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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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library_name: transformers |
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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 40,000 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**: 64 |
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- **Gradient Accumulation Steps**: 16 |
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- **Learning Rate**: 5.12e-05 |
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- **Optimizer**: AdamW |
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- **Scheduler**: polynomial |
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- **Epochs**: 8.36 |
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## Dataset |
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The fine-tuning process utilized a 40,000 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` and then post-processed with `google/gemma-2-9b` to remove vagueness. |
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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-2.2-Large", trust_remote_code=True).to(device).eval() |
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processor = AutoProcessor.from_pretrained("thwri/CogFlorence-2.2-Large", 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 vivid portrayal of a classic Volkswagen Beetle parked on a cobblestone street. The car is painted a vibrant turquoise, contrasting with the muted yellow of the building behind it. The building has two wooden doors, one with a white frame and the other with a dark brown finish. The sky is clear, and the sun casts a warm glow on the scene, highlighting the car's details. The image evokes a nostalgic and nostalgic mood, capturing a moment in time without posed elements.'} |
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``` |
