README.md

---
license: apache-2.0

widget:
  - type: image-to-text
    example:
      - src: "tiger.jpg"
      - prompt: "Describe this image in one sentence."

language:
- en
metrics:
- accuracy
base_model:
- nlpconnect/vit-gpt2-image-captioning
tags:
- gpt2
- image_to_text
- COCO
- image-captioning

pipeline_tag: image-to-text
---



# vit-gpt2-image-captioning_COCO_FineTuned
This repository contains the fine-tuned ViT-GPT2 model for image captioning, trained on the COCO dataset. The model combines a Vision Transformer (ViT) for image feature extraction and GPT-2 for text generation to create descriptive captions from images.

# Model Overview
Model Type: Vision Transformer (ViT) + GPT-2
Dataset: COCO (Common Objects in Context)
Task: Image Captioning
This model generates captions for input images based on the objects and contexts identified within the images. It has been fine-tuned on the COCO dataset, which includes a wide variety of images with detailed annotations, making it suitable for diverse image captioning tasks.

# Model Details
The model architecture consists of two main components:

Vision Transformer (ViT): A powerful image encoder that extracts feature maps from input images.
GPT-2: A language model that generates human-like text, fine-tuned to generate captions based on the extracted image features.
The model has been trained to:

Recognize objects and scenes from images.
Generate grammatically correct and contextually accurate captions.
Usage
You can use this model for image captioning tasks with the Hugging Face transformers library. Below is a sample code to load the model and generate captions for input images.

# Installation

To use this model, you need to install the following libraries:
```python
pip install torch torchvision transformers
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, GPT2Tokenizer
import torch
from PIL import Image
```
# Load the fine-tuned model and tokenizer
```python
model = VisionEncoderDecoderModel.from_pretrained("ashok2216/vit-gpt2-image-captioning_COCO_FineTuned")
processor = ViTImageProcessor.from_pretrained("ashok2216/vit-gpt2-image-captioning_COCO_FineTuned")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
```
# Preprocess the image
```python
image = Image.open("path_to_image.jpg")
inputs = processor(images=image, return_tensors="pt")
```
# Generate caption
```python
pixel_values = inputs.pixel_values
output = model.generate(pixel_values)
caption = tokenizer.decode(output[0], skip_special_tokens=True)

print("Generated Caption:", caption)
```
# Input Image:

Generated Caption:
"A group of people walking down the street with umbrellas in their hands."

# Fine-Tuning Details
Dataset: COCO dataset (common objects in context)
Image Size: 224x224 pixels
Training Time: ~12 hours on a GPU (depending on batch size and hardware)
Fine-Tuning Strategy: We fine-tuned the ViT-GPT2 model for 5 epochs using the COCO training split.
Model Performance
This model performs well on various image captioning benchmarks. However, its performance is highly dependent on the diversity and quality of the input image. It is recommended to fine-tune or retrain the model further for more specific domains if necessary.

# Limitations
The model might struggle with generating accurate captions for highly ambiguous or abstract images.
It is trained primarily on the COCO dataset and might perform better on images with similar contexts to the training data.
License
This model is licensed under the MIT License.

# Acknowledgments
COCO Dataset: The model was trained on the COCO dataset, which is widely used for image captioning tasks.
Hugging Face: For providing the platform to share models and facilitate easy usage of transformer-based models.
Contact
For any questions, please contact Ashok Kumar.