bitpoint/ImageAnalysis

# Model Card for vit-gpt2-image-captioning

## Model Details This model is a VisionEncoderDecoderModel using a ViT encoder and GPT-2 decoder to generate captions for images. It was fine-tuned by adding context information to assist in generating meaningful captions.

• —Base Model: nlpconnect/vit-gpt2-image-captioning
• —Processor: ViTImageProcessor
• —Tokenizer: GPT-2 Tokenizer
• —Generated Caption Example: "{generated_text}"

## Intended Use This model is intended for generating captions for stock-related images, with an initial context provided for more accurate descriptions.

## Limitations

• —The model might generate incorrect or biased descriptions depending on the input image or context.
• —It requires specific context inputs for the best performance.

## How to Use

    from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
    model = VisionEncoderDecoderModel.from_pretrained("your_username/your_model_name")
    processor = ViTImageProcessor.from_pretrained("your_username/your_model_name")
    tokenizer = AutoTokenizer.from_pretrained("your_username/your_model_name")

## License This model is licensed under the same terms as the original nlpconnect/vit-gpt2-image-captioning.