Florence 2 FT Lung Cancer Detection

🚀 医学视觉问答模型 - Florence-2_FT_Lung-Cancer-detection

基于microsoft/Florence-2-base-ft微调的肺癌检测模型，利用肺部图像精准识别肺癌类型，为医学诊断提供高效解决方案。

🚀 快速开始

安装依赖

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

环境配置

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

加载模型和处理器

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)

运行示例

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)

✨ 主要特性

• 微调模型：该模型是microsoft/Florence-2-base-ft的微调版本，专门针对肺癌检测任务进行优化。
• 视觉问答：支持视觉问答任务，可根据肺部图像回答相关问题。

📦 安装指南

运行以下命令安装所需依赖：

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

💻 使用示例

基础用法

# 安装依赖
! pip install -q "flash_attn==2.6.3" "timm==1.0.8" "einops==0.8.0" "transformers==4.44.0" 

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

# 加载模型和处理器
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)

# 定义问题和图像
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)

# 处理输入
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)

# 生成回答
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]

# 解析回答
parsed_answer = processor.post_process_generation(generated_text, task="<DocVQA>", image_size=(image.width, image.height))

# 打印回答
print(parsed_answer)

📚 详细文档

模型信息

评估指标

测试准确率：99.17%

开发者信息

• 开发者：Nirusanan
• 许可证：未提供
• 基础模型：microsoft/Florence-2-base-ft