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Model: YuchengShi/LLaVA-v1.5-7B-Plant-Leaf-Diseases-Detection
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags: []
+pipeline_tag: image-text-to-text
+---
+
+# Fine-Grained Visual Classification on Plant Leaf Diseases  
+
+Project Page: [SelfSynthX](https://github.com/sycny/SelfSynthX).
+
+Paper on arXiv: [Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data](https://arxiv.org/abs/2502.14044)
+  
+
+This model is a fine-tuned multimodal foundation model based on [LLaVA-1.5-7B-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf), optimized for detecting and explaining plant leaf diseases using the Plant disease dataset.
+
+## Key Details  
+
+- **Base Model:** LLaVA-1.5-7B  
+- **Dataset:** Healthy and diseased leaves across multiple plant species
+- **Innovation:**  
+  - **Self-Synthesized Data:** Extracts and describes disease-specific visual symptoms using the Information Bottleneck principle.  
+  - **Iterative Fine-Tuning:** Uses reward model-free rejection sampling to improve classification accuracy and explanation quality.  
+- **Intended Use:** Identification of plant leaf diseases with human-verifiable symptom descriptions.
+
+## How to Use  
+
+```python
+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))
+```
+
+## Training & Evaluation  
+
+- **Training:** Fine-tuned using LoRA on PlantVillage with iterative rejection sampling.  
+- **Evaluation:** Demonstrates superior accuracy and robust, interpretable explanations compared to baseline models.
+
+## Citation  
+
+If you use this model, please cite:  
+
+```bibtex
+@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}
+}
+```