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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021uniformnoise-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	dinov2_vitg14_reg_lc	.897
2	dinov2_vitl14_lc_res336	.874
3	dinov2_vitl14_reg_lc	.873
4	dinov2_vitg14_lc	.846
5	eva02_large_in22k	.830
6	vit_large_patch14_clip_224:openai_ft_in1k	.821
7	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.818
8	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.816
9	convnext_xxlarge_clip	.811
10	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.805
11	convnext_base_fb_in22k_ft_in1k	.804
12	dinov2_vitl14_lc	.801
13	deit_base_imagenet_full_seed-0	.799
14	deit_large_imagenet_full_seed-0	.796
15	convnext_large_in22k_supervised	.792
16	convnext_base_fb_in1k	.791
17	convnext_xlarge:fb_in22k_ft_in1k	.776
18	vit_large_clip336_openai	.776
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.772
20	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.772
21	deit3_base_patch16_224_fb_in1k	.765
22	convnext_large:fb_in22k_ft_in1k	.762
23	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.757
24	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.750
25	vit_huge_clip336_laion	.749
26	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.743
27	convnext_large_clip_laion2b_finelayers	.743
28	convnext_tiny:in12k_ft_in1k	.730
29	AdvProp_efficientnet-b7	.724
30	effnetv2m_custom384	.720
31	AdvProp_efficientnet-b6	.708
32	convnext_large_imagenet_full_seed-0	.704
33	resnext101_32x32d_wsl	.701
34	convnext_base_imagenet_full_seed-0	.701
35	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.701
36	vit_large_patch14_clip_336:laion2b_ft_in1k	.700
37	resnext101_32x48d_wsl	.696
38	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.696
39	resnext101_32x16d_wsl	.693
40	deit_small_imagenet_full_seed-0	.689
41	seresnext50_32x4d	.688
42	convnext_base:clip_laiona_augreg_ft_in1k_384	.686
43	focalnet_tiny_lrf_in1k	.680
44	efficientnet-b7	.674
45	fixres_resnext101_32x48d_wsl	.671
46	vit_large_patch14_clip_224:laion2b_ft_in1k	.670
47	convnext_small_imagenet_full_seed-0	.669
48	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.657
49	regnety_032	.657
50	AdvProp_efficientnet-b4	.656
51	AT_efficientnet-b4	.656
52	pnasnet_large-tf	.654
53	resnext101_32x8d_wsl	.650
54	nasnet_large-tf	.649
55	convnext_base_fb_in1k_gram_aligned_dinov3	.647
56	vit_base_patch16_clip_224:openai_ft_in1k	.644
57	deit3_base_patch16_224_fb_in22k_ft_in1k	.644
58	pnasnet_large_halfPrecision	.641
59	pnasnet_large	.641
60	resnet50-SIN	.632
61	vit_relpos_base_patch16_clsgap_224:sw_in1k	.632
62	inception_v4-tf	.627
63	convnext_tiny_imagenet_full_seed-0	.627
64	cv_18_dagger_408_pretrained	.626
65	cvt_cvt-21-224-in1k_4_LucyV4	.621
66	pnasnet_large_pytorch	.621
67	ViT_L_32_imagenet1k	.618
68	AT_efficientnet-b2	.618
69	AdvProp_efficientnet-b2	.618
70	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.616
71	nasnet_large	.616
72	AdvProp_efficientnet-b8	.613
73	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.604
74	resnet-101_v2-tf	.599
75	resnet-152_v2-tf	.595
76	ViT_B_32_imagenet1k	.586
77	resnet-152_v2_pytorch	.579
78	inception_v4	.578
79	inception_v2-tf	.575
80	resnet_101_v2	.573
81	resnet50-moclr8deg	.571
82	cvt_cvt-21-384-in1k_4_LucyV4	.568
83	densenet-201-keras	.561
84	densenet-169-keras	.560
85	densenet-201	.560
86	densenet_201_pytorch	.560
87	densenet-169	.560
88	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.555
89	resnet-50_v2-tf	.554
90	inception_v3_pytorch	.554
91	AdvProp_efficientnet-b0	.551
92	xception-keras	.549
93	xception	.547
94	cvt_cvt-13-224-in1k_4_LucyV4	.546
95	inception_v3-tf	.542
96	cvt_cvt-13-384-in1k_4_LucyV4	.542
97	convnext_tiny_sup	.540
98	antialias-resnet152	.540
99	effnetb1_272x240	.535
100	BiT-S-R101x1	.530
101	resnet152_imagenet_full	.530
102	eBarlow_lmda_02_1000ep	.530
103	inception_v1-tf	.524
104	resnet50_ewconplus_wzc	.522
105	resnet_152_v1	.516
106	inception_v3	.515
107	antialiased-rnext101_32x8d	.514
108	BiT-S-R152x2	.511
109	resnet50_hvs_wzc	.511
110	nasnet_mobile	.507
111	convnext_femto_ols:d1_in1k	.507
112	densenet-121-keras	.506
113	densenet-121	.506
114	BiT-S-R152x4	.505
115	resnet-152_v1-tf	.505
116	resnet50_st_wzc_seed6_2_323	.504
117	resnet50_moco_v2	.501
118	resnet50_st_wzc_2	.501
119	efficientnet_b0	.499
120	resnet50-vitoimagevidnet8	.499
121	resnet50_st_wzc_seed3_2_323	.499
122	resnet_152_v2	.497
123	resnet_SIN_IN_FT_IN	.496
124	resnet50-SIN_IN_IN	.496
125	resnet50-SIN_IN	.496
126	resnet50_st_wzc_seed7_2_323	.490
127	resnet_101_v1	.487
128	resnet-101_v1-tf	.486
129	resnet50-VITO-8deg-cc	.486
130	resnet50_ewconplus_wzc_seed2_2__323	.486
131	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.481
132	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.481
133	grcnn_109	.480
134	BiT-S-R50x3	.479
135	voneresnet-50-non_stochastic	.479
136	voneresnet-50	.477
137	resnet101_imagenet_full	.476
138	BiT-S-R101x3	.475
139	tv_efficientnet-b1	.475
140	CLIP-RN50	.471
141	BiT-S-R50x1	.469
142	vgg-19-keras	.469
143	vonegrcnn_62e_nobn	.468
144	resnet50_ewconplus_wzc_seed5_2__323	.466
145	vgg-16-keras	.464
146	resnet-50_v1-tf	.463
147	vonegrcnn_62e_nobn	.461
148	resnet_50_v2	.455
149	resnet-34	.453
150	Res2Net50_26w_4s	.449
151	vonegrcnn_47e	.449
152	resnet-50-pytorch	.445
153	resnet50-sup	.445
154	resnet50_tutorial	.445
155	resnet_50_v1	.445
156	mobilenet_v2_1-4_224_pytorch	.443
157	mobilenet_v2_1_4_224	.443
158	vonegrcnn_47e	.440
159	vonegrcnn_52e_full	.440
160	vonegrcnn_52e_full	.435
161	resnet50_imagenet_full	.430
162	resnet50	.427
163	antialiased-r50	.427
164	resnet_50_v1_spiking	.420
165	grcnn	.417
166	voneresnet-50-robust	.415
167	mobilenet_v2_1.4_224-tf	.414
168	mobilenet_v1_0.75_192	.410
169	mobilenet_v2_1_3_224	.410
170	mobilenet_v2_1.3_224-tf	.407
171	resnet34_imagenet_full	.406
172	mobilenet_v1_1.0_192	.405
173	blt_vs	.405
174	mobilenet_v2_1.0_192-tf	.404
175	mobilenet_v1_1.0_160	.404
176	inception_v1	.396
177	nasnet_mobile-tf	.395
178	CORnet-S	.394
179	efficientnet_b2_imagenet_full	.393
180	mobilenet_v1_0.5_160	.384
181	efficientnet_b0_imagenet_full	.383
182	resnet50_eMMCR_Vanilla	.381
183	convnext_small_imagenet_100_seed-0	.380
184	efficientnet_b1_imagenet_full	.380
185	mobilenet_v1_0.75_160	.375
186	mobilenet_v1_0.75_224	.374
187	eicircuit_resnet18_bn_20260919	.374
188	resnet18_imagenet_full	.372
189	mobilenet_v1_1.0_128	.371
190	mobilenet_v2_0.75_192-tf	.369
191	mobilenet_v1_1.0_224	.366
192	regnet_y_400mf	.366
193	mobilenet_v2_1.0_160-tf	.359
194	mobilenet_v1_0.75_128	.357
195	mobilenet_v2_1_0_160	.355
196	vgg_16	.351
197	mobilenet_v2_0.5_192-tf	.350
198	vgg_19	.350
199	mobilenet_v2_1_0_128	.345
200	ReAlnet04	.345
201	mobilenet_v1_0.5_192	.344
202	AlexNet_SIN	.343
203	AlexNet_SIN_fov4	.343
204	AlexNet_SIN_fov12	.343
205	AlexNet_SIN_fov16	.343
206	ReAlnet09	.341
207	mobilenet_v2_1.0_128-tf	.340
208	resnet_50_v1_spiking_l4a	.340
209	mobilenet_v1_0.5_224	.339
210	mobilenet_v2_0.75_160-tf	.339
211	mobilenet_v2_0_75_160	.335
212	resnet-18	.330
213	custom_model_cv_18_dagger_408	.330
214	mobilenet_v2_1_0_192	.330
215	ReAlnet06	.329
216	ReAlnet01	.329
217	mobilenet_v2_1.0_224-tf	.326
218	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.324
219	resnet50_robust_l2_eps1	.323
220	mobilenet_v2_1_0_224	.319
221	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.318
222	mobilenet_v2_0_75_192	.318
223	ReAlnet02	.318
224	ReAlnet08	.316
225	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.315
226	mobilenet_v2_0.75_224-tf	.314
227	ReAlnet05	.314
228	mobilenet_v2_0.75_128	.311
229	mobilenet_v1_0.5_128	.311
230	ReAlnet03	.310
231	mobilenet_v2_0_75_224	.309
232	ReAlnet10	.307
233	ReAlnet07	.301
234	mobilenet_v2_0.5_160	.300
235	mobilenet_v1_0.25_192	.294
236	deit_small_imagenet_100_seed-0	.292
237	mobilenet_v1_0.25_160	.291
238	mobilenet_v2_0.5_128	.291
239	mobilenet_v2_0_5_192	.290
240	mobilenet_v1_0.25_224	.287
241	mobilenet_v2_1.0_96	.285
242	shufflenet_v2_x1_0	.284
243	mobilenet_v2_0.75_96	.281
244	resnet50_l2_3_robust	.273
245	resnet-50-robust	.269
246	mobilenet_v2_0.35_192	.269
247	imagenet_l2_3_0	.269
248	resnet50_robust_l2_eps3	.269
249	0.5x_resnet-18_LC_w_sh_1_iter	.265
250	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.259
251	mobilenet_v2_0.35_128	.259
252	mobilenet_v2_0.5_224-tf	.258
253	mobilenet_v2_0_5_224	.256
254	mobilenet_v2_0.35_160	.255
255	mobilenet_v2_0.5_96	.250
256	mobilenet_v1_0.25_128	.247
257	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.242
258	resnet_50_v1_spiking_l4	.231
259	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.230
260	alexnet_early_checkpoint	.229
261	mobilenet_v2_0.35_224	.226
262	alexnet_random_linf8_perturb	.221
263	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.214
264	squeezenet1_1	.212
265	squeezenet1_0	.211
266	imagenet_l2_10_0	.201
267	mobilenet_v2_0.35_96	.196
268	alexnet_robust_correct	.194
269	resnet50_imagenet_100_seed-0	.181
270	alexnet_l2_3_robust	.180
271	alexnet	.177
272	alexnet_ks_torevert	.177
273	alexnet_fov4	.177
274	alexnet_fov12	.177
275	alexnet_fov16	.177
276	resnet50_linf_8_robust	.158
277	alexnet_linf_8_robust	.117
278	convnext_small_imagenet_10_seed-0	.090
279	deit_small_imagenet_1_seed-0	.089
280	resnet50-vicregl0p75	.087
281	convnext_small_imagenet_1_seed-0	.080
282	resnet50_imagenet_10_seed-0	.077
283	deit_small_imagenet_10_seed-0	.076
284	resnet50_imagenet_1_seed-0	.072
285	barlow-twins-resnet50	.069
286	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.066
287	resnet50-simclr-vissl	.064
288	alexnet_training_seed_05	.062
289	alexnet_training_seed_06	.062
290	alexnet_training_seed_02	.062
291	alexnet_training_seed_04	.062
292	alexnet_training_seed_08	.062
293	alexnet_training_seed_10	.062
294	alexnet_training_seed_01	.062
295	alexnet_training_seed_01_fov4	.062
296	alexnet_training_seed_01_fov12	.062
297	alexnet_training_seed_01_fov16	.062
298	alexnet_training_seed_03	.062
299	mobilevit_small	.060
300	resnet50-vicregl0p9	.060
301	alexnet_training_seed_09	.059
302	resnet50-barlow	.058
303	resnet-50_untrained	.058
304	artResNet18_1	.058
305	alexnet_training_seed_07	.056
306	resnet_50_v1_spiking_l3	.051
307	ViT-B/32
308	BiT-M-R50x1
309	resnet-18-LC_w_sh_100_iter_conv_init_m
310	resnet-18-LC_w_sh_1_iter_conv_init_m
311	BiT-M-R152x2
312	BiT-M-R50x3
313	resnet-18-LC_w_sh_10_iter_m
314	resnet-18-LC_w_sh_100_iter_m
315	resnet-18-LC_w_sh_10_iter_conv_init_m
316	FrankRobWobv0
317	resnet-18-LC_w_sh_1_iter_m
318	0.5x_resnet-18_LC
319	CORnetZ_CIFAR10
320	resnet18-local_aggregation
321	r3m_resnet18
322	CORnet-Z
323	vggface
324	my-model
325	resnet18-instance_recognition
326	resnet18-deepcluster
327	0.5x_resnet-18_LC_w_sh_10_iter
328	r3m_resnet50
329	r3m_resnet34
330	resnet-18-LC_m
331	resnet18-supervised
332	r3m_resnet50_nocrop
333	resnet-18-LC_1st_conv_m
334	resnet-18-LC_conv_init_m
335	resnet18-contrastive_multiview
336	0.5x_resnet-18
337	0.5x_resnet-18_LC_w_sh_100_iter
338	RN50
339	RN50
340	resnet18-colorization
341	resnet18-relative_position
342	v1-pyr-nodown
343	resnet18-simclr
344	resnet18-autoencoder
345	ViT_L_32
346	ViT_B_32
347	resnet18-depth_prediction
348	ViT_B_16
349	yudixie_resnet50_cat_obj_class_all_latents_0_240908
350	r3m_resnet34_nocrop
351	resnet18-contrastive_predictive
352	resnet-18-LC_untrained
353	resnet-18_untrained
354	resnet-50x2_untrained
355	dcgan
356	unet_entire
357	CORnetZ_CIFAR10_bs32_20_04
358	pixels
359	dorinet_cornet_z
360	omnivore_swinT
361	openclip
362	vonealexnet_gaussian_noise_std4_fixed
363	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
364	yudixie_resnet50_imagenet1kpret_0_240312
365	yudixie_resnet50_category_class_0_240908
366	yudixie_resnet50_distance_reg_0_240908
367	resnet18_imagenet21kP
368	ConvLSTM
369	PredRNN
370	SimVP
371	TAU
372	MIM
373	hmax
374	vit_base_patch16_dinov3-lvd1689m
375	vit_base_patch14_reg4_dinov2-lvd142m
376	yolos_tiny
377	ReAlnet01_cornet
378	briaai_rmbg_1_4
379	ReAlnet03_cornet
380	yudixie_resnet18_translation_reg_0_240719
381	yudixie_resnet18_distance_reg_0_240719
382	yudixie_resnet18_rotation_reg_0_240719
383	yudixie_resnet18_imagenet1kpret_0_240719
384	compact_resnet50_V4
385	yudixie_resnet50_rotation_reg_0_240908
386	yudixie_resnet50_distance_translation_0_240908
387	yudixie_resnet50_distance_rotation_0_240908
388	resnet18_ecoset_full
389	ReAlnet01_cornet_fov4
390	resnet34_ecoset_full
391	compact_resnet50_IT
392	resnet101_ecoset_full
393	vit_base_patch14_dinov2-lvd142m
394	resnet152_ecoset_full
395	ReAlnet04_cornet
396	compact_vgg19_V4
397	yudixie_resnet18_translation_rotation_0_240719
398	omnivore_swinS
399	ReAlnet01_cornet_fov12
400	resnet-18_test_m
401	yudixie_resnet18_category_class_0_240719
402	ReAlnet05_cornet
403	yudixie_resnet18_random_0_240719
404	yudixie_resnet50_translation_reg_0_240908
405	yudixie_resnet18_cat_obj_class_all_latents_0_240719
406	compact_vgg19_IT
407	ReAlnet06_cornet
408	SeLaVi-AVE
409	ReAlnet01_cornet_fov16
410	compact_resnet50_robust_V4
411	r3d_18
412	yudixie_resnet50_translation_rotation_0_240908
413	ReAlnet01_ALL
414	mc3_18
415	ReAlnet02_ALL
416	s3d
417	VOneCORnet-S
418	resnet50-vicreg
419	ReAlnet08_cornet
420	omnivore_swinB
421	ReAlnet04_ALL
422	mvit_v1_b
423	compact_cornets_V4
424	ReAlnet03_ALL
425	ReAlnet09_cornet
426	compact_cornets_IT
427	SeLaVi-Kinetics400
428	ReAlnet05_ALL
429	SeLaVi-VGG-Sound
430	ReAlnet06_ALL
431	mvit_v2_s
432	ReAlnet10_cornet
433	compact_resnet50_robust_IT
434	yudixie_resnet50_distance_translation_rotation_0_240908
435	ReAlnet07_ALL
436	SeLaVi-Kinetics-Sound
437	r2plus1d_18
438	resnet50_ecoset_full
439	yudixie_resnet18_object_class_0_240719
440	yudixie_resnet50_imagenet1kpret_0_240908
441	ReAlnet08_ALL
442	yudixie_resnet18_distance_translation_rotation_0_240719
443	yudixie_resnet18_distance_rotation_0_240719
444	ReAlnet09_ALL
445	yudixie_resnet18_distance_translation_0_240719
446	ReAlnet02_cornet
447	ReAlnet07_cornet
448	yudixie_resnet50_random_0_240908
449	yudixie_resnet50_object_class_0_240908
450	resnet50_primary_visual_cortex
451	ReAlnet10_ALL
452	resnet18_fair
453	pr1_stl10_disentangled_alpha04
454	predictive_coding_pc
455	yudixie_wbh_object_class_scaling_0415_run_0
456	yudixie_wbh_object_class_scaling_0415_run_3
457	yudixie_wbh_object_class_scaling_0415_imagenet
458	yudixie_wbh_object_class_scaling_0415_run_2
459	yudixie_wbh_object_class_scaling_0415_run_1
460	deepeisnn_ei_cifar10_vgg19
461	deepeisnn_ei_cifar10_resnet18
462	deepeisnn_ei_imagenet_resnet18
463	dinov2_vitl14_reg_lc_featread
464	gabor_filter_entropy_4x4
465	gabor_filter_mean_abs_4x4_v2
466	gabor_filter_entropy_4x4_v2
467	gabor_filter_entropy_4x4_v2_forcedCosine

Benchmark bibtex

@article{geirhos2021partial,
              title={Partial success in closing the gap between human and machine vision},
              author={Geirhos, Robert and Narayanappa, Kantharaju and Mitzkus, Benjamin and Thieringer, Tizian and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
              journal={Advances in Neural Information Processing Systems},
              volume={34},
              year={2021},
              url={https://openreview.net/forum?id=QkljT4mrfs}
        }

Ceiling

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021uniformnoise

Metric: top1