Are you fascinated by the convergence of computer vision and natural language processing? Enter Florence-2, an advanced vision foundation model by Microsoft that takes a prompt-based approach to tackle diverse tasks in vision and vision-language realms. In this blog, we will explore the model, how to get started, and troubleshoot common issues!
Model Summary
The Florence-2 model utilizes a comprehensive technical framework to interpret simple text prompts for various applications, including captioning, object detection, and segmentation. This model is built upon the impressive FLD-5B dataset, boasting 5.4 billion annotations spanning 126 million images. The sequence-to-sequence architecture illustrates Florence-2’s versatility in both zero-shot and fine-tuning settings, making it a robust player in the world of vision tasks.
How to Get Started with the Model
Ready to dive into Florence-2? Here’s a simple and user-friendly guide to get you started!
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large-ft", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
prompt = ""
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)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
generated_ids = model.generate(input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, do_sample=False, num_beams=3)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(generated_text, task="", image_size=(image.width, image.height))
print(parsed_answer) The above code is your magic wand! As a computer scientist applies a methodical approach to solving complex algebra problems step-by-step, this code enables you to work with visual data effectively. Think of the image and prompt as ingredients in a recipe. The code orchestrates the gathering of these ingredients, feeding them into a “chef” (the model) that skillfully crafts a delicious output (the parsed answer).
Tasks
The beauty of Florence-2 resides in its ability to flexibly perform various tasks merely by changing the prompts. Below are some demos categorized for easy execution:
- Captioning:
prompt = "" run_example(prompt) - Object Detection:
prompt = "" run_example(prompt) - Dense Region Caption:
prompt = "" run_example(prompt) - OCR:
prompt = "" run_example(prompt)
Troubleshooting
If you encounter any issues during the setup or execution, consider these troubleshooting ideas to get you through:
- Ensure that you have the latest version of the
transformerslibrary installed. - Check that your GPU is properly set up if you’re planning to run the model with CUDA.
- If you receive an error regarding model loading, confirm that the model name is correctly specified.
- If the outputs aren’t as expected, consider fine-tuning your prompts or try adjusting model settings.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Resources
Happy coding, and may Florence-2 illuminate your path in the exciting realm of vision tasks!
