────────────────┐
            │    Transformer Decoder    │  <-- Autoregressive Text Generator
            └─────────────┬─────────────┘
                          │ (Multi-Head Cross-Attention)
                          ▼
            ┌───────────────────────────┐
            │     Output Softmax        │  <-- Probability Vocabulary Classifier
            └─────────────┬─────────────┘
                          │
                          ▼
                  [Caption / Answer]

For a detailed analysis of our model layers and shapes, see the Architecture Document.

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2. Directory Structure

visiongpt/
├── evaluation/         # Metrics (BLEU, METEOR, ROUGE-L, CIDEr, Exact Match)
├── benchmark/          # Speed, latency, and memory profiling suite
├── monitoring/         # Real-time training Keras callbacks & experiment dashboards
├── visualization/      # Matplotlib architecture layout and flowcharts generators
├── models/             # Core layers (VisionEncoder, FusionLayer, AnswerDecoder)
├── dataset_tools/      # Dataset verification and reader utilities
├── tools/
│   └── dataset_manager/# Concurrent dataset downloader CLI
└── docs/               # Detailed documentation files

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3. Key Features

1. Vision-Language Task Core: Capable of Image Captioning, Visual Question Answering (VQA), and OCR-aware QA.
2. Dataset Manager CLI: Downloads, extracts, and checksum-verifies COCO 2017, TextVQA, GQA, and Visual Genome.
3. Automated Evaluation: Fully implementations for BLEU (1-4), ROUGE-L, METEOR, CIDEr, and VQA Accuracy (Exact Match, Token F1, Word Levenshtein).
4. Hardware & Model Benchmarking: Profiles throughput (images/sec), latency percentiles (P50, P90, P99), RAM peak RSS, GPU VRAM peak, and isolations for forward/backward/update loops.
5. Real-time Callback Dashboards: Standardized Keras training callback pushing scalars and weight histograms to TensorBoard, exporting CSV/JSON histories, and updating a responsive HTML dashboard.
6. Architecture Diagram Generator: Outputs publication-quality vertical layers flowcharts, horizontal pipelines, and parameter pie charts in PNG, PDF, and SVG formats across light, dark, and monochrome themes.

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4. Documentation Index

To get started, explore the detailed guides below:

• Installation Guide: Set up Python, TensorFlow, and GPU CUDA/cuDNN variables.
• Dataset Reference: Explore manager commands for COCO, TextVQA, GQA, and Visual Genome.
• Training Pipeline: Mixed precision, callbacks, checkpointing, and resume instructions.
• Inference execution: Interactive and batch CLI calls and Python API usage.
• Architecture Specifications: Sub-module parameters, activations, and tensor flows.
• API Reference: Public interfaces for Evaluator, BenchmarkSuite, and ExperimentLogger.
• Benchmark Guide: Running performance profiles on CPU/GPU hardware.
• Evaluation Module: Math details of BLEU, METEOR, ROUGE, and CIDEr.
• Troubleshooting FAQ: Out-of-memory errors, CUDA warnings, and dataset extraction failures.
• Contributing Guidelines: PEP8 style compliance, pull requests, and test runs.
• Changelog: Release histories and updates.
• Apache 2.0 License: Open-source terms.

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5. Quick Start Examples

A. Run Hardware and Speed Benchmarks

import models.visiongpt as vgpt
import benchmark.benchmark as bmark

# Instantiate and build model
model = vgpt.VisionGPT()
suite = bmark.BenchmarkSuite(output_dir="./reports")

# Run full speed and VRAM peak benchmarks
results = suite.benchmark_model_and_system(model, batch_sizes=[1, 2, 4])
suite.generate_report()

B. Evaluate Text Generation Metrics

import evaluation.evaluator as eval_mod

evaluator = eval_mod.Evaluator()
predictions = ["a brown cat sitting on a table", "a red bus on the street"]
references = ["a cat sitting on the table", "a bus on a street"]

scores = evaluator.evaluate(predictions, references)
print("BLEU-4:", scores["BLEU-4"])
print("CIDEr:", scores["CIDEr"])

C. Train with Live Dashboards & Callbacks

from monitoring.experiment import ExperimentLogger
from monitoring.callbacks import VisionGPTTrainingCallback

logger = ExperimentLogger(experiment_name="coco_efficientnet_dec", epochs=10)
callback = VisionGPTTrainingCallback(experiment_logger=logger)

model.fit(
    train_dataset,
    epochs=10,
    validation_data=val_dataset,
    callbacks=[callback]
)

D. Generate Publication Diagrams

from visualization.architecture import ArchitectureVisualizer

visualizer = ArchitectureVisualizer(model=model, output_dir=".")
visualizer.generate_architecture(theme="monochrome") # monochrome paper layout
visualizer.export_png("architecture.png")
visualizer.export_pdf("architecture.pdf") # vector format

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6. Citation

If you use VisionGPT v3 in your research, please cite:

@software{visiongpt_v3_2026,
  author = {VisionGPT Team},
  title = {VisionGPT v3: An Infrastructure and Deep Learning Suite for Vision-Language Models},
  year = {2026},
  url = {https://github.com/Amangp/visionGPT}
}

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7. License

Distributed under the Apache 2.0 License. See the LICENSE file for complete details.