I have a instruct tune gemma3 4b model on VQA data...

Why segmentation + reasoning helps

Mis-classified leaves are almost always those in which the model attends to the wrong region (background, healthy tissue, glare, etc.). Giving the model an auxiliary objective that forces it to isolate the symptomatic pixels tightens its attention and makes the class token more discriminative. Recent “reasoning-segmentation” papers show that even small multimodal LLMs gain 2–7 pp on the main task once they are taught to emit a mask token before (or together with) the textual answer — see LISA’s embedding-as-mask trick (CVF Open Access) and the SAM-plus-LLM pipeline in LLM-Seg (arXiv). For agriculture specifically, the SCOLD foundation model reports similar gains when it injects localisation cues during pre-training (arXiv).

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Two practical ways to add segmentation to your Gemma-3 4 B VQA model

Strategy	What you change	Pros	Cons
A. “One-stage” SFT with a <SEG> token (LISA style)	• Add a single extra token to Gemma’s text vocab.
• At train-time, ground-truth mask M is down-sampled to the vision-encoder patch grid (e.g. 14×14), flattened to a bit-vector b, and projected with a small MLP to the same dim as the language embeddings.
• Training sample looks like:

text
<image>  Q: What disease?  
A: <SEG> <cls_token>

Loss = CE on mask bits + CE on class id. | • End-to-end; no extra encoder.
• Mask guides attention early in the decoding stack, so classification improves even if you discard the mask at test time. | • Requires ≥100 – 200 pixel-annotated images per disease to avoid over-fitting.
• Longer answers (mask bits) make generative latency higher unless you truncate. | | B. Two-stage mask-selection (LLM-Seg style) | • Keep Gemma intact but freeze it.
• Run SAM (or similar) to generate K≈100 candidate masks.
• Feed Gemma the prompt:

<image+{mask_i}> "Which of the K regions shows symptoms of late blight?"  

• Gemma chooses the index; the chosen mask is then passed to a tiny CNN head that predicts the class.
• Fine-tune Gemma with a small LoRA on the vision-language cross-attention blocks + a linear head on top of pooled vision features. | • No change to tokenizer.
• Works with synthetic masks (SAM) + minimal GT (only pick-the-best-mask supervision).
• Inference speed is good: SAM runs once; Gemma reasons over K embeddings (not pixels). | • Pipeline, not end-to-end.
• Need to keep SAM weights in memory. |

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Implementation recipe for Strategy A (single-stage SFT)

1. Data
   Label acquisition

   • Hand-annotate ≈500 images covering the long tail of diseases or auto-label with SAM then manually clean 10 %.
   • Down-sample to the patch grid size of Gemma’s ViT (e.g. 14×14 for 224 px inputs).
   • Serialize the mask as a 196-bit string (“0100…1”).
     Combine with your existing VQA pairs so every image has both a question-answer and a mask label.

2. Model surgery

python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("google/gemma-3b-vqa")
tok   = AutoTokenizer.from_pretrained("google/gemma-3b-vqa")
tok.add_tokens(["<SEG>"])               # 1 new id
model.resize_token_embeddings(len(tok))
# small MLP that turns the hidden state of <SEG> into a mask
model.seg_head = torch.nn.Sequential(
        torch.nn.Linear(model.config.hidden_size, 196),
        torch.nn.Sigmoid())

3. Losses

python
loss_cls = cross_entropy(logits_cls, label_cls)
loss_seg = dice_loss(model.seg_head(h_seg), gt_mask)
loss = loss_cls + 0.5 * loss_seg            # weight can be tuned

4. Fine-tuning strategy

   • Freeze vision encoder for the first 1 – 2 epochs.
   • Use LoRA (rank 32) on the last 8 transformer layers and the new seg_head; lr = 2e-4; batch = 32; 8-bit AdamW.
   • Train 3-5 epochs — the auxiliary mask stabilises quickly.

5. Inference
   Omit the mask: prompt Gemma just with the image and “What disease?”; the learned attention still focuses on the diseased pixels, lifting top-1 accuracy.

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Can you use SFT?

Yes. Both approaches above are standard supervised fine-tuning:

• Single-stage: joint loss (classification + mask) on the same forward pass.
• Two-stage: SFT Gemma to select the correct SAM mask, then optionally SFT a light classifier on the masked crop.

If you want to squeeze more out of the model after SFT:

• Self-distillation – iterate: use the current Gemma to pseudo-label masks on unlabeled images, keep only high-confidence ones (>0.8 IoU with SAM proposal), and re-train.
• RL-HF – reward ≈ (class-accuracy + IoU) and fine-tune with PPO; this often refines the decision boundary when the mask is ambiguous.
• Prompt-engineering – at test time prepend a chain-of-thought hint, e.g. “First, locate the lesion area, then answer with the disease name.” Tiny but free gains (0.5 – 1 pp).

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Checklist before you start

Item	Done?
250–500 pixel-level masks per class (or reliable SAM pseudo-labels)	☐
20 % of data kept for IoU + accuracy evaluation	☐
Updated tokenizer + checkpoint with <SEG>	☐
Mixed-precision & gradient-accum for 4 B params	☐
Plan for serving: mask-less prompt or two-stage pipeline	☐

Once these are in place, SFT should give you +3 – 5 pp class accuracy on PlantVillage-style test sets, and you’ll get the bonus ability to visualise exactly why the model predicted a class — a nice trust-building feature when deploying to growers.