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	.718
4	CLIP_resnet50_float32	.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	.253
93	densenet_201_pytorch	.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	resnet_SIN_IN_FT_IN	.229
101	resnet50-SIN_IN_IN	.229
102	Res2Net50_26w_4s	.228
103	resnet-50-pytorch	.221
104	resnet50-sup	.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	resnet_50_v1_spiking	.190
118	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.186
119	grcnn	.184
120	alexnet	.182
121	resnet-50_v1-tf	.182
122	resnet-152_v1-tf	.182
123	BiT-S-R101x1	.181
124	AdvProp_efficientnet-b6	.179
125	vgg_19	.178
126	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.176
127	mobilenet_v2_0_5_224	.176
128	resnet101_imagenet_full	.171
129	vonegrcnn_52e_full	.171
130	efficientnet-b7	.166
131	efficientnet_b2_imagenet_full	.166
132	mobilenet_v2_1_4_224	.166
133	mobilenet_v2_1-4_224_pytorch	.166
134	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
135	BiT-S-R50x1	.158
136	nasnet_mobile-tf	.158
137	alexnet_training_seed_10	.156
138	alexnet_training_seed_02	.156
139	alexnet_training_seed_04	.156
140	vgg-16-keras	.155
141	alexnet_random_linf8_perturb	.155
142	AdvProp_efficientnet-b4	.155
143	AT_efficientnet-b4	.155
144	ReAlnet05	.154
145	mobilenet_v2_0.5_224-tf	.152
146	mobilenet_v1_0.25_128	.152
147	vonegrcnn_47e	.151
148	alexnet_random_l2_3_perturb	.148
149	ReAlnet02	.147
150	resnet50-vitoimagevidnet8	.147
151	resnet-18	.147
152	convnext_xlarge:fb_in22k_ft_in1k	.145
153	ReAlnet04	.144
154	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.143
155	mobilenet_v2_1_0_224	.140
156	voneresnet-50-non_stochastic	.140
157	AdvProp_efficientnet-b7	.139
158	cornet_s	.139
159	ReAlnet08	.135
160	ReAlnet03	.133
161	alexnet_reduced_aliasing_early_checkpoint	.131
162	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.131
163	vonegrcnn_52e_full	.125
164	alexnet_ks_torevert	.123
165	alexnet	.123
166	alexnet	.123
167	alexnet	.123
168	alexnet	.123
169	alexnet	.123
170	alexnet	.123
171	alexnet	.123
172	alexnet	.123
173	alexnet-baseline	.123
174	alexnet	.123
175	texture_shape_resnet50_trained_on_SIN	.122
176	resnet50_l2_3_robust	.121
177	vgg_16	.121
178	resnet50-meshes-lt-100-original-scratch	.120
179	mobilenet_v1_0.25_192	.120
180	vonegrcnn_62e_nobn	.120
181	mobilenet_v2_0.5_128	.119
182	voneresnet-50-robust	.119
183	resnet50_linf_4_robust	.119
184	mobilevit_small	.118
185	alexnet_robust_correct	.117
186	resnet50_linf_8_robust	.116
187	alexnet_l2_3_robust	.115
188	vgg-19-keras	.114
189	vonegrcnn_62e_nobn	.113
190	imagenet_l2_3_0	.113
191	resnet50_robust_l2_eps3	.113
192	resnet-50-robust	.113
193	resnet50_robust_l2_eps1	.113
194	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
195	alexnet_linf_8_robust	.109
196	imagenet_l2_10_0	.108
197	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
198	ReAlnet09	.107
199	mobilenet_v2_1_0_128	.107
200	ReAlnet07	.107
201	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
202	deit_small_imagenet_10_seed-0	.106
203	resnet50_imagenet_10_seed-0	.106
204	deit_small_imagenet_1_seed-0	.106
205	mobilenet_v2_0_5_192	.105
206	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
207	ReAlnet06	.102
208	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
209	vonealexnet_gaussian_noise_std4_fixed	.101
210	alexnet_training_seed_01	.101
211	alexnet_training_seed_05	.101
212	barlow-twins-resnet50	.099
213	ReAlnet10	.097
214	vonegrcnn_47e	.096
215	konkle_alexnetgn_ipcl_ref01_primary_model	.094
216	resnet50-vicreg	.094
217	mobilenet_v1_0.5_192	.094
218	mobilenet_v2_1.0_96	.093
219	mobilenet_v2_0.35_128	.091
220	mobilenet_v2_1_0_160	.088
221	mobilenet_v2_0.35_192	.088
222	resnet18_imagenet_full	.087
223	resnet50-SIN	.085
224	mobilenet_v1_0.5_128	.083
225	mobilenet_v1_1.0_224	.081
226	mobilenet_v2_0.35_160	.081
227	squeezenet1_0	.081
228	mobilenet_v2_1.0_128-tf	.080
229	AdvProp_efficientnet-b8	.079
230	alexnet_early_checkpoint	.075
231	inception_v1	.074
232	resnet50-vicregl0p9	.072
233	mobilenet_v1_0.5_160	.072
234	mobilenet_v2_1_0_192	.071
235	mobilenet_v2_1.0_160-tf	.070
236	resnet50_FractalDB	.070
237	ReAlnet01	.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	resnet_50_v1_spiking_l4	.049
250	mobilenet_v2_0.5_160	.049
251	resnet50_imagenet_1_seed-0	.048
252	resnet-34	.047
253	mobilenet_v1_1.0_160	.046
254	mobilenet_v1_1.0_192	.045
255	resnet50-vicregl0p75	.044
256	efficientnet_b1_imagenet_full	.044
257	AdvProp_efficientnet-b0	.044
258	mobilenet_v2_0_75_192	.044
259	custom_model_cv_18_dagger_408	.044
260	mobilenet_v2_0.35_224	.043
261	mobilenet_v1_0.5_224	.043
262	resnet_50_v1_spiking_l3	.038
263	resnet50-barlow	.037
264	resnet50_imagenet_100_seed-0	.035
265	resnet_50_v1_spiking_l4a	.033
266	mobilenet_v2_0_75_160	.033
267	mobilenet_v1_0.75_128	.033
268	mobilenet_v2_0.75_128	.032
269	my-model	.030
270	mobilenet_v2_1_3_224	.025
271	convnext_small_imagenet_10_seed-0	.025
272	mobilenet_v1_0.75_192	.024
273	mobilenet_v2_0_75_224	.024
274	AlexNet_SIN	.022
275	alexnet_training_seed_03	.022
276	mobilenet_v1_0.25_224	.022
277	deit_small_imagenet_100_seed-0	.021
278	mobilenet_v2_0.75_96	.020
279	squeezenet1_1	.020
280	convnext_small_imagenet_1_seed-0	.020
281	alexnet_training_seed_08	.020
282	mobilenet_v2_1.3_224-tf	.019
283	mobilenet_v2_0.75_160-tf	.018
284	artResNet18_1	.018
285	resnet50_random_linf8_perturb	.017
286	mobilenet_v2_0.5_96	.017
287	mobilenet_v1_1.0_128	.016
288	mobilenet_v1_0.25_160	.016
289	alexnet_training_seed_06	.016
290	texture_shape_alexnet_trained_on_SIN	.015
291	mobilenet_v2_0.35_96	.015
292	mobilenet_v2_0.75_224-tf	.014
293	mobilenet_v2_0.75_192-tf	.013
294	regnet_y_400mf	.013
295	resnet50-meshes-lt-100-original-pretrained	.012
296	resnet-50_untrained	.012
297	mobilenet_v1_0.75_224	.011
298	resnet50-simclr-vissl	.008
299	shufflenet_v2_x1_0	.006
300	0.5x_resnet-18_LC_w_sh_1_iter	.006
301	0.5x_resnet-18_LC_w_sh_10_iter
302	vggface
303	my-model
304	ReAlnet01_cornet
305	inception_v3_pytorch
306	resnet-18-LC_m
307	r3m_resnet50_nocrop
308	yolos_tiny
309	resnet18-supervised
310	resnet-18-LC_1st_conv_m
311	resnet18-instance_recognition
312	resnet-18-LC_conv_init_m
313	resnet18-contrastive_multiview
314	resnet152_ecoset_full
315	0.5x_resnet-18
316	CORnet-Z
317	yudixie_resnet50_object_class_0_240908
318	RN50
319	yudixie_resnet50_cat_obj_class_all_latents_0_240908
320	yudixie_resnet50_imagenet1kpret_0_240312
321	yudixie_resnet18_translation_reg_0_240719
322	briaai_rmbg_1_4
323	yudixie_resnet18_distance_reg_0_240719
324	r3m_resnet18
325	ReAlnet03_cornet
326	yudixie_resnet18_rotation_reg_0_240719
327	yudixie_resnet18_imagenet1kpret_0_240719
328	resnet18-colorization
329	0.5x_resnet-18_LC_w_sh_100_iter
330	yudixie_resnet50_imagenet1kpret_0_240908
331	resnet18-deepcluster
332	CORnetZ_CIFAR10
333	0.5x_resnet-18_LC
334	ReAlnet02_cornet
335	RN50
336	resnet18-local_aggregation
337	resnet18_ecoset_full
338	yudixie_resnet50_random_0_240908
339	resnet34_ecoset_full
340	VOneCORnet-S
341	resnet50_ecoset_full
342	resnet101_ecoset_full
343	resnet18-relative_position
344	resnet18-autoencoder
345	resnet18-simclr
346	ViT_L_32
347	yudixie_resnet50_distance_translation_0_240908
348	yudixie_resnet18_distance_translation_0_240719
349	ReAlnet04_cornet
350	yudixie_resnet18_translation_rotation_0_240719
351	yudixie_resnet18_distance_rotation_0_240719
352	yudixie_resnet18_category_class_0_240719
353	yudixie_resnet50_distance_rotation_0_240908
354	ReAlnet05_cornet
355	yudixie_resnet50_rotation_reg_0_240908
356	yudixie_resnet18_random_0_240719
357	FrankRobWobv0
358	yudixie_resnet18_cat_obj_class_all_latents_0_240719
359	resnet-18_test_m
360	yudixie_resnet18_distance_translation_rotation_0_240719
361	yudixie_resnet18_object_class_0_240719
362	resnet-18-LC_w_sh_100_iter_conv_init_m
363	ViT_B_32
364	yudixie_resnet50_translation_reg_0_240908
365	ReAlnet06_cornet
366	yudixie_resnet50_translation_rotation_0_240908
367	resnet18-depth_prediction
368	resnet-18-LC_w_sh_10_iter_conv_init_m
369	resnet-18-LC_w_sh_100_iter_m
370	ReAlnet07_cornet
371	ViT_B_16
372	r3m_resnet34_nocrop
373	resnet-18-LC_w_sh_10_iter_m
374	ReAlnet08_cornet
375	hmax
376	resnet-18-LC_w_sh_1_iter_m
377	omnivore_swinB
378	resnet18-contrastive_predictive
379	ReAlnet09_cornet
380	omnivore_swinT
381	resnet-18-LC_untrained
382	resnet-18_untrained
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	omnivore_swinS
385	resnet-18-LC_w_sh_1_iter_conv_init_m
386	resnet-50x2_untrained
387	dcgan
388	BiT-M-R152x2
389	v1-pyr-nodown
390	resnet18_imagenet21kP
391	ReAlnet10_cornet
392	BiT-M-R50x1
393	BiT-M-R50x3
394	yudixie_resnet50_distance_translation_rotation_0_240908
395	resnet50_pretrained_with_retinal_waves
396	unet_entire
397	resnet50-cifar
398	CORnetZ_CIFAR10_bs32_20_04
399	pixels
400	resnet50_primary_visual_cortex
401	ViT-B/32
402	dorinet_cornet_z
403	resnet50
404	openclip
405	resnet50_ImageNet
406	ConvLSTM
407	PredRNN
408	yudixie_resnet50_distance_reg_0_240908
409	SimVP
410	r3m_resnet50
411	TAU
412	yudixie_resnet50_category_class_0_240908
413	MIM
414	r3m_resnet34

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