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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	convnext_tiny_imagenet_full_seed-0	.321
66	CLIP-RN50	.317
67	pnasnet_large_pytorch	.316
68	resnet101	.316
69	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.316
70	deit3_base_patch16_224_fb_in22k_ft_in1k	.315
71	focalnet_tiny_lrf_in1k	.313
72	inception_v3-tf	.311
73	ViT_B_32_imagenet1k	.309
74	resnet152_imagenet_full	.309
75	cvt_cvt-21-224-in1k_4_LucyV4	.309
76	inception_v3_pytorch	.300
77	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.299
78	convnext_tiny:in12k_ft_in1k	.297
79	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.294
80	resnet50_st_wzc_seed7_2_323	.294
81	resnet50_ewconplus_wzc	.293
82	xception	.292
83	resnet-50_v2-tf	.292
84	convnext_base_fb_in1k	.289
85	resnet50_ewconplus_wzc_seed2_2__323	.289
86	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.284
87	convnext_large_imagenet_full_seed-0	.284
88	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.282
89	convnext_base_imagenet_full_seed-0	.278
90	resnet50-SIN_IN	.272
91	resnet_101_v1	.271
92	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.271
93	nasnet_large-tf	.266
94	resnet-152_v2_pytorch	.260
95	resnet50_st_wzc_seed3_2_323	.257
96	resnet50_ewconplus_wzc_seed6_2_323	.256
97	resnet_152_v1	.255
98	inception_v4	.254
99	resnet50_byol	.254
100	AlexNet_SIN_fov4	.253
101	antialias-resnet152	.253
102	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.250
103	resnet50_imagenet_full	.244
104	resnet101_imagenet_full	.242
105	resnet50_hvs_wzc	.241
106	resnet50_st_wzc_2	.241
107	antialiased-rnext101_32x8d	.240
108	AdvProp_efficientnet-b7	.238
109	resnet_50_v1_spiking	.237
110	densenet-201	.236
111	densenet_201_pytorch	.236
112	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.235
113	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.235
114	custom_model_cv_18_dagger_408	.235
115	cvt_cvt-13-224-in1k_4_LucyV4	.233
116	resnet50	.233
117	densenet-201-keras	.233
118	resnet34_imagenet_full	.233
119	AT_efficientnet-b2	.232
120	AdvProp_efficientnet-b2	.232
121	resnet50-moclr8deg	.229
122	tv_efficientnet-b1	.229
123	efficientnet-b7	.228
124	resnet50-SIN_IN_IN	.228
125	resnet_SIN_IN_FT_IN	.228
126	resnet50_tutorial	.226
127	resnet_50_v1	.226
128	resnet-50-pytorch	.226
129	resnet50-sup	.226
130	resnet50_random_l2_perturb	.220
131	AdvProp_efficientnet-b6	.219
132	Res2Net50_26w_4s	.218
133	densenet-121-keras	.213
134	AdvProp_efficientnet-b4	.213
135	AT_efficientnet-b4	.213
136	densenet-121	.212
137	mobilenet_v2_0_5_224	.211
138	mobilenet_v2_1-4_224_pytorch	.210
139	mobilenet_v2_1_4_224	.210
140	inception_v4-tf	.210
141	alexnet_fov4	.208
142	grcnn_109	.208
143	convnext_small_imagenet_full_seed-0	.206
144	convnext_base_fb_in1k_gram_aligned_dinov3	.204
145	voneresnet-50-non_stochastic	.204
146	ReAlnet04	.204
147	texture_shape_resnet50_trained_on_SIN	.202
148	resnet-152_v1-tf	.196
149	eBarlow_lmda_02_1000ep	.195
150	resnet50_moco_v2	.191
151	cornet_s	.191
152	nasnet_mobile	.190
153	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.189
154	mobilenet_v2_1.4_224-tf	.189
155	resnet-101_v1-tf	.189
156	BiT-S-R152x4	.189
157	vgg_16	.188
158	ReAlnet03	.187
159	resnet18_imagenet_full	.186
160	ReAlnet09	.184
161	antialiased-r50	.182
162	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.181
163	ReAlnet01	.181
164	deit_large_imagenet_full_seed-0	.181
165	resnet-50_v1-tf	.180
166	ReAlnet10	.179
167	grcnn	.179
168	resnet50_random_linf8_perturb	.177
169	BiT-S-R101x1	.177
170	blt_vs	.177
171	CORnet-S	.175
172	resnet50_linf_4_robust	.175
173	ReAlnet08	.173
174	resnet50_linf_8_robust	.173
175	BiT-S-R50x1	.173
176	mobilenet_v2_1_0_224	.171
177	inception_v1	.167
178	densenet-169	.166
179	ReAlnet06	.166
180	mobilenet_v1_0.75_224	.165
181	texture_shape_alexnet_trained_on_SIN	.164
182	mobilenet_v2_1_0_160	.164
183	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.163
184	BiT-S-R50x3	.163
185	resnet-18	.163
186	resnet50-vitoimagevidnet8	.162
187	voneresnet-50-robust	.162
188	resnet50_eMMCR_Vanilla	.160
189	inception_v2-tf	.160
190	deit_base_imagenet_full_seed-0	.160
191	AdvProp_efficientnet-b8	.159
192	ReAlnet05	.157
193	regnet_y_400mf	.157
194	mobilenet_v1_0.5_192	.157
195	vonealexnet_gaussian_noise_std4_fixed	.157
196	BiT-S-R152x2	.156
197	mobilenet_v1_1.0_224	.156
198	resnet50_l2_3_robust	.155
199	alexnet_reduced_aliasing_early_checkpoint	.155
200	inception_v1-tf	.154
201	alexnet_training_seed_03	.153
202	mobilenet_v2_1.0_224-tf	.153
203	ReAlnet07	.153
204	mobilenet_v2_1_3_224	.153
205	alexnet_random_linf8_perturb	.153
206	ReAlnet02	.153
207	mobilenet_v2_0.5_224-tf	.153
208	vonegrcnn_52e_full	.153
209	mobilenet_v2_1.3_224-tf	.152
210	resnet50_simclr	.152
211	densenet-169-keras	.149
212	mobilenet_v1_1.0_192	.147
213	vgg-16-keras	.146
214	0.5x_resnet-18_LC_w_sh_1_iter	.146
215	squeezenet1_1	.146
216	efficientnet_b1_imagenet_full	.145
217	vonegrcnn_47e	.144
218	resnet50_robust_l2_eps1	.144
219	resnet50-SIN	.144
220	efficientnet_b2_imagenet_full	.144
221	BiT-S-R101x3	.144
222	vgg_19	.142
223	alexnet_random_l2_3_perturb	.142
224	mobilenet_v2_0.5_96	.141
225	mobilenet_v1_1.0_128	.140
226	mobilenet_v2_1.0_160-tf	.137
227	vonegrcnn_62e_nobn	.137
228	mobilenet_v1_0.75_192	.136
229	resnet-34	.136
230	mobilenet_v2_1_0_128	.135
231	AdvProp_efficientnet-b0	.135
232	alexnet_early_checkpoint	.135
233	efficientnet_b0_imagenet_full	.134
234	mobilenet_v2_0.35_128	.134
235	convnext_small_imagenet_100_seed-0	.133
236	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.133
237	mobilenet_v2_0.35_96	.133
238	konkle_alexnetgn_ipcl_ref01_primary_model	.132
239	mobilenet_v2_1.0_128-tf	.132
240	mobilenet_v1_0.25_224	.132
241	mobilenet_v2_0.75_224-tf	.132
242	AlexNet_SIN	.132
243	vonegrcnn_52e_full	.132
244	resnet-50-robust	.131
245	imagenet_l2_3_0	.131
246	resnet50_robust_l2_eps3	.131
247	mobilenet_v2_1_0_192	.130
248	alexnet_ks_torevert	.128
249	alexnet	.128
250	mobilenet_v2_0.5_192-tf	.128
251	resnet50_imagenet_100_seed-0	.127
252	vgg-19-keras	.127
253	vonegrcnn_62e_nobn	.127
254	mobilenet_v2_0_75_224	.125
255	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.125
256	mobilenet_v2_1.0_192-tf	.125
257	shufflenet_v2_x1_0	.123
258	mobilenet_v1_0.75_128	.122
259	voneresnet-50	.121
260	vonegrcnn_47e	.121
261	mobilenet_v2_0.75_96	.119
262	mobilenet_v2_0.5_160	.117
263	mobilenet_v1_1.0_160	.114
264	mobilenet_v1_0.25_192	.112
265	convnext_tiny_sup	.112
266	alexnet_l2_3_robust	.110
267	squeezenet1_0	.110
268	mobilenet_v2_0.5_128	.109
269	mobilenet_v2_0_75_192	.107
270	mobilenet_v2_0_5_192	.106
271	AlexNet_SIN_fov12	.104
272	mobilenet_v2_0.35_160	.102
273	mobilenet_v2_0.35_192	.102
274	mobilenet_v1_0.5_128	.102
275	mobilenet_v2_0.75_160-tf	.101
276	nasnet_mobile-tf	.101
277	mobilenet_v2_0_75_160	.098
278	mobilenet_v1_0.25_160	.098
279	mobilenet_v1_0.75_160	.097
280	deit_small_imagenet_full_seed-0	.096
281	mobilenet_v2_0.75_128	.096
282	convnext_small_imagenet_1_seed-0	.096
283	mobilenet_v2_0.35_224	.093
284	convnext_small_imagenet_10_seed-0	.091
285	deit_small_imagenet_10_seed-0	.090
286	mobilenet_v2_0.75_192-tf	.090
287	alexnet_fov12	.088
288	deit_small_imagenet_1_seed-0	.087
289	AlexNet_SIN_fov16	.083
290	mobilenet_v1_0.5_224	.083
291	resnet_50_v1_spiking_l4a	.081
292	mobilevit_small	.079
293	mobilenet_v2_1.0_96	.078
294	alexnet_training_seed_02	.068
295	alexnet_training_seed_04	.068
296	alexnet_training_seed_08	.068
297	alexnet_training_seed_10	.068
298	alexnet_training_seed_09	.067
299	alexnet_training_seed_07	.066
300	alexnet_robust_correct	.063
301	resnet_50_v1_spiking_l4	.060
302	mobilenet_v1_0.25_128	.059
303	mobilenet_v1_0.5_160	.055
304	barlow-twins-resnet50	.053
305	alexnet_training_seed_06	.050
306	resnet50-vicreg	.049
307	alexnet_fov16	.047
308	resnet50-barlow	.046
309	alexnet_linf_8_robust	.045
310	imagenet_l2_10_0	.034
311	resnet_50_v1_spiking_l3	.034
312	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.033
313	resnet50_FractalDB	.032
314	resnet50-vicregl0p9	.032
315	resnet-50_untrained	.031
316	deit_small_imagenet_100_seed-0	.028
317	artResNet18_1	.013
318	resnet50_imagenet_1_seed-0	.011
319	resnet50-simclr-vissl	.011
320	resnet50-vicregl0p75	.011
321	resnet50_imagenet_10_seed-0	.003
322	alexnet_training_seed_05	.003
323	alexnet_training_seed_01	.003
324	alexnet_training_seed_01_fov4	.003
325	alexnet_training_seed_01_fov12	.003
326	alexnet_training_seed_01_fov16	.003
327	resnet-18-LC_w_sh_100_iter_conv_init_m
328	ViT-B/32
329	BiT-M-R50x3
330	BiT-M-R50x1
331	BiT-M-R152x2
332	resnet-18-LC_w_sh_1_iter_conv_init_m
333	resnet-18-LC_w_sh_1_iter_m
334	resnet-18-LC_w_sh_10_iter_m
335	yudixie_resnet50_random_0_240908
336	resnet-18-LC_w_sh_100_iter_m
337	resnet-18-LC_w_sh_10_iter_conv_init_m
338	FrankRobWobv0
339	resnet18-local_aggregation
340	0.5x_resnet-18_LC
341	CORnetZ_CIFAR10
342	resnet18-deepcluster
343	r3m_resnet18
344	resnet18-colorization
345	CORnet-Z
346	resnet18-instance_recognition
347	my-model
348	vggface
349	0.5x_resnet-18_LC_w_sh_10_iter
350	r3m_resnet50
351	r3m_resnet34
352	resnet-18-LC_m
353	r3m_resnet50_nocrop
354	resnet18-supervised
355	resnet-18-LC_1st_conv_m
356	resnet-18-LC_conv_init_m
357	resnet18-contrastive_multiview
358	0.5x_resnet-18
359	RN50
360	0.5x_resnet-18_LC_w_sh_100_iter
361	RN50
362	resnet18-relative_position
363	resnet18-autoencoder
364	resnet18-simclr
365	ViT_L_32
366	ViT_L_16_imagenet1k
367	ViT_B_32
368	resnet18-depth_prediction
369	ViT_B_16
370	r3m_resnet34_nocrop
371	resnet18-contrastive_predictive
372	omnivore_swinT
373	resnet-18-LC_untrained
374	resnet-18_untrained
375	resnet-50x2_untrained
376	dcgan
377	v1-pyr-nodown
378	unet_entire
379	CORnetZ_CIFAR10_bs32_20_04
380	pixels
381	dorinet_cornet_z
382	my-model
383	openclip
384	resnet50-cifar
385	resnet50_pretrained_with_retinal_waves
386	resnet50_ImageNet
387	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
388	yudixie_resnet50_imagenet1kpret_0_240908
389	yudixie_resnet50_imagenet1kpret_0_240312
390	yudixie_resnet50_cat_obj_class_all_latents_0_240908
391	yudixie_resnet50_object_class_0_240908
392	yudixie_resnet50_category_class_0_240908
393	resnet18_imagenet21kP
394	ConvLSTM
395	PredRNN
396	yudixie_resnet50_distance_reg_0_240908
397	SimVP
398	briaai_rmbg_1_4
399	TAU
400	MIM
401	hmax
402	ReAlnet01_cornet
403	yolos_tiny
404	yudixie_resnet18_translation_reg_0_240719
405	ReAlnet02_cornet
406	ReAlnet03_cornet
407	yudixie_resnet18_distance_reg_0_240719
408	yudixie_resnet18_rotation_reg_0_240719
409	yudixie_resnet18_imagenet1kpret_0_240719
410	compact_resnet50_V4
411	vit_base_patch14_dinov2-lvd142m
412	vit_base_patch16_dinov3-lvd1689m
413	vit_base_patch14_reg4_dinov2-lvd142m
414	resnet18_ecoset_full
415	resnet34_ecoset_full
416	ReAlnet01_cornet_fov4
417	resnet50_ecoset_full
418	compact_resnet50_robust_V4
419	resnet101_ecoset_full
420	yudixie_resnet50_distance_rotation_0_240908
421	yudixie_resnet50_rotation_reg_0_240908
422	ReAlnet01_cornet_fov12
423	resnet152_ecoset_full
424	yudixie_resnet18_distance_translation_0_240719
425	ReAlnet04_cornet
426	yudixie_resnet50_distance_translation_0_240908
427	yudixie_resnet18_translation_rotation_0_240719
428	yudixie_resnet18_distance_rotation_0_240719
429	compact_vgg19_V4
430	yudixie_resnet18_category_class_0_240719
431	ReAlnet05_cornet
432	yudixie_resnet18_cat_obj_class_all_latents_0_240719
433	resnet-18_test_m
434	yudixie_resnet18_distance_translation_rotation_0_240719
435	compact_resnet50_IT
436	SeLaVi-AVE
437	ReAlnet06_cornet
438	r3d_18
439	compact_vgg19_IT
440	ReAlnet01_cornet_fov16
441	ReAlnet01_ALL
442	mc3_18
443	ReAlnet07_cornet
444	yudixie_resnet50_translation_rotation_0_240908
445	s3d
446	mvit_v1_b
447	ReAlnet08_cornet
448	ReAlnet02_ALL
449	ReAlnet03_ALL
450	aughost_fair_resnet18
451	compact_cornets_V4
452	VOneCORnet-S
453	ReAlnet04_ALL
454	ReAlnet09_cornet
455	SeLaVi-Kinetics400
456	SeLaVi-VGG-Sound
457	omnivore_swinB
458	compact_cornets_IT
459	SeLaVi-Kinetics-Sound
460	ReAlnet06_ALL
461	mvit_v2_s
462	omnivore_swinS
463	r2plus1d_18
464	compact_resnet50_robust_IT
465	ReAlnet10_cornet
466	ReAlnet07_ALL
467	yudixie_resnet18_object_class_0_240719
468	ReAlnet05_ALL
469	ReAlnet08_ALL
470	yudixie_resnet18_random_0_240719
471	ReAlnet09_ALL
472	yudixie_resnet50_translation_reg_0_240908
473	resnet50_primary_visual_cortex
474	yudixie_resnet50_distance_translation_rotation_0_240908
475	ReAlnet10_ALL
476	resnet18_fair
477	pr1_stl10_disentangled_alpha04
478	predictive_coding_pc
479	yudixie_wbh_object_class_scaling_0415_run_0
480	yudixie_wbh_object_class_scaling_0415_run_3
481	yudixie_wbh_object_class_scaling_0415_imagenet
482	yudixie_wbh_object_class_scaling_0415_run_2
483	yudixie_wbh_object_class_scaling_0415_run_1
484	deepeisnn_ei_cifar10_vgg19
485	deepeisnn_ei_cifar10_resnet18
486	deepeisnn_ei_imagenet_resnet18
487	dinov2_vitl14_reg_lc_featread

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