Cogflorence 2 Large Freeze

🚀 microsoft/Florence-2-large tuned on Ejafa/ye-pop captioned with CogVLM2

This repository holds a fine - tuned version of the microsoft/Florence-2-large model. It has been tuned on a 38,000 - image subset of the Ejafa/ye-pop dataset. The captions for this tuning were generated using THUDM/cogvlm2-llama3-chat-19B, enhancing the model's image - to - text capabilities.

✨ Features

• The model is a fine - tuned variant of microsoft/Florence-2-large, optimized for image captioning.
• It has been trained on a diverse image dataset, Ejafa/ye-pop, to improve its generalization ability.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained("thwri/CogFlorence-2-Large-Freeze", trust_remote_code=True).to(device).eval()
processor = AutoProcessor.from_pretrained("thwri/CogFlorence-2-Large-Freeze", trust_remote_code=True)

# Function to run the model on an example
def run_example(task_prompt, image):
    prompt = task_prompt

    # Ensure the image is in RGB mode
    if image.mode != "RGB":
        image = image.convert("RGB")

    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        num_beams=3,
        do_sample=True
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
    return parsed_answer

from PIL import Image
import requests
import copy

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)
result = run_example("<MORE_DETAILED_CAPTION>" , image)
print(result)

# {'<MORE_DETAILED_CAPTION>': 'a turquoise volkswagen beetle parked on a cobblestone street in front of a yellow wall with two wooden doors. the car's body is painted in a vibrant shade of teal, with a glossy finish that reflects the sunlight, and the wheels are polished with a silver hubcap. the building behind the car has a weathered, aged appearance, with visible cracks and peeling paint. the sky above is clear and blue, suggesting a sunny day.'}

📚 Documentation

Training Details

• Vision Encoder: The vision encoder was frozen during training.
• Batch Size: 32
• Gradient Accumulation Steps: 8
• Learning Rate: 4.2667e - 5
• Optimizer: AdamW
• Scheduler: linear
• Epochs: 7

Dataset

The fine - tuning process used a 38,000 - image subset from the Ejafa/ye-pop dataset. This dataset contains a wide variety of images with different subjects, offering a solid training foundation for enhancing the model's captioning capabilities.

Captioning

The captions were generated using THUDM/cogvlm2-llama3-chat-19B.

📄 License

This project is licensed under the MIT license.