Llava V1.5 7B Plant Leaf Diseases Detection

🚀 植物叶片病害细粒度视觉分类

本项目基于多模态基础模型，针对植物叶片病害检测与解释进行了优化，能够精准识别病害并给出可验证的症状描述，为植物健康监测提供有力支持。

🚀 快速开始

本模型是基于 [LLaVA - 1.5 - 7B - hf](https://huggingface.co/llava - hf/llava - 1.5 - 7b - hf) 微调的多模态基础模型，使用植物病害数据集进行优化，用于检测和解释植物叶片病害。

项目页面：SelfSynthX。

arXiv 论文：Enhancing Cognition and Explainability of Multimodal Foundation Models with Self - Synthesized Data

✨ 主要特性

💻 使用示例

基础用法

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "YuchengShi/LLaVA-v1.5-7B-Plant-Leaf-Diseases-Detection"
model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True,
).to("cuda")
processor = AutoProcessor.from_pretrained(model_id)

conversation = [
    {
      "role": "user",
      "content": [
          {"type": "text", "text": "What disease does this leaf have?"},
          {"type": "image"},
        ],
    },
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "plant-disease/test1.png"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to("cuda", torch.float16)

output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))

🔧 技术细节

• 训练：使用 LoRA 在 PlantVillage 上进行微调，并采用迭代拒绝采样。
• 评估：与基线模型相比，该模型表现出更高的准确性，并且能够提供可靠且可解释的解释。

📄 许可证

文档中未提及许可证相关信息。

📚 引用

如果您使用此模型，请引用：

@inproceedings{
  shi2025enhancing,
  title={Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data},
  author={Yucheng Shi and Quanzheng Li and Jin Sun and Xiang Li and Ninghao Liu},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025},
  url={https://openreview.net/forum?id=lHbLpwbEyt}
}