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