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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_halfPrecision	.544
33	pnasnet_large	.544
34	xception	.540
35	ViT_B_32_imagenet1k	.533
36	inception_v3-tf	.511
37	cvt_cvt-13-224-in1k_4_LucyV4	.507
38	focalnet_tiny_lrf_in1k	.506
39	convnext_femto_ols:d1_in1k	.503
40	convnext_tiny:in12k_ft_in1k	.503
41	resnet-152_v2_pytorch	.496
42	tv_efficientnet-b1	.492
43	resnet50_ewconplus_wzc	.490
44	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.481
45	convnext_tiny_sup	.478
46	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.476
47	cvt_cvt-21-224-in1k_4_LucyV4	.460
48	convnext_small_imagenet_100_seed-0	.456
49	convnext_small_imagenet_full_seed-0	.452
50	cvt_cvt-21-384-in1k_4_LucyV4	.445
51	convnext_tiny_imagenet_full_seed-0	.433
52	cvt_cvt-13-384-in1k_4_LucyV4	.431
53	resnet50-VITO-8deg-cc	.430
54	vit_relpos_base_patch16_clsgap_224:sw_in1k	.429
55	vit_base_patch16_clip_224:openai_ft_in1k	.418
56	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
57	vit_large_patch14_clip_336:laion2b_ft_in1k	.413
58	resnet50-moclr8deg	.403
59	resnet50_moco_v2	.400
60	resnet101	.399
61	BiT-S-R50x3	.397
62	resnet_152_v2	.376
63	antialias-resnet152	.370
64	resnet50_byol	.367
65	inception_v3	.351
66	convnext_base:clip_laiona_augreg_ft_in1k_384	.349
67	antialiased-rnext101_32x8d	.347
68	convnext_base_imagenet_full_seed-0	.347
69	resnet50	.345
70	fixres_resnext101_32x48d_wsl	.344
71	resnet152_imagenet_full	.340
72	resnet50_imagenet_full	.335
73	cv_18_dagger_408_pretrained	.332
74	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.329
75	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.328
76	densenet-169-keras	.324
77	convnext_large_imagenet_full_seed-0	.322
78	densenet-121-keras	.317
79	deit_base_imagenet_full_seed-0	.309
80	antialiased-r50	.308
81	deit_small_imagenet_full_seed-0	.308
82	densenet-201-keras	.302
83	resnet_101_v1	.301
84	BiT-S-R152x4	.294
85	blt_vs	.283
86	resnet34_imagenet_full	.276
87	resnet50_random_l2_perturb	.274
88	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.271
89	AlexNet_SIN_fov4	.270
90	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.269
91	inception_v2-tf	.265
92	BiT-S-R101x3	.261
93	resnet50_simclr	.260
94	densenet-169	.254
95	densenet-201	.253
96	densenet_201_pytorch	.253
97	densenet-121	.250
98	resnet_152_v1	.246
99	inception_v1-tf	.242
100	vit_large_patch14_clip_224:openai_ft_in1k	.241
101	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.240
102	nasnet_mobile	.237
103	resnet50-SIN_IN_IN	.229
104	resnet_SIN_IN_FT_IN	.229
105	Res2Net50_26w_4s	.228
106	resnet-50-pytorch	.221
107	resnet50-sup	.221
108	resnet50_tutorial	.221
109	resnet_50_v1	.221
110	grcnn_109	.220
111	efficientnet_b0	.219
112	resnet-101_v1-tf	.216
113	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.215
114	deit_large_imagenet_full_seed-0	.206
115	convnext_large:fb_in22k_ft_in1k	.204
116	alexnet_fov4	.204
117	AT_efficientnet-b2	.200
118	AdvProp_efficientnet-b2	.200
119	resnet50-SIN_IN	.193
120	BiT-S-R152x2	.191
121	resnet_50_v1_spiking	.190
122	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.186
123	grcnn	.184
124	resnet-50_v1-tf	.182
125	resnet-152_v1-tf	.182
126	BiT-S-R101x1	.181
127	AdvProp_efficientnet-b6	.179
128	vgg_19	.178
129	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.176
130	mobilenet_v2_0_5_224	.176
131	resnet101_imagenet_full	.171
132	vonegrcnn_52e_full	.171
133	efficientnet-b7	.166
134	efficientnet_b2_imagenet_full	.166
135	mobilenet_v2_1-4_224_pytorch	.166
136	mobilenet_v2_1_4_224	.166
137	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
138	BiT-S-R50x1	.158
139	nasnet_mobile-tf	.158
140	alexnet_training_seed_02	.156
141	alexnet_training_seed_04	.156
142	alexnet_training_seed_10	.156
143	vgg-16-keras	.155
144	alexnet_random_linf8_perturb	.155
145	AdvProp_efficientnet-b4	.155
146	AT_efficientnet-b4	.155
147	ReAlnet05	.154
148	mobilenet_v2_0.5_224-tf	.152
149	mobilenet_v1_0.25_128	.152
150	vonegrcnn_47e	.151
151	alexnet_random_l2_3_perturb	.148
152	ReAlnet02	.147
153	resnet50-vitoimagevidnet8	.147
154	resnet-18	.147
155	convnext_xlarge:fb_in22k_ft_in1k	.145
156	ReAlnet04	.144
157	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.143
158	mobilenet_v2_1_0_224	.140
159	voneresnet-50-non_stochastic	.140
160	AdvProp_efficientnet-b7	.139
161	cornet_s	.139
162	ReAlnet08	.135
163	ReAlnet03	.133
164	alexnet_reduced_aliasing_early_checkpoint	.131
165	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.131
166	vonegrcnn_52e_full	.125
167	alexnet_ks_torevert	.123
168	alexnet	.123
169	texture_shape_resnet50_trained_on_SIN	.122
170	resnet50_l2_3_robust	.121
171	vgg_16	.121
172	resnet50-meshes-lt-100-original-scratch	.120
173	mobilenet_v1_0.25_192	.120
174	vonegrcnn_62e_nobn	.120
175	mobilenet_v2_0.5_128	.119
176	voneresnet-50-robust	.119
177	resnet50_linf_4_robust	.119
178	mobilevit_small	.118
179	alexnet_fov16	.118
180	alexnet_robust_correct	.117
181	resnet50_linf_8_robust	.116
182	alexnet_l2_3_robust	.115
183	vgg-19-keras	.114
184	vonegrcnn_62e_nobn	.113
185	resnet-50-robust	.113
186	imagenet_l2_3_0	.113
187	resnet50_robust_l2_eps3	.113
188	resnet50_robust_l2_eps1	.113
189	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
190	alexnet_linf_8_robust	.109
191	imagenet_l2_10_0	.108
192	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
193	ReAlnet09	.107
194	mobilenet_v2_1_0_128	.107
195	ReAlnet07	.107
196	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
197	resnet50_imagenet_10_seed-0	.106
198	deit_small_imagenet_10_seed-0	.106
199	deit_small_imagenet_1_seed-0	.106
200	mobilenet_v2_0_5_192	.105
201	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
202	ReAlnet06	.102
203	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
204	vonealexnet_gaussian_noise_std4_fixed	.101
205	alexnet_training_seed_05	.101
206	alexnet_training_seed_01	.101
207	alexnet_training_seed_01_fov4	.101
208	alexnet_training_seed_01_fov12	.101
209	alexnet_training_seed_01_fov16	.101
210	barlow-twins-resnet50	.099
211	ReAlnet10	.097
212	alexnet_fov12	.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	resnet50_eMMCR_Vanilla	.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	AlexNet_SIN_fov12	.027
271	mobilenet_v2_1_3_224	.025
272	convnext_small_imagenet_10_seed-0	.025
273	mobilenet_v1_0.75_192	.024
274	mobilenet_v2_0_75_224	.024
275	AlexNet_SIN_fov16	.023
276	AlexNet_SIN	.022
277	alexnet_training_seed_03	.022
278	mobilenet_v1_0.25_224	.022
279	deit_small_imagenet_100_seed-0	.021
280	mobilenet_v2_0.75_96	.020
281	squeezenet1_1	.020
282	convnext_small_imagenet_1_seed-0	.020
283	alexnet_training_seed_08	.020
284	mobilenet_v2_1.3_224-tf	.019
285	mobilenet_v2_0.75_160-tf	.018
286	artResNet18_1	.018
287	resnet50_random_linf8_perturb	.017
288	mobilenet_v2_0.5_96	.017
289	mobilenet_v1_1.0_128	.016
290	mobilenet_v1_0.25_160	.016
291	alexnet_training_seed_06	.016
292	texture_shape_alexnet_trained_on_SIN	.015
293	mobilenet_v2_0.35_96	.015
294	mobilenet_v2_0.75_224-tf	.014
295	mobilenet_v2_0.75_192-tf	.013
296	regnet_y_400mf	.013
297	resnet-50_untrained	.012
298	resnet50-meshes-lt-100-original-pretrained	.012
299	mobilenet_v1_0.75_224	.011
300	resnet50-simclr-vissl	.008
301	shufflenet_v2_x1_0	.006
302	0.5x_resnet-18_LC_w_sh_1_iter	.006
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	ViT-B/32
305	BiT-M-R50x3
306	BiT-M-R50x1
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_1_iter_conv_init_m
309	resnet-18-LC_w_sh_1_iter_m
310	resnet-18-LC_w_sh_10_iter_m
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	resnet18-colorization
319	r3m_resnet18
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_B_32
342	resnet18-depth_prediction
343	ViT_B_16
344	r3m_resnet34_nocrop
345	hmax
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	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_random_0_240908
364	yudixie_resnet50_imagenet1kpret_0_240908
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	TAU
374	MIM
375	vit_base_patch14_reg4_dinov2-lvd142m
376	ReAlnet01_cornet
377	inception_v3_pytorch
378	yolos_tiny
379	yudixie_resnet50_imagenet1kpret_0_240312
380	yudixie_resnet18_translation_reg_0_240719
381	briaai_rmbg_1_4
382	yudixie_resnet18_distance_reg_0_240719
383	ReAlnet03_cornet
384	yudixie_resnet18_rotation_reg_0_240719
385	yudixie_resnet18_imagenet1kpret_0_240719
386	eBarlow_lmda_02_1000ep
387	compact_resnet50_V4
388	ReAlnet02_cornet
389	vit_base_patch16_dinov3-lvd1689m
390	resnet18_ecoset_full
391	resnet34_ecoset_full
392	vit_base_patch14_dinov2-lvd142m
393	VOneCORnet-S
394	resnet50_ecoset_full
395	compact_resnet50_robust_V4
396	resnet101_ecoset_full
397	ReAlnet01_cornet_fov12
398	yudixie_resnet50_distance_translation_0_240908
399	yudixie_resnet18_distance_translation_0_240719
400	ReAlnet04_cornet
401	yudixie_resnet18_translation_rotation_0_240719
402	yudixie_resnet18_category_class_0_240719
403	yudixie_resnet50_distance_rotation_0_240908
404	compact_vgg19_V4
405	compact_resnet50_IT
406	ReAlnet05_cornet
407	yudixie_resnet50_rotation_reg_0_240908
408	yudixie_resnet18_random_0_240719
409	yudixie_resnet18_cat_obj_class_all_latents_0_240719
410	resnet-18_test_m
411	ReAlnet01_cornet_fov4
412	SeLaVi-AVE
413	yudixie_resnet18_object_class_0_240719
414	yudixie_resnet50_translation_reg_0_240908
415	ReAlnet06_cornet
416	ReAlnet01_cornet_fov16
417	compact_vgg19_IT
418	r3d_18
419	yudixie_resnet50_translation_rotation_0_240908
420	mc3_18
421	ReAlnet01_ALL
422	ReAlnet07_cornet
423	s3d
424	mvit_v1_b
425	aughost_fair_resnet18
426	ReAlnet02_ALL
427	ReAlnet08_cornet
428	compact_cornets_V4
429	omnivore_swinB
430	ReAlnet04_ALL
431	SeLaVi-Kinetics400
432	ReAlnet09_cornet
433	SeLaVi-VGG-Sound
434	compact_cornets_IT
435	ReAlnet05_ALL
436	SeLaVi-Kinetics-Sound
437	omnivore_swinS
438	r2plus1d_18
439	mvit_v2_s
440	compact_resnet50_robust_IT
441	ReAlnet10_cornet
442	resnet152_ecoset_full
443	ReAlnet07_ALL
444	yudixie_resnet18_distance_rotation_0_240719
445	ReAlnet08_ALL
446	yudixie_resnet50_distance_translation_rotation_0_240908
447	yudixie_resnet18_distance_translation_rotation_0_240719
448	ReAlnet09_ALL
449	resnet50_primary_visual_cortex
450	ReAlnet10_ALL
451	ReAlnet03_ALL
452	ReAlnet06_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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021contrast

Metric: error_consistency