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---
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license: mit
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base_model:
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- microsoft/Florence-2-large
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datasets:
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- Ejafa/ye-pop
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tags:
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- art
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pipeline_tag: image-to-text
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language:
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- en
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---
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# microsoft/Florence-2-large tuned on Ejafa/ye-pop captioned with CogVLM2
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This repository contains a fine-tuned version of the `microsoft/Florence-2-large` model. The model has been tuned on a 38,000 image subset of the `Ejafa/ye-pop` dataset, with captions generated using `THUDM/cogvlm2-llama3-chat-19B`.
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## Training Details
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- **Vision Encoder**: The vision encoder was frozen during training.
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- **Batch Size**: 32
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- **Gradient Accumulation Steps**: 8
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- **Learning Rate**: 4.2667e-5
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- **Optimizer**: AdamW
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- **Scheduler**: linear
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- **Epochs**: 7
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## Dataset
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The fine-tuning process utilized a 38,000 image subset from the `Ejafa/ye-pop` dataset. This dataset contains a wide array of images with varying subjects, providing a robust training ground for improving the model's captioning abilities.
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## Captioning
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The captions were generated using `THUDM/cogvlm2-llama3-chat-19B`.
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## Usage
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To use this model, you can load it directly from the Hugging Face Model Hub:
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```python
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from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = AutoModelForCausalLM.from_pretrained("thwri/CogFlorence-2-Large-Freeze", trust_remote_code=True).to(device).eval()
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processor = AutoProcessor.from_pretrained("thwri/CogFlorence-2-Large-Freeze", trust_remote_code=True)
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# Function to run the model on an example
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def run_example(task_prompt, image):
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prompt = task_prompt
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# Ensure the image is in RGB mode
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if image.mode != "RGB":
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image = image.convert("RGB")
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inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
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generated_ids = model.generate(
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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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num_beams=3,
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do_sample=True
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
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return parsed_answer
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from PIL import Image
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import requests
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import copy
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
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image = Image.open(requests.get(url, stream=True).raw)
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result = run_example("<MORE_DETAILED_CAPTION>" , image)
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print(result)
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# {'<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.'}
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``` |
