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

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