arXiv, 2024

Paper citation: Deitke, Matt, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Mohammadreza Salehi et al. “Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models.” arXiv preprint arXiv:2409.17146 (2024).

Two Sentence Summary

• The paper introduces Molmo.
• It is a family of open-weight vision-language models that excel in performance without using synthetic data from proprietary systems.

Dataset

• Models are trained with a new dataset called PixMo.
• PixMo contains detailed image captions created through a unique speech-based description method.
• This method allows annotators to provide comprehensive image descriptions.

Performance

• Molmo models achieve state-of-the-art results.
• They perform exceptionally well on academic benchmarks and user preference evaluations.
• They outperform proprietary models like GPT-4o and Claude 3.5.

Approach

Implementation Components

Data Collection:

• Annotators described images verbally in 60–90 seconds.
• Dense image captions were produced, then transcribed for processing.

Model Training:

• It involved two major stages:
• Multimodal pre-training for generating captions.
• Supervised fine-tuning using a diverse dataset mixture.

Python Code for Molmo Algorithm

Step 1: Preparing the inputs

import pandas as pd
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("model-name")  # replace with actual model name
model = AutoModel.from_pretrained("model-name")  # replace with actual model name
image_data = pd.read_csv('path_to_image_data.csv')  # Load image data from CSV
image_descriptions = []

Step 2: Generate captions for images

for index, row in image_data.iterrows():
    image = row['image_path']  # Extract image path from dataset
    caption = model.generate_captions(image)  # Replace with actual call to generate captions
    image_descriptions.append(caption)

Step 3: Fine-tuning the model on the annotated data

import torch
fine_tune_data = pd.read_csv('path_to_fine_tuning_data.csv')  # Load fine-tuning data
inputs = tokenizer(fine_tune_data['text'], return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
model.save_pretrained('path_to_save_model_weights/')  # Save updated model weights

In the code snippets above:

• The first block prepares the data and inputs required for the model.
• The second part generates captions for the images using the model.
• Finally, the model is fine-tuned on a specific dataset and saved.

Summary of the Evaluation

• The Molmo models were evaluated across 11 distinct academic benchmarks.
• They showed clear advantages in benchmark performance and human evaluations.
• The Molmo-72B model scored highest in benchmark evaluations.
• It achieved second place in human preference evaluations, showing agreement between automated and human metrics.

Important Findings from the Evaluation Results

• Despite using only high-quality datasets, Molmo models performed closely to proprietary systems like GPT-4o.
• This highlights the potential for open models to compete with proprietary solutions in AI.

Analysis: Pros

• Open-Weight Models: Encourage transparency and accessibility for researchers.
• Innovative Data Collection Approach: Speech-based image descriptions enhance dataset richness.
• Strong Performance: Out-competes several proprietary competitors with effective open training methodologies.

Analysis: Cons

• Reliance on High-Quality Input Data: Performance depends on the input data quality, which is crucial.
• Potential for Overfitting: High specificity may lead to poor generalization on unseen images.
• Computational Resource Usage: Training requires significant resources, limiting access for smaller entities.

The article is also available on Medium.