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VietPhong/kitti-yolo11n-robustness-benchmark

KITTI YOLO11n Robustness & Adversarial Benchmark Suite This dataset contains 649,425 benchmark samples evaluating the perception robustness of YOLO11n (Ultralytics YOLOv11 nano in original FP32 precision) on the official KITTI Object Detection train set (3,711 images) under 35 attack & corruption techniques across 5 severity levels. ?? Benchmark Leaderboard (mAP@0.5 Drop on YOLO11n) Clean Baseline AP50: 0.3555 Evaluation Model: YOLO11n (Original weights:… See the full description on the dataset page: https://huggingface.co/datasets/VietPhong/kitti-yolo11n-robustness-benchmark.

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KITTI YOLO11n Robustness & Adversarial Benchmark Suite

This dataset contains 649,425 benchmark samples evaluating the perception robustness of YOLO11n (Ultralytics YOLOv11 nano in original FP32 precision) on the official KITTI Object Detection train set (3,711 images) under 35 attack & corruption techniques across 5 severity levels.

?? Benchmark Leaderboard (mAP@0.5 Drop on YOLO11n)

—Clean Baseline AP50: 0.3555
—Evaluation Model: YOLO11n (Original weights: yolo11n.pt, FP32 precision, size: 640x640)
—Target Classes: Car, Pedestrian, Cyclist

Attack Name	Group	Sev 1 AP (Drop)	Sev 2 AP (Drop)	Sev 3 AP (Drop)	Sev 4 AP (Drop)	Sev 5 AP (Drop)	Mean $\Delta$mAP
`brightness`	A	0.350 (-1.5%)	0.344 (-3.2%)	0.338 (-5.0%)	0.335 (-5.8%)	0.332 (-6.7%)	-0.0158
`contrast`	A	0.316 (-11.1%)	0.298 (-16.1%)	0.274 (-23.1%)	0.204 (-42.6%)	0.080 (-77.5%)	-0.1211
`cw_l2`	D	0.335 (-5.7%)	0.255 (-28.3%)	0.193 (-45.6%)	0.162 (-54.4%)	0.153 (-57.0%)	-0.1358
`defocus_blur`	A	0.347 (-2.3%)	0.328 (-7.7%)	0.286 (-19.4%)	0.259 (-27.1%)	0.220 (-38.1%)	-0.0673
`depth_fog`	B	0.291 (-18.1%)	0.213 (-40.1%)	0.036 (-89.9%)	0.000 (-99.9%)	0.000 (-99.9%)	-0.2474
`depth_rain`	B	0.352 (-1.0%)	0.352 (-1.0%)	0.348 (-2.0%)	0.345 (-3.1%)	0.338 (-5.0%)	-0.0086
`depth_snow`	B	0.351 (-1.3%)	0.344 (-3.2%)	0.326 (-8.2%)	0.314 (-11.8%)	0.210 (-41.0%)	-0.0466
`dpatch`	E	0.343 (-3.5%)	0.334 (-6.0%)	0.325 (-8.7%)	0.307 (-13.7%)	N/A	-0.0283
`elastic_transform`	A	0.352 (-0.9%)	0.352 (-1.0%)	0.341 (-4.1%)	0.341 (-4.2%)	0.325 (-8.7%)	-0.0136
`fgsm`	D	0.226 (-36.3%)	0.185 (-48.0%)	0.158 (-55.5%)	0.138 (-61.2%)	0.097 (-72.6%)	-0.1946
`fog`	A	0.309 (-13.0%)	0.300 (-15.6%)	0.287 (-19.2%)	0.292 (-17.7%)	0.273 (-23.2%)	-0.0630
`frame_freeze`	C	0.352 (-1.1%)	0.322 (-9.6%)	0.287 (-19.4%)	0.238 (-33.0%)	0.208 (-41.6%)	-0.0745
`frost`	A	0.312 (-12.4%)	0.264 (-25.8%)	0.228 (-36.0%)	0.211 (-40.8%)	0.186 (-47.5%)	-0.1155
`gaussian_blur`	A	0.354 (-0.5%)	0.335 (-5.8%)	0.304 (-14.4%)	0.273 (-23.1%)	0.207 (-41.8%)	-0.0609
`gaussian_noise`	A	0.330 (-7.1%)	0.317 (-10.7%)	0.284 (-20.0%)	0.244 (-31.2%)	0.172 (-51.8%)	-0.0859
`glass_blur`	A	0.307 (-13.5%)	0.308 (-13.3%)	0.231 (-35.0%)	0.207 (-41.7%)	0.153 (-56.8%)	-0.1140
`impulse_noise`	A	0.278 (-21.8%)	0.228 (-36.0%)	0.178 (-50.0%)	0.094 (-73.5%)	0.021 (-94.1%)	-0.1957
`jpeg_compression`	A	0.339 (-4.6%)	0.317 (-10.8%)	0.318 (-10.6%)	0.305 (-14.1%)	0.284 (-20.1%)	-0.0428
`mi_fgsm`	D	0.192 (-46.0%)	0.131 (-63.2%)	0.096 (-73.1%)	0.070 (-80.2%)	0.045 (-87.3%)	-0.2488
`motion_blur`	A	0.349 (-1.8%)	0.320 (-10.1%)	0.277 (-22.1%)	0.179 (-49.6%)	0.101 (-71.6%)	-0.1104
`object_occlusion`	C	0.322 (-9.3%)	0.186 (-47.8%)	0.004 (-98.8%)	0.000 (-100.0%)	0.000 (-100.0%)	-0.2531
`pgd`	D	0.149 (-58.0%)	0.096 (-73.2%)	0.060 (-83.2%)	0.039 (-88.9%)	0.024 (-93.3%)	-0.2820
`pixelate`	A	0.352 (-1.1%)	0.354 (-0.4%)	0.343 (-3.4%)	0.306 (-14.0%)	0.289 (-18.6%)	-0.0266
`random_erasing`	C	0.338 (-5.0%)	0.322 (-9.5%)	0.296 (-16.8%)	0.264 (-25.7%)	0.245 (-31.2%)	-0.0627
`random_noise_linf`	F	0.353 (-0.8%)	0.353 (-0.8%)	0.352 (-1.0%)	0.349 (-1.7%)	0.335 (-5.8%)	-0.0072
`saturate`	A	0.345 (-3.1%)	0.337 (-5.1%)	0.342 (-3.9%)	0.310 (-12.7%)	0.302 (-15.0%)	-0.0283
`sensor_fault`	C	0.352 (-0.9%)	0.326 (-8.3%)	0.306 (-14.0%)	0.280 (-21.2%)	0.211 (-40.6%)	-0.0604
`shot_noise`	A	0.317 (-10.8%)	0.288 (-18.8%)	0.241 (-32.3%)	0.150 (-57.9%)	0.074 (-79.3%)	-0.1416
`snow`	A	0.256 (-28.0%)	0.214 (-39.9%)	0.169 (-52.6%)	0.125 (-64.7%)	0.162 (-54.4%)	-0.1704
`spatter`	A	0.352 (-1.1%)	0.315 (-11.3%)	0.264 (-25.8%)	0.270 (-24.1%)	0.206 (-42.0%)	-0.0740
`speckle_noise`	A	0.343 (-3.4%)	0.335 (-5.7%)	0.294 (-17.2%)	0.269 (-24.2%)	0.238 (-33.0%)	-0.0594
`square_attack`	F	0.335 (-5.8%)	0.337 (-5.3%)	0.338 (-5.0%)	N/A	N/A	-0.0191
`thys_patch`	E	0.350 (-1.6%)	0.343 (-3.6%)	0.333 (-6.3%)	0.327 (-8.1%)	N/A	-0.0174
`tog`	D	0.148 (-58.4%)	0.101 (-71.7%)	0.066 (-81.5%)	0.035 (-90.1%)	0.024 (-93.4%)	-0.2809
`zoom_blur`	A	0.228 (-35.9%)	0.177 (-50.1%)	0.138 (-61.3%)	0.108 (-69.7%)	0.076 (-78.7%)	-0.2102

?? Dataset Structure

Each sample is stored in Apache Parquet format containing image bytes and rich detection metadata:

python

from datasets import load_dataset

dataset = load_dataset("VietPhong/kitti-yolo11n-robustness-benchmark", split="train", streaming=True)
sample = next(iter(dataset))

print("Original Image ID:", sample["original_image_id"])
print("Attack:", sample["attack_name"], "Severity:", sample["severity"])
print("PSNR (dB):", sample["psnr_db"], "SSIM:", sample["ssim"])
print("YOLO11n Clean Predictions:", sample["clean_predictions"])
print("YOLO11n Attacked Predictions:", sample["attacked_predictions"])

??? Reproduction & Provenance

Generated using the AdverTest Framework. All evaluations use strict FP32 baseline execution.