库名称：transformers
标签：

• 医疗
  语言：
• 英语
  任务类型：视觉问答
  基础模型：microsoft/Florence-2-base-ft
  基础模型关系：微调

模型描述

Florence-2_FT_Lung-Cancer-detection 模型是基于 microsoft/Florence-2-base-ft 模型微调而成的版本，专门用于通过肺部图像检测肺癌的任务。

• 开发者： Nirusanan
• 许可证：
• 微调自模型： microsoft/Florence-2-base-ft

使用方法

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

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("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)  

prompt = "<DocVQA>" + "这是什么类型的肺癌？"  

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)  

评估

测试准确率：99.17%