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nirusanan/
Florence-2_FT_Lung-Cancer-detection

Name: Florence-2_FT_Lung-Cancer-detection
Availability: InStock

visual-question-answering

custom_code

microsoft/Florence-2-base-ft

finetune

endpoints_compatible

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Model Description

The Florence-2_FT_Lung-Cancer-detection model is a fine-tuned version of the microsoft/Florence-2-base-ft model, tailored specifically for the task of lung cancer detection using lung images.

Developed by: Nirusanan
License:
Finetuned from model: microsoft/Florence-2-base-ft

How to use

! pip install -q "flash_attn==2.6.3" "timm==1.0.8" "einops==0.8.0" "transformers==4.44.0"

python

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

python

model = AutoModelForCausalLM.from_pretrained("nirusanan/Florence-2_FT_Lung-Cancer-detection", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("nirusanan/Florence-2_FT_Lung-Cancer-detection", trust_remote_code=True)

python

prompt = "<DocVQA>" + "What is the type of lung cancer?"

url = "https://www.uab.edu/news/images/ct_scan.jpg"
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="<DocVQA>", image_size=(image.width, image.height))

print(parsed_answer)

Evaluation

Test Accuracy: 99.17%