Image-Captioning-Llama-3.2-1B

AnyModal/Image-Captioning-Llama-3.2-1B

AnyModal/Image-Captioning-Llama-3.2-1B is an image captioning model built within the AnyModal framework. It integrates a Vision Transformer (ViT) encoder with the Llama 3.2-1B language model and has been trained on the Flickr30k dataset. The model demonstrates the integration of pre-trained vision and language components for generating descriptive captions from natural images.

---

Trained On

This model was trained on the Flickr30k Dataset:

From Image Descriptions to Visual Denotations: New Similarity Metrics for Semantic Inference Over Event Descriptions
Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, Svetlana Lazebnik

The dataset contains 31,000 images collected from Flickr, each annotated with five descriptive sentences written by human annotators, covering a variety of real-world scenes and events.

---

How to Use

Installation

Install the necessary dependencies:

pip install torch transformers torchvision huggingface_hub tqdm matplotlib Pillow

Inference

Below is an example of generating captions for an image using this model:

import llm
import anymodal
import torch
import vision
from torch.utils.data import DataLoader
import numpy as np
import os
from PIL import Image
from huggingface_hub import hf_hub_download

# Load language model and tokenizer
llm_tokenizer, llm_model = llm.get_llm(
    "meta-llama/Llama-3.2-1B",
    access_token="GET_YOUR_OWN_TOKEN_FROM_HUGGINGFACE",
    use_peft=False,
)
llm_hidden_size = llm.get_hidden_size(llm_tokenizer, llm_model)

# Load vision model components
image_processor, vision_model, vision_hidden_size = vision.get_image_encoder("google/vit-base-patch16-224", use_peft=False)

# Initialize vision tokenizer and encoder
vision_encoder = vision.VisionEncoder(vision_model)
vision_tokenizer = vision.Projector(vision_hidden_size, llm_hidden_size, num_hidden=1)

# Initialize MultiModalModel
multimodal_model = anymodal.MultiModalModel(
    input_processor=None,
    input_encoder=vision_encoder,
    input_tokenizer=vision_tokenizer,
    language_tokenizer=llm_tokenizer,
    language_model=llm_model,
    input_start_token="<|imstart|>",
    input_end_token="<|imend|>",
    prompt_text="The description of the given image is: ",
)

# Download pre-trained model weights
if not os.path.exists("image_captioning_model"):
    os.makedirs("image_captioning_model")

hf_hub_download("AnyModal/Image-Captioning-Llama-3.2-1B", filename="input_tokenizer.pt", local_dir="image_captioning_model")
multimodal_model._load_model("image_captioning_model")

# Generate caption for an image
image_path = "example_image.jpg"  # Path to your image
image = Image.open(image_path).convert("RGB")
processed_image = image_processor(image, return_tensors="pt")
processed_image = {key: val.squeeze(0) for key, val in processed_image.items()}  # Remove batch dimension

# Generate caption
generated_caption = multimodal_model.generate(processed_image, max_new_tokens=120)
print("Generated Caption:", generated_caption)

---

Project and Training Scripts

This model is part of the AnyModal Image Captioning Project.

• Training Script: train.py
• Inference Script: inference.py

Refer to the project repository for further implementation details and customization.

---

Project Details

• Vision Encoder: Pre-trained Vision Transformer (ViT) model for visual feature extraction.
• Projector Network: Projects visual features into a token space compatible with Llama 3.2-1B using a dense network.
• Language Model: Llama 3.2-1B, a pre-trained causal language model for text generation.

This implementation serves as a proof of concept, combining a ViT-based image encoder and a small language model. Future iterations could achieve improved performance by incorporating text-conditioned image encoders and larger-scale language models.