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