Test set contaminated with Trainval objects!

[j-alex-hanson]

Hi there!

I was hoping to use the AI-TOD dataset, but noticed that both v1 and v2 have contaminated the test set with trainval objects and parts of trainval images.

This is happening because the overlapping crops from each xView dataset image are split across the train, val, and test sets.

Additionally this inflates the total number of objects.

Here are the stats I’ve found:

V1:
Total number of contaminating xview bboxes: 84704
Total number of unique xview bboxes: 308286
Total number of xview bboxes: 443943
Total number of bboxes: 700621

V2:
Total number of contaminating xview bboxes: 44801
Total number of unique xview bboxes: 419747
Total number of xview bboxes: 475857
Total number of bboxes: 752746

Given this overlap on xView, I'm concerned there may be additional contamination in the non-xView images. But without access to the script creating the non-xView images crops, I'm not able to check this.

I sent an email 2 months ago regarding this but haven't heard back yet. Let me know if I’m mistaken!

Here's the script I created to measure these numbers:

import json
import os
import numpy as np
from tqdm import tqdm


def process_annos(annos):

    id_to_filename = dict()
    print('Process Images:')
    for img_ann in tqdm(annos['images']):
        filename = img_ann['file_name']
        filename_arr = filename.split('_')
        if len(filename_arr) == 4:
            id_ = img_ann['id']
            id_to_filename[id_] = filename

    xview_id_to_ori_bboxes = dict()
    print('Process Annotations:')
    for ann in tqdm(annos['annotations']):
        if ann['iscrowd']:
            continue
        if not ann['image_id'] in id_to_filename.keys():
            continue

        filename = id_to_filename[ann['image_id']]
        filename = os.path.splitext(filename)[0]
        filename_arr = filename.split('_')
        xview_id = filename_arr[0]
        offsets = filename_arr[-2:]
        offsets = np.array([float(coord) for coord in offsets])

        bbox = ann['bbox']
        bbox = np.array(bbox)
        bbox[:2] += offsets

        if xview_id not in xview_id_to_ori_bboxes.keys():
            xview_id_to_ori_bboxes[xview_id] = []

        xview_id_to_ori_bboxes[xview_id].append(bbox)

    return xview_id_to_ori_bboxes


aitod_test_filename = './aitod_test_v1_1.0.json'
# aitod_test_filename = './AI-TOD-v2/aitodv2_test.json'
with open(aitod_test_filename, 'r') as f:
    annos_test = json.load(f)
print('Process Test:')
xview_id_to_ori_bboxes_test = process_annos(annos_test)


aitod_trainval_filename = './aitod_trainval_v1_1.0.json'
# aitod_trainval_filename = './AI-TOD-v2/aitodv2_trainval.json'
with open(aitod_trainval_filename, 'r') as f:
    annos_trainval = json.load(f)
print('Process Trainval:')
xview_id_to_ori_bboxes_trainval = process_annos(annos_trainval)


total_num_bboxes = len(annos_test['annotations']) + len(annos_trainval['annotations'])

total_num_contaminating_bboxes = 0
total_num_xview_bboxes = 0
total_num_unique_xview_bboxes = 0
for xv_id, bboxes_test in xview_id_to_ori_bboxes_test.items():
    if not xv_id in xview_id_to_ori_bboxes_trainval.keys():
        continue
    bboxes_train = xview_id_to_ori_bboxes_trainval[xv_id]

    num_bboxes_test = len(bboxes_test)
    num_bboxes_train = len(bboxes_train)
    total_num_xview_bboxes += num_bboxes_test
    total_num_xview_bboxes += num_bboxes_train

    # duplicates also exist among test bboxes, remove these
    bboxes_test = np.unique(bboxes_test, axis=0)
    # duplicates also exist among trainval bboxes, remove these
    bboxes_train = np.unique(bboxes_train, axis=0)

    num_unique_bboxes_test = len(bboxes_test)
    num_unique_bboxes_train = len(bboxes_train)
    total_num_unique_xview_bboxes += num_unique_bboxes_test
    total_num_unique_xview_bboxes += num_unique_bboxes_train

    all_bboxes = np.concatenate((bboxes_test, bboxes_train))
    unique_bboxes, counts = np.unique(all_bboxes, axis=0, return_counts=True)
    contaminating_bboxes = unique_bboxes[counts > 1]
    num_contaminating_bboxes = len(contaminating_bboxes)
    if num_contaminating_bboxes > 0:
        print(f'Xview id: {xv_id} \t Contaminating bboxes: {num_contaminating_bboxes}')
        total_num_contaminating_bboxes += num_contaminating_bboxes

print(f'Total number of contaminating xview bboxes: {total_num_contaminating_bboxes}')
print(f'Total number of unique xview bboxes: {total_num_unique_xview_bboxes}')
print(f'Total number of xview bboxes: {total_num_xview_bboxes}')
print(f'Total number of bboxes: {total_num_bboxes}')

[Chasel-Tsui]

Hi Alex,

Thanks for your interest in the AI-TOD series. I think this observation is interesting, and we haven't noticed it yet. Given that the original version of this dataset has been used in the community for benchmarking algorithms for more than two years, it is hard to make adjustments to it and filter out overlapping parts if they do exist.

In this case, I recommend using v2 since the number of overlapping boxes takes up a small portion of the whole dataset. Besides, as long as the method was evaluated under the same experimental setting, I think this dataset is still a good candidate for benchmarking tiny object detection since the sota mAP is still extremely low. Moreover, you can filter out these overlapping boxes and get a high-quality dataset if you would like to.