microsoft/Florence-2-large tuned on Ejafa/ye-pop captioned with CogVLM2
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.
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 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.
Captioning
The captions were generated using THUDM/cogvlm2-llama3-chat-19B.
Usage
To use this model, you can load it directly from the Hugging Face Model Hub:
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.'}