Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021contrast-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnext101_32x16d_wsl	.771
2	effnetv2m_custom384	.731
3	CLIP_resnet50_float32	.718
4	CLIP_resnet50	.718
5	resnet-152_v2-tf	.709
6	CLIP_ViT-B_32	.708
7	SWSL_resnet50	.696
8	nasnet_large-tf	.688
9	ViT_B_16_imagenet1k	.658
10	resnet_50_v2	.647
11	ViT_L_32_imagenet1k	.635
12	effnetb1_272x240	.632
13	CLIP-RN50	.631
14	inception_v4	.623
15	resnext101_32x8d_wsl	.621
16	ViT_L_16_imagenet1k	.617
17	pnasnet_large-tf	.617
18	inception_v4-tf	.606
19	nasnet_large	.598
20	vision_transformer_vit_large_patch16_224	.590
21	resnext101_32x48d_wsl	.581
22	resnext101_32x32d_wsl	.577
23	xception-keras	.574
24	vit_large_patch14_clip_224:laion2b_ft_in1k	.568
25	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.565
26	resnet-50_v2-tf	.563
27	resnet_101_v2	.562
28	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.555
29	pnasnet_large_pytorch	.554
30	SWSL_resnext101_32x8d	.547
31	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.544
32	pnasnet_large	.544
33	xception	.540
34	ViT_B_32_imagenet1k	.533
35	inception_v3-tf	.511
36	cvt_cvt-13-224-in1k_4_LucyV4	.507
37	focalnet_tiny_lrf_in1k	.506
38	convnext_femto_ols:d1_in1k	.503
39	convnext_tiny:in12k_ft_in1k	.503
40	resnet-152_v2_pytorch	.496
41	tv_efficientnet-b1	.492
42	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.481
43	convnext_tiny_sup	.478
44	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.476
45	cvt_cvt-21-224-in1k_4_LucyV4	.460
46	convnext_small_imagenet_100_seed-0	.456
47	convnext_small_imagenet_full_seed-0	.452
48	cvt_cvt-21-384-in1k_4_LucyV4	.445
49	convnext_tiny_imagenet_full_seed-0	.433
50	cvt_cvt-13-384-in1k_4_LucyV4	.431
51	resnet50-VITO-8deg-cc	.430
52	vit_relpos_base_patch16_clsgap_224:sw_in1k	.429
53	vit_base_patch16_clip_224:openai_ft_in1k	.418
54	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
55	vit_large_patch14_clip_336:laion2b_ft_in1k	.413
56	resnet50-moclr8deg	.403
57	resnet50_moco_v2	.400
58	resnet101	.399
59	BiT-S-R50x3	.397
60	resnet_152_v2	.376
61	antialias-resnet152	.370
62	resnet50_byol	.367
63	inception_v3	.351
64	convnext_base:clip_laiona_augreg_ft_in1k_384	.349
65	antialiased-rnext101_32x8d	.347
66	convnext_base_imagenet_full_seed-0	.347
67	resnet50	.345
68	fixres_resnext101_32x48d_wsl	.344
69	resnet152_imagenet_full	.340
70	resnet50_imagenet_full	.335
71	cv_18_dagger_408_pretrained	.332
72	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.329
73	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.328
74	densenet-169-keras	.324
75	convnext_large_imagenet_full_seed-0	.322
76	densenet-121-keras	.317
77	deit_base_imagenet_full_seed-0	.309
78	antialiased-r50	.308
79	deit_small_imagenet_full_seed-0	.308
80	densenet-201-keras	.302
81	resnet_101_v1	.301
82	BiT-S-R152x4	.294
83	blt_vs	.283
84	resnet34_imagenet_full	.276
85	resnet50_random_l2_perturb	.274
86	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.271
87	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.269
88	inception_v2-tf	.265
89	BiT-S-R101x3	.261
90	resnet50_simclr	.260
91	densenet-169	.254
92	densenet_201_pytorch	.253
93	densenet-201	.253
94	densenet-121	.250
95	resnet_152_v1	.246
96	inception_v1-tf	.242
97	vit_large_patch14_clip_224:openai_ft_in1k	.241
98	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.240
99	nasnet_mobile	.237
100	resnet50-SIN_IN_IN	.229
101	resnet_SIN_IN_FT_IN	.229
102	Res2Net50_26w_4s	.228
103	resnet50-sup	.221
104	resnet-50-pytorch	.221
105	resnet50_tutorial	.221
106	resnet_50_v1	.221
107	grcnn_109	.220
108	efficientnet_b0	.219
109	resnet-101_v1-tf	.216
110	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.215
111	deit_large_imagenet_full_seed-0	.206
112	convnext_large:fb_in22k_ft_in1k	.204
113	AT_efficientnet-b2	.200
114	AdvProp_efficientnet-b2	.200
115	resnet50-SIN_IN	.193
116	BiT-S-R152x2	.191
117	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.186
118	grcnn	.184
119	alexnet	.182
120	resnet-50_v1-tf	.182
121	resnet-152_v1-tf	.182
122	BiT-S-R101x1	.181
123	AdvProp_efficientnet-b6	.179
124	vgg_19	.178
125	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.176
126	mobilenet_v2_0_5_224	.176
127	resnet101_imagenet_full	.171
128	vonegrcnn_52e_full	.171
129	efficientnet-b7	.166
130	efficientnet_b2_imagenet_full	.166
131	mobilenet_v2_1_4_224	.166
132	mobilenet_v2_1-4_224_pytorch	.166
133	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
134	BiT-S-R50x1	.158
135	nasnet_mobile-tf	.158
136	alexnet_training_seed_04	.156
137	alexnet_training_seed_02	.156
138	alexnet_training_seed_10	.156
139	vgg-16-keras	.155
140	alexnet_random_linf8_perturb	.155
141	AT_efficientnet-b4	.155
142	AdvProp_efficientnet-b4	.155
143	ReAlnet05	.154
144	mobilenet_v2_0.5_224-tf	.152
145	mobilenet_v1_0.25_128	.152
146	vonegrcnn_47e	.151
147	alexnet_random_l2_3_perturb	.148
148	ReAlnet02	.147
149	resnet50-vitoimagevidnet8	.147
150	resnet-18	.147
151	convnext_xlarge:fb_in22k_ft_in1k	.145
152	ReAlnet04	.144
153	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.143
154	mobilenet_v2_1_0_224	.140
155	voneresnet-50-non_stochastic	.140
156	AdvProp_efficientnet-b7	.139
157	cornet_s	.139
158	ReAlnet08	.135
159	ReAlnet03	.133
160	alexnet_reduced_aliasing_early_checkpoint	.131
161	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.131
162	vonegrcnn_52e_full	.125
163	alexnet_ks_torevert	.123
164	alexnet	.123
165	alexnet	.123
166	alexnet	.123
167	alexnet-baseline	.123
168	alexnet	.123
169	alexnet	.123
170	alexnet	.123
171	alexnet	.123
172	alexnet	.123
173	alexnet	.123
174	texture_shape_resnet50_trained_on_SIN	.122
175	resnet50_l2_3_robust	.121
176	vgg_16	.121
177	resnet50-meshes-lt-100-original-scratch	.120
178	mobilenet_v1_0.25_192	.120
179	vonegrcnn_62e_nobn	.120
180	mobilenet_v2_0.5_128	.119
181	voneresnet-50-robust	.119
182	resnet50_linf_4_robust	.119
183	mobilevit_small	.118
184	alexnet_robust_correct	.117
185	resnet50_linf_8_robust	.116
186	alexnet_l2_3_robust	.115
187	vgg-19-keras	.114
188	vonegrcnn_62e_nobn	.113
189	resnet50_robust_l2_eps3	.113
190	imagenet_l2_3_0	.113
191	resnet-50-robust	.113
192	resnet50_robust_l2_eps1	.113
193	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
194	alexnet_linf_8_robust	.109
195	imagenet_l2_10_0	.108
196	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
197	ReAlnet09	.107
198	mobilenet_v2_1_0_128	.107
199	ReAlnet07	.107
200	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
201	deit_small_imagenet_10_seed-0	.106
202	deit_small_imagenet_1_seed-0	.106
203	resnet50_imagenet_10_seed-0	.106
204	mobilenet_v2_0_5_192	.105
205	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
206	ReAlnet06	.102
207	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
208	vonealexnet_gaussian_noise_std4_fixed	.101
209	alexnet_training_seed_05	.101
210	alexnet_training_seed_01	.101
211	barlow-twins-resnet50	.099
212	ReAlnet10	.097
213	vonegrcnn_47e	.096
214	konkle_alexnetgn_ipcl_ref01_primary_model	.094
215	resnet50-vicreg	.094
216	mobilenet_v1_0.5_192	.094
217	mobilenet_v2_1.0_96	.093
218	mobilenet_v2_0.35_128	.091
219	mobilenet_v2_1_0_160	.088
220	mobilenet_v2_0.35_192	.088
221	resnet18_imagenet_full	.087
222	resnet50-SIN	.085
223	mobilenet_v1_0.5_128	.083
224	mobilenet_v1_1.0_224	.081
225	mobilenet_v2_0.35_160	.081
226	squeezenet1_0	.081
227	mobilenet_v2_1.0_128-tf	.080
228	AdvProp_efficientnet-b8	.079
229	alexnet_early_checkpoint	.075
230	inception_v1	.074
231	resnet50-vicregl0p9	.072
232	mobilenet_v1_0.5_160	.072
233	mobilenet_v2_1_0_192	.071
234	mobilenet_v2_1.0_160-tf	.070
235	resnet50_FractalDB	.070
236	ReAlnet01	.070
237	ReAlnet_incorrect	.070
238	mobilenet_v2_1.0_224-tf	.070
239	efficientnet_b0_imagenet_full	.069
240	voneresnet-50	.069
241	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.064
242	alexnet_training_seed_09	.058
243	mobilenet_v2_1.0_192-tf	.056
244	alexnet_training_seed_07	.054
245	CORnet-S	.052
246	mobilenet_v2_0.5_192-tf	.052
247	mobilenet_v2_1.4_224-tf	.050
248	mobilenet_v1_0.75_160	.050
249	mobilenet_v2_0.5_160	.049
250	resnet50_imagenet_1_seed-0	.048
251	resnet-34	.047
252	mobilenet_v1_1.0_160	.046
253	mobilenet_v1_1.0_192	.045
254	resnet50-vicregl0p75	.044
255	efficientnet_b1_imagenet_full	.044
256	AdvProp_efficientnet-b0	.044
257	mobilenet_v2_0_75_192	.044
258	custom_model_cv_18_dagger_408	.044
259	mobilenet_v2_0.35_224	.043
260	mobilenet_v1_0.5_224	.043
261	resnet50-barlow	.037
262	resnet50_imagenet_100_seed-0	.035
263	mobilenet_v2_0_75_160	.033
264	mobilenet_v1_0.75_128	.033
265	mobilenet_v2_0.75_128	.032
266	my-model	.030
267	mobilenet_v2_1_3_224	.025
268	convnext_small_imagenet_10_seed-0	.025
269	mobilenet_v1_0.75_192	.024
270	mobilenet_v2_0_75_224	.024
271	AlexNet_SIN	.022
272	alexnet_training_seed_03	.022
273	mobilenet_v1_0.25_224	.022
274	deit_small_imagenet_100_seed-0	.021
275	mobilenet_v2_0.75_96	.020
276	squeezenet1_1	.020
277	convnext_small_imagenet_1_seed-0	.020
278	alexnet_training_seed_08	.020
279	mobilenet_v2_1.3_224-tf	.019
280	mobilenet_v2_0.75_160-tf	.018
281	artResNet18_1	.018
282	resnet50_random_linf8_perturb	.017
283	mobilenet_v2_0.5_96	.017
284	mobilenet_v1_1.0_128	.016
285	mobilenet_v1_0.25_160	.016
286	alexnet_training_seed_06	.016
287	texture_shape_alexnet_trained_on_SIN	.015
288	mobilenet_v2_0.35_96	.015
289	mobilenet_v2_0.75_224-tf	.014
290	mobilenet_v2_0.75_192-tf	.013
291	regnet_y_400mf	.013
292	resnet-50_untrained	.012
293	resnet50-meshes-lt-100-original-pretrained	.012
294	mobilenet_v1_0.75_224	.011
295	resnet50-simclr-vissl	.008
296	shufflenet_v2_x1_0	.006
297	0.5x_resnet-18_LC_w_sh_1_iter	.006
298	PredRNN	X
299	ConvLSTM	X
300	0.5x_resnet-18_LC_w_sh_10_iter	X
301	vggface	X
302	ReAlnet01_cornet	X
303	my-model	X
304	inception_v3_pytorch	X
305	resnet18_imagenet21kP	X
306	r3m_resnet50	X
307	r3m_resnet34	X
308	yudixie_resnet50_category_class_0_240908	X
309	resnet18-instance_recognition	X
310	resnet-18-LC_m	X
311	resnet152_ecoset_full	X
312	r3m_resnet50_nocrop	X
313	resnet18-supervised	X
314	resnet-18-LC_1st_conv_m	X
315	resnet-18-LC_conv_init_m	X
316	CORnet-Z	X
317	yudixie_resnet50_imagenet1kpret_0_240312	X
318	yudixie_resnet18_translation_reg_0_240719	X
319	yudixie_resnet18_distance_reg_0_240719	X
320	resnet18-contrastive_multiview	X
321	ReAlnet03_cornet	X
322	yudixie_resnet18_rotation_reg_0_240719	X
323	yudixie_resnet18_imagenet1kpret_0_240719	X
324	r3m_resnet18	X
325	resnet18-colorization	X
326	0.5x_resnet-18	X
327	RN50	X
328	resnet18-deepcluster	X
329	CORnetZ_CIFAR10	X
330	ReAlnet02_cornet	X
331	yudixie_resnet50_object_class_0_240908	X
332	0.5x_resnet-18_LC	X
333	resnet18_ecoset_full	X
334	resnet18-local_aggregation	X
335	resnet34_ecoset_full	X
336	VOneCORnet-S	X
337	0.5x_resnet-18_LC_w_sh_100_iter	X
338	resnet50_ecoset_full	X
339	RN50	X
340	resnet101_ecoset_full	X
341	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
342	resnet-18-LC_w_sh_100_iter_conv_init_m	X
343	resnet18-relative_position	X
344	yudixie_resnet50_distance_translation_0_240908	X
345	yudixie_resnet18_distance_translation_0_240719	X
346	ReAlnet04_cornet	X
347	yudixie_resnet18_translation_rotation_0_240719	X
348	yudixie_resnet18_distance_rotation_0_240719	X
349	yudixie_resnet18_category_class_0_240719	X
350	yudixie_resnet50_distance_rotation_0_240908	X
351	ReAlnet05_cornet	X
352	yudixie_resnet50_rotation_reg_0_240908	X
353	yudixie_resnet18_random_0_240719	X
354	yudixie_resnet50_imagenet1kpret_0_240908	X
355	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
356	resnet-18_test_m	X
357	yudixie_resnet18_distance_translation_rotation_0_240719	X
358	yudixie_resnet18_object_class_0_240719	X
359	FrankRobWobv0	X
360	resnet18-autoencoder	X
361	yudixie_resnet50_translation_reg_0_240908	X
362	ReAlnet06_cornet	X
363	yudixie_resnet50_translation_rotation_0_240908	X
364	resnet-18-LC_w_sh_10_iter_conv_init_m	X
365	resnet-18-LC_w_sh_100_iter_m	X
366	ReAlnet07_cornet	X
367	resnet18-simclr	X
368	yudixie_resnet50_random_0_240908	X
369	ViT_L_32	X
370	resnet-18-LC_w_sh_10_iter_m	X
371	ReAlnet08_cornet	X
372	ViT_B_32	X
373	resnet-18-LC_w_sh_1_iter_m	X
374	omnivore_swinB	X
375	resnet18-depth_prediction	X
376	ViT_B_16	X
377	ReAlnet09_cornet	X
378	r3m_resnet34_nocrop	X
379	hmax	X
380	resnet18-contrastive_predictive	X
381	omnivore_swinT	X
382	resnet-18-LC_untrained	X
383	resnet-18_untrained	X
384	omnivore_swinS	X
385	resnet-18-LC_w_sh_1_iter_conv_init_m	X
386	resnet-50x2_untrained	X
387	dcgan	X
388	BiT-M-R152x2	X
389	v1-pyr-nodown	X
390	ReAlnet10_cornet	X
391	BiT-M-R50x1	X
392	BiT-M-R50x3	X
393	unet_entire	X
394	yudixie_resnet50_distance_translation_rotation_0_240908	X
395	CORnetZ_CIFAR10_bs32_20_04	X
396	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
397	pixels	X
398	dorinet_cornet_z	X
399	resnet50	X
400	ViT-B/32	X
401	resnet50_ImageNet	X
402	resnet50_pretrained_with_retinal_waves	X
403	openclip	X
404	TAU	X
405	resnet50-cifar	X
406	SimVP	X
407	MIM	X
408	yudixie_resnet50_distance_reg_0_240908	X

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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021contrast

Metric: error_consistency