ashok2216
/

vit-gpt2-image-captioning_COCO_FineTuned

@@ -14,16 +14,16 @@ tags:
 - image-captioning
 ---
-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.
@@ -35,7 +35,8 @@ 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
@@ -49,15 +50,18 @@ 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)
@@ -69,12 +73,12 @@ 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)
@@ -82,13 +86,13 @@ Fine-Tuning Strategy: We fine-tuned the ViT-GPT2 model for 5 epochs using the CO
 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

 - image-captioning
 ---
+# 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.
 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
 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)
 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)
 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