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README.md

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-license: apache-2.0
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+license: apache-2.0
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+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:
+
+bash
+Copy code
+pip install torch torchvision transformers
+Code Example
+python
+Copy code
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, GPT2Tokenizer
+import torch
+from PIL import Image
+
+# Load the fine-tuned model and tokenizer
+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
+image = Image.open("path_to_image.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+# Generate caption
+pixel_values = inputs.pixel_values
+output = model.generate(pixel_values)
+caption = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print("Generated Caption:", caption)
+Inputs
+Image Input: The input should be an image file. Supported formats include .jpg, .png, etc.
+Output: A text string representing the generated caption for the image.
+Example
+For an input image, the model might generate a caption like:
+
+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.