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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021cueconflict-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.943
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.938
3	convnext_xxlarge_clip	.937
4	dinov2_vitg14_reg_lc	.933
5	dinov2_vitg14_lc	.881
6	eva02_large_in22k	.873
7	vit_large_patch14_clip_224:laion2b_ft_in1k	.855
8	dinov2_vitl14_lc_res336	.846
9	dinov2_vitl14_reg_lc	.845
10	vit_large_patch14_clip_224:openai_ft_in1k	.840
11	convnext_large_clip_laion2b_finelayers	.825
12	vit_huge_clip336_laion	.816
13	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.804
14	dinov2_vitl14_lc	.804
15	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.784
16	vit_large_clip336_openai	.782
17	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.780
18	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.752
19	vit_large_patch14_clip_336:laion2b_ft_in1k	.748
20	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.740
21	convnext_large:fb_in22k_ft_in1k	.698
22	convnext_base:clip_laiona_augreg_ft_in1k_384	.686
23	vision_transformer_vit_large_patch16_224	.656
24	convnext_xlarge:fb_in22k_ft_in1k	.651
25	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.633
26	convnext_large_in22k_supervised	.628
27	fixres_resnext101_32x48d_wsl	.611
28	convnext_base_fb_in22k_ft_in1k	.600
29	resnext101_32x32d_wsl	.596
30	resnext101_32x16d_wsl	.595
31	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
32	resnext101_32x48d_wsl	.560
33	vit_base_patch16_clip_224:openai_ft_in1k	.560
34	SWSL_resnext101_32x8d	.546
35	resnext101_32x8d_wsl	.540
36	deit3_base_patch16_224_fb_in1k	.519
37	CLIP_ViT-B_32	.482
38	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.477
39	resnet50-VITO-8deg-cc	.451
40	effnetv2m_custom384	.448
41	cvt_cvt-13-384-in1k_4_LucyV4	.441
42	resnet-152_v2-tf	.405
43	resnet_152_v2	.402
44	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.394
45	resnet_101_v2	.393
46	SWSL_resnet50	.390
47	resnet_50_v2	.383
48	vit_relpos_base_patch16_clsgap_224:sw_in1k	.380
49	ViT_B_16_imagenet1k	.376
50	cvt_cvt-21-384-in1k_4_LucyV4	.372
51	CLIP_resnet50	.371
52	CLIP_resnet50_float32	.371
53	pnasnet_large_halfPrecision	.357
54	effnetb1_272x240	.357
55	pnasnet_large	.355
56	efficientnet_b0	.346
57	inception_v3	.345
58	ViT_L_32_imagenet1k	.344
59	xception-keras	.343
60	resnet50_ewconplus_wzc_seed5_2__323	.339
61	nasnet_large	.332
62	cv_18_dagger_408_pretrained	.328
63	pnasnet_large-tf	.327
64	convnext_femto_ols:d1_in1k	.325
65	regnety_032	.323
66	convnext_tiny_imagenet_full_seed-0	.321
67	CLIP-RN50	.317
68	pnasnet_large_pytorch	.316
69	resnet101	.316
70	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.316
71	deit3_base_patch16_224_fb_in22k_ft_in1k	.315
72	focalnet_tiny_lrf_in1k	.313
73	inception_v3-tf	.311
74	ViT_B_32_imagenet1k	.309
75	resnet152_imagenet_full	.309
76	cvt_cvt-21-224-in1k_4_LucyV4	.309
77	inception_v3_pytorch	.300
78	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.299
79	convnext_tiny:in12k_ft_in1k	.297
80	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.294
81	resnet50_st_wzc_seed7_2_323	.294
82	resnet50_ewconplus_wzc	.293
83	xception	.292
84	resnet-50_v2-tf	.292
85	convnext_base_fb_in1k	.289
86	resnet50_ewconplus_wzc_seed2_2__323	.289
87	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.284
88	convnext_large_imagenet_full_seed-0	.284
89	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.282
90	convnext_base_imagenet_full_seed-0	.278
91	resnet50-SIN_IN	.272
92	resnet_101_v1	.271
93	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.271
94	nasnet_large-tf	.266
95	resnet-152_v2_pytorch	.260
96	resnet50_st_wzc_seed3_2_323	.257
97	resnet50_ewconplus_wzc_seed6_2_323	.256
98	resnet_152_v1	.255
99	inception_v4	.254
100	resnet50_byol	.254
101	AlexNet_SIN_fov4	.253
102	antialias-resnet152	.253
103	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.250
104	resnet50_imagenet_full	.244
105	resnet101_imagenet_full	.242
106	resnet50_hvs_wzc	.241
107	resnet50_st_wzc_2	.241
108	antialiased-rnext101_32x8d	.240
109	AdvProp_efficientnet-b7	.238
110	resnet_50_v1_spiking	.237
111	densenet-201	.236
112	densenet_201_pytorch	.236
113	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.235
114	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.235
115	custom_model_cv_18_dagger_408	.235
116	cvt_cvt-13-224-in1k_4_LucyV4	.233
117	resnet50	.233
118	densenet-201-keras	.233
119	resnet34_imagenet_full	.233
120	AT_efficientnet-b2	.232
121	AdvProp_efficientnet-b2	.232
122	resnet50-moclr8deg	.229
123	tv_efficientnet-b1	.229
124	efficientnet-b7	.228
125	resnet50-SIN_IN_IN	.228
126	resnet_SIN_IN_FT_IN	.228
127	resnet50_tutorial	.226
128	resnet_50_v1	.226
129	resnet-50-pytorch	.226
130	resnet50-sup	.226
131	resnet50_random_l2_perturb	.220
132	AdvProp_efficientnet-b6	.219
133	Res2Net50_26w_4s	.218
134	densenet-121-keras	.213
135	AdvProp_efficientnet-b4	.213
136	AT_efficientnet-b4	.213
137	densenet-121	.212
138	mobilenet_v2_0_5_224	.211
139	mobilenet_v2_1-4_224_pytorch	.210
140	mobilenet_v2_1_4_224	.210
141	inception_v4-tf	.210
142	alexnet_fov4	.208
143	grcnn_109	.208
144	convnext_small_imagenet_full_seed-0	.206
145	convnext_base_fb_in1k_gram_aligned_dinov3	.204
146	voneresnet-50-non_stochastic	.204
147	ReAlnet04	.204
148	texture_shape_resnet50_trained_on_SIN	.202
149	seresnext50_32x4d	.201
150	resnet-152_v1-tf	.196
151	eBarlow_lmda_02_1000ep	.195
152	resnet50_moco_v2	.191
153	cornet_s	.191
154	nasnet_mobile	.190
155	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.189
156	mobilenet_v2_1.4_224-tf	.189
157	resnet-101_v1-tf	.189
158	BiT-S-R152x4	.189
159	vgg_16	.188
160	ReAlnet03	.187
161	resnet18_imagenet_full	.186
162	ReAlnet09	.184
163	antialiased-r50	.182
164	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.181
165	ReAlnet01	.181
166	deit_large_imagenet_full_seed-0	.181
167	resnet-50_v1-tf	.180
168	ReAlnet10	.179
169	grcnn	.179
170	resnet50_random_linf8_perturb	.177
171	BiT-S-R101x1	.177
172	blt_vs	.177
173	CORnet-S	.175
174	resnet50_linf_4_robust	.175
175	ReAlnet08	.173
176	resnet50_linf_8_robust	.173
177	BiT-S-R50x1	.173
178	mobilenet_v2_1_0_224	.171
179	inception_v1	.167
180	densenet-169	.166
181	ReAlnet06	.166
182	mobilenet_v1_0.75_224	.165
183	texture_shape_alexnet_trained_on_SIN	.164
184	mobilenet_v2_1_0_160	.164
185	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.163
186	BiT-S-R50x3	.163
187	resnet-18	.163
188	resnet50-vitoimagevidnet8	.162
189	voneresnet-50-robust	.162
190	resnet50_eMMCR_Vanilla	.160
191	inception_v2-tf	.160
192	deit_base_imagenet_full_seed-0	.160
193	AdvProp_efficientnet-b8	.159
194	ReAlnet05	.157
195	regnet_y_400mf	.157
196	mobilenet_v1_0.5_192	.157
197	vonealexnet_gaussian_noise_std4_fixed	.157
198	BiT-S-R152x2	.156
199	mobilenet_v1_1.0_224	.156
200	resnet50_l2_3_robust	.155
201	alexnet_reduced_aliasing_early_checkpoint	.155
202	inception_v1-tf	.154
203	alexnet_training_seed_03	.153
204	mobilenet_v2_1.0_224-tf	.153
205	ReAlnet07	.153
206	mobilenet_v2_1_3_224	.153
207	alexnet_random_linf8_perturb	.153
208	ReAlnet02	.153
209	mobilenet_v2_0.5_224-tf	.153
210	vonegrcnn_52e_full	.153
211	mobilenet_v2_1.3_224-tf	.152
212	resnet50_simclr	.152
213	densenet-169-keras	.149
214	mobilenet_v1_1.0_192	.147
215	vgg-16-keras	.146
216	0.5x_resnet-18_LC_w_sh_1_iter	.146
217	squeezenet1_1	.146
218	efficientnet_b1_imagenet_full	.145
219	vonegrcnn_47e	.144
220	resnet50_robust_l2_eps1	.144
221	resnet50-SIN	.144
222	efficientnet_b2_imagenet_full	.144
223	BiT-S-R101x3	.144
224	vgg_19	.142
225	alexnet_random_l2_3_perturb	.142
226	mobilenet_v2_0.5_96	.141
227	eicircuit_resnet18_bn_20260919	.141
228	mobilenet_v1_1.0_128	.140
229	mobilenet_v2_1.0_160-tf	.137
230	vonegrcnn_62e_nobn	.137
231	mobilenet_v1_0.75_192	.136
232	resnet-34	.136
233	mobilenet_v2_1_0_128	.135
234	AdvProp_efficientnet-b0	.135
235	alexnet_early_checkpoint	.135
236	efficientnet_b0_imagenet_full	.134
237	mobilenet_v2_0.35_128	.134
238	convnext_small_imagenet_100_seed-0	.133
239	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.133
240	mobilenet_v2_0.35_96	.133
241	konkle_alexnetgn_ipcl_ref01_primary_model	.132
242	mobilenet_v2_1.0_128-tf	.132
243	mobilenet_v1_0.25_224	.132
244	mobilenet_v2_0.75_224-tf	.132
245	AlexNet_SIN	.132
246	vonegrcnn_52e_full	.132
247	resnet-50-robust	.131
248	imagenet_l2_3_0	.131
249	resnet50_robust_l2_eps3	.131
250	mobilenet_v2_1_0_192	.130
251	alexnet_ks_torevert	.128
252	alexnet	.128
253	mobilenet_v2_0.5_192-tf	.128
254	resnet50_imagenet_100_seed-0	.127
255	vgg-19-keras	.127
256	vonegrcnn_62e_nobn	.127
257	mobilenet_v2_0_75_224	.125
258	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.125
259	mobilenet_v2_1.0_192-tf	.125
260	shufflenet_v2_x1_0	.123
261	mobilenet_v1_0.75_128	.122
262	voneresnet-50	.121
263	vonegrcnn_47e	.121
264	mobilenet_v2_0.75_96	.119
265	mobilenet_v2_0.5_160	.117
266	mobilenet_v1_1.0_160	.114
267	mobilenet_v1_0.25_192	.112
268	convnext_tiny_sup	.112
269	alexnet_l2_3_robust	.110
270	squeezenet1_0	.110
271	mobilenet_v2_0.5_128	.109
272	mobilenet_v2_0_75_192	.107
273	mobilenet_v2_0_5_192	.106
274	AlexNet_SIN_fov12	.104
275	mobilenet_v2_0.35_160	.102
276	mobilenet_v2_0.35_192	.102
277	mobilenet_v1_0.5_128	.102
278	mobilenet_v2_0.75_160-tf	.101
279	nasnet_mobile-tf	.101
280	mobilenet_v2_0_75_160	.098
281	mobilenet_v1_0.25_160	.098
282	mobilenet_v1_0.75_160	.097
283	deit_small_imagenet_full_seed-0	.096
284	mobilenet_v2_0.75_128	.096
285	convnext_small_imagenet_1_seed-0	.096
286	mobilenet_v2_0.35_224	.093
287	convnext_small_imagenet_10_seed-0	.091
288	deit_small_imagenet_10_seed-0	.090
289	mobilenet_v2_0.75_192-tf	.090
290	alexnet_fov12	.088
291	deit_small_imagenet_1_seed-0	.087
292	AlexNet_SIN_fov16	.083
293	mobilenet_v1_0.5_224	.083
294	resnet_50_v1_spiking_l4a	.081
295	mobilevit_small	.079
296	mobilenet_v2_1.0_96	.078
297	alexnet_training_seed_02	.068
298	alexnet_training_seed_04	.068
299	alexnet_training_seed_08	.068
300	alexnet_training_seed_10	.068
301	alexnet_training_seed_09	.067
302	alexnet_training_seed_07	.066
303	alexnet_robust_correct	.063
304	resnet_50_v1_spiking_l4	.060
305	mobilenet_v1_0.25_128	.059
306	mobilenet_v1_0.5_160	.055
307	barlow-twins-resnet50	.053
308	alexnet_training_seed_06	.050
309	resnet50-vicreg	.049
310	alexnet_fov16	.047
311	resnet50-barlow	.046
312	alexnet_linf_8_robust	.045
313	imagenet_l2_10_0	.034
314	resnet_50_v1_spiking_l3	.034
315	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.033
316	resnet50_FractalDB	.032
317	resnet50-vicregl0p9	.032
318	resnet-50_untrained	.031
319	deit_small_imagenet_100_seed-0	.028
320	artResNet18_1	.013
321	resnet50_imagenet_1_seed-0	.011
322	resnet50-simclr-vissl	.011
323	resnet50-vicregl0p75	.011
324	resnet50_imagenet_10_seed-0	.003
325	alexnet_training_seed_05	.003
326	alexnet_training_seed_01	.003
327	alexnet_training_seed_01_fov4	.003
328	alexnet_training_seed_01_fov12	.003
329	alexnet_training_seed_01_fov16	.003
330	resnet-18-LC_w_sh_100_iter_conv_init_m
331	ViT-B/32
332	BiT-M-R50x3
333	BiT-M-R50x1
334	BiT-M-R152x2
335	resnet-18-LC_w_sh_1_iter_conv_init_m
336	resnet-18-LC_w_sh_1_iter_m
337	resnet-18-LC_w_sh_10_iter_m
338	yudixie_resnet50_random_0_240908
339	resnet-18-LC_w_sh_100_iter_m
340	resnet-18-LC_w_sh_10_iter_conv_init_m
341	FrankRobWobv0
342	resnet18-local_aggregation
343	0.5x_resnet-18_LC
344	CORnetZ_CIFAR10
345	resnet18-deepcluster
346	r3m_resnet18
347	resnet18-colorization
348	CORnet-Z
349	resnet18-instance_recognition
350	my-model
351	vggface
352	0.5x_resnet-18_LC_w_sh_10_iter
353	r3m_resnet50
354	r3m_resnet34
355	resnet-18-LC_m
356	r3m_resnet50_nocrop
357	resnet18-supervised
358	resnet-18-LC_1st_conv_m
359	resnet-18-LC_conv_init_m
360	resnet18-contrastive_multiview
361	0.5x_resnet-18
362	RN50
363	0.5x_resnet-18_LC_w_sh_100_iter
364	RN50
365	resnet18-relative_position
366	resnet18-autoencoder
367	resnet18-simclr
368	ViT_L_32
369	ViT_L_16_imagenet1k
370	ViT_B_32
371	resnet18-depth_prediction
372	ViT_B_16
373	r3m_resnet34_nocrop
374	resnet18-contrastive_predictive
375	omnivore_swinT
376	resnet-18-LC_untrained
377	resnet-18_untrained
378	resnet-50x2_untrained
379	dcgan
380	v1-pyr-nodown
381	unet_entire
382	CORnetZ_CIFAR10_bs32_20_04
383	pixels
384	dorinet_cornet_z
385	my-model
386	openclip
387	resnet50-cifar
388	resnet50_pretrained_with_retinal_waves
389	resnet50_ImageNet
390	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
391	yudixie_resnet50_imagenet1kpret_0_240908
392	yudixie_resnet50_imagenet1kpret_0_240312
393	yudixie_resnet50_cat_obj_class_all_latents_0_240908
394	yudixie_resnet50_object_class_0_240908
395	yudixie_resnet50_category_class_0_240908
396	resnet18_imagenet21kP
397	ConvLSTM
398	PredRNN
399	yudixie_resnet50_distance_reg_0_240908
400	SimVP
401	briaai_rmbg_1_4
402	TAU
403	MIM
404	hmax
405	ReAlnet01_cornet
406	yolos_tiny
407	yudixie_resnet18_translation_reg_0_240719
408	ReAlnet02_cornet
409	ReAlnet03_cornet
410	yudixie_resnet18_distance_reg_0_240719
411	yudixie_resnet18_rotation_reg_0_240719
412	yudixie_resnet18_imagenet1kpret_0_240719
413	compact_resnet50_V4
414	vit_base_patch14_dinov2-lvd142m
415	vit_base_patch16_dinov3-lvd1689m
416	vit_base_patch14_reg4_dinov2-lvd142m
417	resnet18_ecoset_full
418	resnet34_ecoset_full
419	ReAlnet01_cornet_fov4
420	resnet50_ecoset_full
421	compact_resnet50_robust_V4
422	resnet101_ecoset_full
423	yudixie_resnet50_distance_rotation_0_240908
424	yudixie_resnet50_rotation_reg_0_240908
425	ReAlnet01_cornet_fov12
426	resnet152_ecoset_full
427	yudixie_resnet18_distance_translation_0_240719
428	ReAlnet04_cornet
429	yudixie_resnet50_distance_translation_0_240908
430	yudixie_resnet18_translation_rotation_0_240719
431	yudixie_resnet18_distance_rotation_0_240719
432	compact_vgg19_V4
433	yudixie_resnet18_category_class_0_240719
434	ReAlnet05_cornet
435	yudixie_resnet18_cat_obj_class_all_latents_0_240719
436	resnet-18_test_m
437	yudixie_resnet18_distance_translation_rotation_0_240719
438	compact_resnet50_IT
439	SeLaVi-AVE
440	ReAlnet06_cornet
441	r3d_18
442	compact_vgg19_IT
443	ReAlnet01_cornet_fov16
444	ReAlnet01_ALL
445	mc3_18
446	ReAlnet07_cornet
447	yudixie_resnet50_translation_rotation_0_240908
448	s3d
449	mvit_v1_b
450	ReAlnet08_cornet
451	ReAlnet02_ALL
452	ReAlnet03_ALL
453	compact_cornets_V4
454	VOneCORnet-S
455	ReAlnet04_ALL
456	ReAlnet09_cornet
457	SeLaVi-Kinetics400
458	SeLaVi-VGG-Sound
459	omnivore_swinB
460	compact_cornets_IT
461	SeLaVi-Kinetics-Sound
462	ReAlnet06_ALL
463	mvit_v2_s
464	omnivore_swinS
465	r2plus1d_18
466	compact_resnet50_robust_IT
467	ReAlnet10_cornet
468	ReAlnet07_ALL
469	yudixie_resnet18_object_class_0_240719
470	ReAlnet05_ALL
471	ReAlnet08_ALL
472	yudixie_resnet18_random_0_240719
473	ReAlnet09_ALL
474	yudixie_resnet50_translation_reg_0_240908
475	resnet50_primary_visual_cortex
476	yudixie_resnet50_distance_translation_rotation_0_240908
477	ReAlnet10_ALL
478	resnet18_fair
479	pr1_stl10_disentangled_alpha04
480	predictive_coding_pc
481	yudixie_wbh_object_class_scaling_0415_run_0
482	yudixie_wbh_object_class_scaling_0415_run_3
483	yudixie_wbh_object_class_scaling_0415_imagenet
484	yudixie_wbh_object_class_scaling_0415_run_2
485	yudixie_wbh_object_class_scaling_0415_run_1
486	deepeisnn_ei_cifar10_vgg19
487	deepeisnn_ei_cifar10_resnet18
488	deepeisnn_ei_imagenet_resnet18
489	dinov2_vitl14_reg_lc_featread
490	gabor_filter_entropy_4x4_v2
491	gabor_filter_entropy_4x4
492	gabor_filter_mean_abs_4x4_v2
493	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

0.33.

Note that scores are relative to this ceiling.

Data: Geirhos2021cueconflict

Metric: error_consistency