GeoGround

Inference

1. Install LLaVA-1.5 from https://github.com/haotian-liu/LLaVA

2-1. Inference Coarse Masks

MODEL_PATH=path/to/checkpoints/llava-v1.5-7b-task-lora-geoground
OUTPUT=data/exp_0125
ANSWER_PATH=$OUTPUT/llava-v1.5-7b-task-lora-geoground
GPU_NUM=0


echo "Processing RRSIS-D test"
IMAGE_FOLDER=path/to/data/images/rrsisd/
JSON_PATH=path/to/data/metadata/rrsisd_val.jsonl

CUDA_VISIBLE_DEVICES=$GPU_NUM \
python inference_hbb.py \
    --model-path $MODEL_PATH \
    --model-base $MODEL_PATH \
    --question-file $JSON_PATH \
    --image-folder $IMAGE_FOLDER \
    --answers-file $ANSWER_PATH-rrsisd_val.jsonl \
    --batch_size 1

2-2. Inference Horizontal Bounding Boxes (HBBs)

CUDA_VISIBLE_DEVICES=$GPU_NUM \
python inference_seg.py \
    --model-path $MODEL_PATH \
    --model-base $MODEL_PATH \
    --question-file $JSON_PATH \
    --image-folder $IMAGE_FOLDER \
    --answers-file $ANSWER_PATH-rrsisd_val.jsonl \
    --batch_size 1

3-1. Generate Masks using Coarse Masks

python generate_mask.py \
    --answers-file $ANSWER_PATH-rrsisd_val.jsonl \
    --image-folder $IMAGE_FOLDER  \
    --scale 16 \
    --vis-dir $OUTPUT/vis_seg/

3-2. Generate Masks by SAM using HBBs

Download ViT-H SAM model from https://github.com/facebookresearch/segment-anything

python generate_mask_sam_by_box.py \
    --answers-file $ANSWER_PATH-rrsisd_val.jsonl \
    --image-folder $IMAGE_FOLDER  \
    --scale 16 \
    --vis-dir $OUTPUT/vis_sam_box/

3-3. Generate Masks by SAM using HBBs and Coarse Masks

python generate_mask_sam_by_box+seg.py \
    --answers-file $ANSWER_PATH-rrsisd_val.jsonl \
    --image-folder $IMAGE_FOLDER  \
    --scale 16 \
    --vis-dir $OUTPUT/vis_sam_box+seg/

1. Compute Metric

python compute_mask_metric.py