README.md

Image-text Alignment Evaluation - Retrieval

We provide CLIP-based R-precision evaluation.

Setup

Download 30K image-caption pairs.

# pip install gdown
gdown 1au9DI9tr-dcfGMFxFkFrJt0p_htojxH2

The uid_caption.csv file consists of 30K image-caption pairs. The images are sampled from COCO val2014 split. For each image, a caption out of 5 paired captions is randomly sampled. The file has two keys:

• uid: {COCO img id}_mscoco_{caption index}
• caption: paired caption

Below are the first few lines of uid_caption.csv:

uid,caption
346904_mscoco_0,A bus driving down a road by a building.
416478_mscoco_2,A woman and two men looking at a laptop screen.
155051_mscoco_0,a close of up a clock that makes the moon look small next to it
135161_mscoco_3,A bathroom being renovated featuring a toilette and shower.
280036_mscoco_2,A boy in black sweater standing on beach flying a kite.
439969_mscoco_0,A rusted pink fire hydrant in the grass
...

Calculate R-precision

1. Generate 30K images from the captions of uid_caption.csv in a directory $image_dir. The images should be either .jpg or .png format.

2. Download COCO val 2014 images from http://images.cocodataset.org/zips/val2014.zip at $coco_image_dir.

3. Calculate R-precision

python rprecision.py \
    --uid_caption_path uid_caption.csv \
    --image_dir $image_dir \
    --coco_image_dir $coco_image_dir