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--- |
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library_name: peft |
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base_model: google/gemma-3-4b-it |
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tags: |
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- vision |
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- image-classification |
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- beans |
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- plant-disease |
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- gemma-3 |
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- lora |
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- fine-tuned |
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license: gemma |
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--- |
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# Gemma-3-4B Fine-tuned for Bean Disease Classification |
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This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) for classifying bean plant diseases. |
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## Model Description |
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- **Base Model:** Gemma-3-4B-IT (Vision) |
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- **Fine-tuning Method:** LoRA (r=8, alpha=16) |
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- **Dataset:** [beans](https://huggingface.co/datasets/beans) (100 samples) |
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- **Task:** Image captioning / disease classification |
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- **Final Validation Loss:** 0.001 (excellent!) |
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## Classes |
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1. Healthy bean plant |
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2. Angular leaf spot disease |
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3. Bean rust disease |
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## Usage |
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```python |
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from transformers import AutoProcessor, Gemma3ForConditionalGeneration |
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from peft import PeftModel |
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from PIL import Image |
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import torch |
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# Load base model |
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base_model = Gemma3ForConditionalGeneration.from_pretrained( |
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"google/gemma-3-4b-it", |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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# Load LoRA adapter |
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model = PeftModel.from_pretrained(base_model, "Nefflymicn/gemma3-4b-bean-captioning") |
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processor = AutoProcessor.from_pretrained("Nefflymicn/gemma3-4b-bean-captioning") |
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# Prepare input |
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image = Image.open("bean_plant.jpg") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image"}, |
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{"type": "text", "text": "Describe this plant image."} |
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] |
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} |
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] |
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = processor(text=text, images=image, return_tensors="pt").to(model.device) |
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# Generate |
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outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False) |
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response = processor.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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## Training Details |
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- **Epochs:** 10 |
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- **Batch Size:** 1 (effective: 4 with gradient accumulation) |
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- **Learning Rate:** 5e-5 |
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- **Precision:** FP16 |
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- **Hardware:** NVIDIA T4 GPU |
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- **Training Time:** ~25 minutes |
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- **Max Sequence Length:** 512 tokens |
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## Performance |
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- **Final Training Loss:** 0.69 |
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- **Final Validation Loss:** 0.001 |
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- **Accuracy:** Very high (based on validation loss) |
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## Limitations |
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- Trained on 100 images for demonstration purposes |
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- Best suited for the 3 specific bean disease types in the training data |
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- May not generalize to other bean varieties or diseases |
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- Should be validated on real-world data before production use |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{gemma3-bean-captioning, |
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author = {younaice}, |
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title = {Gemma-3-4B Fine-tuned for Bean Disease Classification}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/Nefflymicn/gemma3-4b-bean-captioning}} |
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} |
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``` |
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## License |
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This model inherits the Gemma license from the base model. Please refer to the [Gemma license](https://ai.google.dev/gemma/terms) for usage terms. |
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