scripts/train_sam3_buscot_lora.sh

#!/usr/bin/env bash
# Optional portable retraining entry point. Inference does not require this.
set -euo pipefail

DATASET_CSV="${1:?Usage: $0 DATASET_CSV OUTPUT_DIR [GPU]}"
OUTPUT_DIR="${2:?Usage: $0 DATASET_CSV OUTPUT_DIR [GPU]}"
GPU="${3:-0}"

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
BUNDLE="$(cd "${SCRIPT_DIR}/.." && pwd)"
PYTHON="${PYTHON:-python}"
SAM3_CKPT="${SAM3_CHECKPOINT:-${BUNDLE}/model/sam3_base.pt}"

CUDA_VISIBLE_DEVICES="${GPU}" PYTHONNOUSERSITE=1 "${PYTHON}" -u \
  "${SCRIPT_DIR}/train_sam3_decoder_segmentation.py" \
  --dataset_csv "${DATASET_CSV}" \
  --protocol all_public_indomain \
  --output_dir "${OUTPUT_DIR}" \
  --sam3_checkpoint "${SAM3_CKPT}" \
  --decoder sam3_native \
  --prompt_type semantic_text \
  --prompt_text "${PROMPT_TEXT:-breast lesion}" \
  --encoder_trainable lora \
  --lora_rank 8 \
  --lora_alpha 16 \
  --batch_size "${BATCH_SIZE:-8}" \
  --epochs "${EPOCHS:-50}" \
  --patience "${PATIENCE:-10}" \
  --lr 1e-4 \
  --encoder_lr 1e-5 \
  --use_amp \
  --resume