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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	vit_large_patch14_clip_224:laion2b_ft_in1k	.855
4	vit_large_patch14_clip_224:openai_ft_in1k	.840
5	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.804
6	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.784
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.780
8	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.752
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.748
10	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.740
11	convnext_large:fb_in22k_ft_in1k	.698
12	convnext_base:clip_laiona_augreg_ft_in1k_384	.686
13	vision_transformer_vit_large_patch16_224	.656
14	convnext_xlarge:fb_in22k_ft_in1k	.651
15	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.633
16	fixres_resnext101_32x48d_wsl	.611
17	convnext_base_fb_in22k_ft_in1k	.600
18	resnext101_32x32d_wsl	.596
19	resnext101_32x16d_wsl	.595
20	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
21	resnext101_32x48d_wsl	.560
22	vit_base_patch16_clip_224:openai_ft_in1k	.560
23	SWSL_resnext101_32x8d	.546
24	resnext101_32x8d_wsl	.540
25	deit3_base_patch16_224_fb_in1k	.519
26	CLIP_ViT-B_32	.482
27	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.477
28	resnet50-VITO-8deg-cc	.451
29	effnetv2m_custom384	.448
30	cvt_cvt-13-384-in1k_4_LucyV4	.441
31	resnet-152_v2-tf	.405
32	resnet_152_v2	.402
33	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.394
34	resnet_101_v2	.393
35	SWSL_resnet50	.390
36	resnet_50_v2	.383
37	vit_relpos_base_patch16_clsgap_224:sw_in1k	.380
38	ViT_B_16_imagenet1k	.376
39	cvt_cvt-21-384-in1k_4_LucyV4	.372
40	CLIP_resnet50	.371
41	CLIP_resnet50_float32	.371
42	pnasnet_large_halfPrecision	.357
43	effnetb1_272x240	.357
44	pnasnet_large	.355
45	resnet50_ewconplus_wzc_2_323	.349
46	resnet50_ewconplus_noseed_2_323	.346
47	efficientnet_b0	.346
48	inception_v3	.345
49	ViT_L_32_imagenet1k	.344
50	xception-keras	.343
51	resnet50_ewconplus_wzc_seed5_2__323	.339
52	nasnet_large	.332
53	cv_18_dagger_408_pretrained	.328
54	pnasnet_large-tf	.327
55	convnext_femto_ols:d1_in1k	.325
56	convnext_tiny_imagenet_full_seed-0	.321
57	CLIP-RN50	.317
58	resnet50_st_wzc_noseed_2_323	.316
59	pnasnet_large_pytorch	.316
60	resnet101	.316
61	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.316
62	deit3_base_patch16_224_fb_in22k_ft_in1k	.315
63	resnet50_ewconplus_wzc_seed4_2_323	.313
64	focalnet_tiny_lrf_in1k	.313
65	inception_v3-tf	.311
66	ViT_B_32_imagenet1k	.309
67	resnet152_imagenet_full	.309
68	cvt_cvt-21-224-in1k_4_LucyV4	.309
69	inception_v3_pytorch	.300
70	resnet50_st_wzc_seed5_2_323	.299
71	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.299
72	convnext_tiny:in12k_ft_in1k	.297
73	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.294
74	resnet50_st_wzc_seed7_2_323	.294
75	resnet50_ewconplus_wzc	.293
76	xception	.292
77	resnet-50_v2-tf	.292
78	convnext_base_fb_in1k	.289
79	resnet50_ewconplus_wzc_seed2_2__323	.289
80	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.284
81	convnext_large_imagenet_full_seed-0	.284
82	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.282
83	convnext_base_imagenet_full_seed-0	.278
84	resnet50-SIN_IN	.272
85	resnet_101_v1	.271
86	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.271
87	resnet50_ewconplus_wzc_seed3_2_323	.268
88	nasnet_large-tf	.266
89	resnet-152_v2_pytorch	.260
90	resnet50_st_wzc_seed3_2_323	.257
91	resnet50_ewconplus_wzc_seed6_2_323	.256
92	resnet_152_v1	.255
93	inception_v4	.254
94	resnet50_byol	.254
95	AlexNet_SIN_fov4	.253
96	antialias-resnet152	.253
97	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.250
98	resnet50_st_wzc_seed4_2_323	.246
99	resnet50_imagenet_full	.244
100	resnet101_imagenet_full	.242
101	resnet50_st_wzc_2	.241
102	antialiased-rnext101_32x8d	.240
103	AdvProp_efficientnet-b7	.238
104	resnet_50_v1_spiking	.237
105	densenet-201	.236
106	densenet_201_pytorch	.236
107	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.235
108	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.235
109	custom_model_cv_18_dagger_408	.235
110	cvt_cvt-13-224-in1k_4_LucyV4	.233
111	resnet50	.233
112	densenet-201-keras	.233
113	resnet34_imagenet_full	.233
114	AT_efficientnet-b2	.232
115	AdvProp_efficientnet-b2	.232
116	resnet50-moclr8deg	.229
117	tv_efficientnet-b1	.229
118	efficientnet-b7	.228
119	resnet50-SIN_IN_IN	.228
120	resnet_SIN_IN_FT_IN	.228
121	resnet50_tutorial	.226
122	resnet_50_v1	.226
123	resnet-50-pytorch	.226
124	resnet50-sup	.226
125	resnet50_random_l2_perturb	.220
126	AdvProp_efficientnet-b6	.219
127	Res2Net50_26w_4s	.218
128	densenet-121-keras	.213
129	AdvProp_efficientnet-b4	.213
130	AT_efficientnet-b4	.213
131	densenet-121	.212
132	mobilenet_v2_0_5_224	.211
133	mobilenet_v2_1-4_224_pytorch	.210
134	mobilenet_v2_1_4_224	.210
135	inception_v4-tf	.210
136	alexnet_fov4	.208
137	grcnn_109	.208
138	convnext_small_imagenet_full_seed-0	.206
139	convnext_base_fb_in1k_gram_aligned_dinov3	.204
140	voneresnet-50-non_stochastic	.204
141	ReAlnet04	.204
142	texture_shape_resnet50_trained_on_SIN	.202
143	resnet-152_v1-tf	.196
144	eBarlow_lmda_02_1000ep	.195
145	resnet50_moco_v2	.191
146	cornet_s	.191
147	nasnet_mobile	.190
148	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.189
149	mobilenet_v2_1.4_224-tf	.189
150	resnet-101_v1-tf	.189
151	BiT-S-R152x4	.189
152	vgg_16	.188
153	ReAlnet03	.187
154	resnet18_imagenet_full	.186
155	ReAlnet09	.184
156	antialiased-r50	.182
157	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.181
158	ReAlnet01	.181
159	deit_large_imagenet_full_seed-0	.181
160	resnet-50_v1-tf	.180
161	ReAlnet10	.179
162	grcnn	.179
163	resnet50_random_linf8_perturb	.177
164	BiT-S-R101x1	.177
165	blt_vs	.177
166	CORnet-S	.175
167	resnet50_linf_4_robust	.175
168	ReAlnet08	.173
169	resnet50_linf_8_robust	.173
170	BiT-S-R50x1	.173
171	mobilenet_v2_1_0_224	.171
172	inception_v1	.167
173	densenet-169	.166
174	ReAlnet06	.166
175	mobilenet_v1_0.75_224	.165
176	texture_shape_alexnet_trained_on_SIN	.164
177	mobilenet_v2_1_0_160	.164
178	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.163
179	BiT-S-R50x3	.163
180	resnet-18	.163
181	resnet50-vitoimagevidnet8	.162
182	voneresnet-50-robust	.162
183	resnet50_eMMCR_Vanilla	.160
184	inception_v2-tf	.160
185	deit_base_imagenet_full_seed-0	.160
186	AdvProp_efficientnet-b8	.159
187	ReAlnet05	.157
188	regnet_y_400mf	.157
189	mobilenet_v1_0.5_192	.157
190	vonealexnet_gaussian_noise_std4_fixed	.157
191	BiT-S-R152x2	.156
192	mobilenet_v1_1.0_224	.156
193	resnet50_l2_3_robust	.155
194	alexnet_reduced_aliasing_early_checkpoint	.155
195	inception_v1-tf	.154
196	alexnet_training_seed_03	.153
197	mobilenet_v2_1.0_224-tf	.153
198	ReAlnet07	.153
199	mobilenet_v2_1_3_224	.153
200	alexnet_random_linf8_perturb	.153
201	ReAlnet02	.153
202	mobilenet_v2_0.5_224-tf	.153
203	vonegrcnn_52e_full	.153
204	mobilenet_v2_1.3_224-tf	.152
205	resnet50_simclr	.152
206	densenet-169-keras	.149
207	mobilenet_v1_1.0_192	.147
208	vgg-16-keras	.146
209	0.5x_resnet-18_LC_w_sh_1_iter	.146
210	squeezenet1_1	.146
211	efficientnet_b1_imagenet_full	.145
212	vonegrcnn_47e	.144
213	resnet50_robust_l2_eps1	.144
214	resnet50-SIN	.144
215	efficientnet_b2_imagenet_full	.144
216	BiT-S-R101x3	.144
217	vgg_19	.142
218	alexnet_random_l2_3_perturb	.142
219	mobilenet_v2_0.5_96	.141
220	mobilenet_v1_1.0_128	.140
221	mobilenet_v2_1.0_160-tf	.137
222	vonegrcnn_62e_nobn	.137
223	mobilenet_v1_0.75_192	.136
224	resnet-34	.136
225	mobilenet_v2_1_0_128	.135
226	AdvProp_efficientnet-b0	.135
227	alexnet_early_checkpoint	.135
228	efficientnet_b0_imagenet_full	.134
229	mobilenet_v2_0.35_128	.134
230	convnext_small_imagenet_100_seed-0	.133
231	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.133
232	mobilenet_v2_0.35_96	.133
233	konkle_alexnetgn_ipcl_ref01_primary_model	.132
234	mobilenet_v2_1.0_128-tf	.132
235	mobilenet_v1_0.25_224	.132
236	mobilenet_v2_0.75_224-tf	.132
237	AlexNet_SIN	.132
238	vonegrcnn_52e_full	.132
239	resnet-50-robust	.131
240	imagenet_l2_3_0	.131
241	resnet50_robust_l2_eps3	.131
242	mobilenet_v2_1_0_192	.130
243	alexnet_ks_torevert	.128
244	alexnet	.128
245	mobilenet_v2_0.5_192-tf	.128
246	resnet50_imagenet_100_seed-0	.127
247	vgg-19-keras	.127
248	vonegrcnn_62e_nobn	.127
249	mobilenet_v2_0_75_224	.125
250	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.125
251	mobilenet_v2_1.0_192-tf	.125
252	shufflenet_v2_x1_0	.123
253	mobilenet_v1_0.75_128	.122
254	voneresnet-50	.121
255	vonegrcnn_47e	.121
256	mobilenet_v2_0.75_96	.119
257	mobilenet_v2_0.5_160	.117
258	mobilenet_v1_1.0_160	.114
259	mobilenet_v1_0.25_192	.112
260	convnext_tiny_sup	.112
261	alexnet_l2_3_robust	.110
262	squeezenet1_0	.110
263	mobilenet_v2_0.5_128	.109
264	mobilenet_v2_0_75_192	.107
265	mobilenet_v2_0_5_192	.106
266	AlexNet_SIN_fov12	.104
267	mobilenet_v2_0.35_160	.102
268	mobilenet_v2_0.35_192	.102
269	mobilenet_v1_0.5_128	.102
270	mobilenet_v2_0.75_160-tf	.101
271	nasnet_mobile-tf	.101
272	mobilenet_v2_0_75_160	.098
273	mobilenet_v1_0.25_160	.098
274	mobilenet_v1_0.75_160	.097
275	deit_small_imagenet_full_seed-0	.096
276	mobilenet_v2_0.75_128	.096
277	convnext_small_imagenet_1_seed-0	.096
278	mobilenet_v2_0.35_224	.093
279	convnext_small_imagenet_10_seed-0	.091
280	deit_small_imagenet_10_seed-0	.090
281	mobilenet_v2_0.75_192-tf	.090
282	alexnet_fov12	.088
283	deit_small_imagenet_1_seed-0	.087
284	AlexNet_SIN_fov16	.083
285	mobilenet_v1_0.5_224	.083
286	resnet_50_v1_spiking_l4a	.081
287	mobilevit_small	.079
288	mobilenet_v2_1.0_96	.078
289	alexnet_training_seed_02	.068
290	alexnet_training_seed_04	.068
291	alexnet_training_seed_08	.068
292	alexnet_training_seed_10	.068
293	alexnet_training_seed_09	.067
294	alexnet_training_seed_07	.066
295	resnet50-meshes-lt-100-original-pretrained	.066
296	alexnet_robust_correct	.063
297	resnet_50_v1_spiking_l4	.060
298	mobilenet_v1_0.25_128	.059
299	mobilenet_v1_0.5_160	.055
300	barlow-twins-resnet50	.053
301	resnet50-meshes-lt-100-original-scratch	.052
302	alexnet_training_seed_06	.050
303	resnet50-vicreg	.049
304	alexnet_fov16	.047
305	resnet50-barlow	.046
306	alexnet_linf_8_robust	.045
307	imagenet_l2_10_0	.034
308	resnet_50_v1_spiking_l3	.034
309	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.033
310	resnet50_FractalDB	.032
311	resnet50-vicregl0p9	.032
312	resnet-50_untrained	.031
313	deit_small_imagenet_100_seed-0	.028
314	artResNet18_1	.013
315	resnet50_imagenet_1_seed-0	.011
316	resnet50-simclr-vissl	.011
317	resnet50-vicregl0p75	.011
318	resnet50_imagenet_10_seed-0	.003
319	alexnet_training_seed_05	.003
320	alexnet_training_seed_01	.003
321	alexnet_training_seed_01_fov4	.003
322	alexnet_training_seed_01_fov12	.003
323	alexnet_training_seed_01_fov16	.003
324	resnet-18-LC_w_sh_100_iter_conv_init_m
325	ViT-B/32
326	BiT-M-R50x3
327	BiT-M-R50x1
328	BiT-M-R152x2
329	resnet-18-LC_w_sh_1_iter_conv_init_m
330	resnet-18-LC_w_sh_1_iter_m
331	resnet-18-LC_w_sh_10_iter_m
332	yudixie_resnet50_random_0_240908
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	FrankRobWobv0
336	resnet18-local_aggregation
337	0.5x_resnet-18_LC
338	CORnetZ_CIFAR10
339	resnet18-deepcluster
340	r3m_resnet18
341	resnet18-colorization
342	CORnet-Z
343	resnet18-instance_recognition
344	my-model
345	vggface
346	0.5x_resnet-18_LC_w_sh_10_iter
347	r3m_resnet50
348	r3m_resnet34
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_L_16_imagenet1k
364	ViT_B_32
365	resnet18-depth_prediction
366	ViT_B_16
367	r3m_resnet34_nocrop
368	resnet18-contrastive_predictive
369	omnivore_swinT
370	resnet-18-LC_untrained
371	resnet-18_untrained
372	resnet-50x2_untrained
373	dcgan
374	v1-pyr-nodown
375	unet_entire
376	CORnetZ_CIFAR10_bs32_20_04
377	pixels
378	dorinet_cornet_z
379	my-model
380	resnet50
381	openclip
382	resnet50-cifar
383	resnet50_pretrained_with_retinal_waves
384	resnet50_ImageNet
385	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
386	yudixie_resnet50_imagenet1kpret_0_240908
387	yudixie_resnet50_imagenet1kpret_0_240312
388	yudixie_resnet50_cat_obj_class_all_latents_0_240908
389	yudixie_resnet50_object_class_0_240908
390	yudixie_resnet50_category_class_0_240908
391	resnet18_imagenet21kP
392	ConvLSTM
393	PredRNN
394	yudixie_resnet50_distance_reg_0_240908
395	SimVP
396	briaai_rmbg_1_4
397	TAU
398	MIM
399	hmax
400	ReAlnet01_cornet
401	yolos_tiny
402	yudixie_resnet18_translation_reg_0_240719
403	ReAlnet02_cornet
404	ReAlnet03_cornet
405	yudixie_resnet18_distance_reg_0_240719
406	yudixie_resnet18_rotation_reg_0_240719
407	yudixie_resnet18_imagenet1kpret_0_240719
408	compact_resnet50_V4
409	vit_base_patch14_dinov2-lvd142m
410	vit_base_patch16_dinov3-lvd1689m
411	vit_base_patch14_reg4_dinov2-lvd142m
412	resnet18_ecoset_full
413	resnet34_ecoset_full
414	ReAlnet01_cornet_fov4
415	resnet50_ecoset_full
416	compact_resnet50_robust_V4
417	resnet101_ecoset_full
418	yudixie_resnet50_distance_rotation_0_240908
419	yudixie_resnet50_rotation_reg_0_240908
420	ReAlnet01_cornet_fov12
421	resnet152_ecoset_full
422	yudixie_resnet18_distance_translation_0_240719
423	ReAlnet04_cornet
424	yudixie_resnet50_distance_translation_0_240908
425	yudixie_resnet18_translation_rotation_0_240719
426	yudixie_resnet18_distance_rotation_0_240719
427	compact_vgg19_V4
428	yudixie_resnet18_category_class_0_240719
429	ReAlnet05_cornet
430	yudixie_resnet18_cat_obj_class_all_latents_0_240719
431	resnet-18_test_m
432	yudixie_resnet18_distance_translation_rotation_0_240719
433	compact_resnet50_IT
434	SeLaVi-AVE
435	ReAlnet06_cornet
436	r3d_18
437	compact_vgg19_IT
438	ReAlnet01_cornet_fov16
439	ReAlnet01_ALL
440	mc3_18
441	ReAlnet07_cornet
442	yudixie_resnet50_translation_rotation_0_240908
443	s3d
444	mvit_v1_b
445	ReAlnet08_cornet
446	ReAlnet02_ALL
447	ReAlnet03_ALL
448	aughost_fair_resnet18
449	compact_cornets_V4
450	VOneCORnet-S
451	ReAlnet04_ALL
452	ReAlnet09_cornet
453	SeLaVi-Kinetics400
454	SeLaVi-VGG-Sound
455	omnivore_swinB
456	compact_cornets_IT
457	SeLaVi-Kinetics-Sound
458	ReAlnet06_ALL
459	mvit_v2_s
460	omnivore_swinS
461	r2plus1d_18
462	compact_resnet50_robust_IT
463	ReAlnet10_cornet
464	ReAlnet07_ALL
465	yudixie_resnet18_object_class_0_240719
466	ReAlnet05_ALL
467	ReAlnet08_ALL
468	yudixie_resnet18_random_0_240719
469	ReAlnet09_ALL
470	yudixie_resnet50_translation_reg_0_240908
471	resnet50_primary_visual_cortex
472	yudixie_resnet50_distance_translation_rotation_0_240908
473	ReAlnet10_ALL
474	resnet18_fair
475	pr1_stl10_disentangled_alpha04
476	predictive_coding_pc
477	yudixie_wbh_object_class_scaling_0415_run_0
478	yudixie_wbh_object_class_scaling_0415_run_3
479	yudixie_wbh_object_class_scaling_0415_imagenet
480	yudixie_wbh_object_class_scaling_0415_run_2
481	yudixie_wbh_object_class_scaling_0415_run_1
482	deepeisnn_ei_cifar10_vgg19
483	deepeisnn_ei_cifar10_resnet18
484	deepeisnn_ei_imagenet_resnet18

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