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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	resnet50_st_wzc_seed7_2_323	.676
10	ViT_B_16_imagenet1k	.658
11	resnet_50_v2	.647
12	ViT_L_32_imagenet1k	.635
13	effnetb1_272x240	.632
14	CLIP-RN50	.631
15	inception_v4	.623
16	resnext101_32x8d_wsl	.621
17	ViT_L_16_imagenet1k	.617
18	pnasnet_large-tf	.617
19	inception_v4-tf	.606
20	resnet50_ewconplus_wzc_seed5_2__323	.602
21	nasnet_large	.598
22	vision_transformer_vit_large_patch16_224	.590
23	resnet50_ewconplus_wzc_seed2_2__323	.584
24	resnext101_32x48d_wsl	.581
25	resnext101_32x32d_wsl	.577
26	xception-keras	.574
27	convnext_base_fb_in1k_gram_aligned_dinov3	.570
28	vit_large_patch14_clip_224:laion2b_ft_in1k	.568
29	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.565
30	resnet-50_v2-tf	.563
31	resnet_101_v2	.562
32	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.555
33	pnasnet_large_pytorch	.554
34	SWSL_resnext101_32x8d	.547
35	deit3_base_patch16_224_fb_in22k_ft_in1k	.546
36	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.544
37	pnasnet_large_halfPrecision	.544
38	pnasnet_large	.544
39	xception	.540
40	resnet50_st_wzc_seed3_2_323	.537
41	ViT_B_32_imagenet1k	.533
42	resnet50_ewconplus_wzc_seed6_2_323	.513
43	inception_v3-tf	.511
44	cvt_cvt-13-224-in1k_4_LucyV4	.507
45	focalnet_tiny_lrf_in1k	.506
46	convnext_base_fb_in1k	.506
47	convnext_femto_ols:d1_in1k	.503
48	convnext_tiny:in12k_ft_in1k	.503
49	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.499
50	resnet-152_v2_pytorch	.496
51	tv_efficientnet-b1	.492
52	resnet50_ewconplus_wzc	.490
53	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.481
54	convnext_tiny_sup	.478
55	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.476
56	cvt_cvt-21-224-in1k_4_LucyV4	.460
57	convnext_small_imagenet_100_seed-0	.456
58	convnext_small_imagenet_full_seed-0	.452
59	cvt_cvt-21-384-in1k_4_LucyV4	.445
60	convnext_tiny_imagenet_full_seed-0	.433
61	resnet50_st_wzc_2	.432
62	cvt_cvt-13-384-in1k_4_LucyV4	.431
63	resnet50-VITO-8deg-cc	.430
64	vit_relpos_base_patch16_clsgap_224:sw_in1k	.429
65	vit_base_patch16_clip_224:openai_ft_in1k	.418
66	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
67	vit_large_patch14_clip_336:laion2b_ft_in1k	.413
68	resnet50_hvs_wzc	.403
69	resnet50-moclr8deg	.403
70	resnet50_moco_v2	.400
71	resnet101	.399
72	BiT-S-R50x3	.397
73	convnext_base_fb_in22k_ft_in1k	.390
74	resnet_152_v2	.376
75	antialias-resnet152	.370
76	resnet50_byol	.367
77	inception_v3	.351
78	convnext_base:clip_laiona_augreg_ft_in1k_384	.349
79	antialiased-rnext101_32x8d	.347
80	convnext_base_imagenet_full_seed-0	.347
81	resnet50	.345
82	fixres_resnext101_32x48d_wsl	.344
83	resnet152_imagenet_full	.340
84	resnet50_imagenet_full	.335
85	cv_18_dagger_408_pretrained	.332
86	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.329
87	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.328
88	densenet-169-keras	.324
89	convnext_large_imagenet_full_seed-0	.322
90	densenet-121-keras	.317
91	deit_base_imagenet_full_seed-0	.309
92	antialiased-r50	.308
93	deit_small_imagenet_full_seed-0	.308
94	densenet-201-keras	.302
95	resnet_101_v1	.301
96	BiT-S-R152x4	.294
97	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.290
98	blt_vs	.283
99	resnet34_imagenet_full	.276
100	resnet50_random_l2_perturb	.274
101	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.271
102	AlexNet_SIN_fov4	.270
103	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.269
104	inception_v2-tf	.265
105	BiT-S-R101x3	.261
106	resnet50_simclr	.260
107	densenet-169	.254
108	densenet-201	.253
109	densenet_201_pytorch	.253
110	densenet-121	.250
111	resnet_152_v1	.246
112	inception_v1-tf	.242
113	vit_large_patch14_clip_224:openai_ft_in1k	.241
114	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.240
115	nasnet_mobile	.237
116	resnet50-SIN_IN_IN	.229
117	resnet_SIN_IN_FT_IN	.229
118	Res2Net50_26w_4s	.228
119	resnet-50-pytorch	.221
120	resnet50-sup	.221
121	resnet50_tutorial	.221
122	resnet_50_v1	.221
123	grcnn_109	.220
124	efficientnet_b0	.219
125	resnet-101_v1-tf	.216
126	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.215
127	deit_large_imagenet_full_seed-0	.206
128	convnext_large:fb_in22k_ft_in1k	.204
129	alexnet_fov4	.204
130	AT_efficientnet-b2	.200
131	AdvProp_efficientnet-b2	.200
132	dinov2_vitl14_lc	.197
133	dinov2_vitl14_reg_lc	.193
134	resnet50-SIN_IN	.193
135	BiT-S-R152x2	.191
136	resnet_50_v1_spiking	.190
137	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.186
138	grcnn	.184
139	resnet-50_v1-tf	.182
140	resnet-152_v1-tf	.182
141	BiT-S-R101x1	.181
142	AdvProp_efficientnet-b6	.179
143	vgg_19	.178
144	dinov2_vitg14_lc	.177
145	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.176
146	mobilenet_v2_0_5_224	.176
147	resnet101_imagenet_full	.171
148	vonegrcnn_52e_full	.171
149	efficientnet-b7	.166
150	efficientnet_b2_imagenet_full	.166
151	mobilenet_v2_1-4_224_pytorch	.166
152	mobilenet_v2_1_4_224	.166
153	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
154	BiT-S-R50x1	.158
155	nasnet_mobile-tf	.158
156	alexnet_training_seed_02	.156
157	alexnet_training_seed_04	.156
158	alexnet_training_seed_10	.156
159	vgg-16-keras	.155
160	alexnet_random_linf8_perturb	.155
161	AdvProp_efficientnet-b4	.155
162	AT_efficientnet-b4	.155
163	ReAlnet05	.154
164	mobilenet_v2_0.5_224-tf	.152
165	mobilenet_v1_0.25_128	.152
166	vonegrcnn_47e	.151
167	alexnet_random_l2_3_perturb	.148
168	ReAlnet02	.147
169	resnet50-vitoimagevidnet8	.147
170	resnet-18	.147
171	convnext_xlarge:fb_in22k_ft_in1k	.145
172	ReAlnet04	.144
173	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.143
174	mobilenet_v2_1_0_224	.140
175	voneresnet-50-non_stochastic	.140
176	convnext_large_in22k_supervised	.140
177	AdvProp_efficientnet-b7	.139
178	cornet_s	.139
179	ReAlnet08	.135
180	ReAlnet03	.133
181	alexnet_reduced_aliasing_early_checkpoint	.131
182	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.131
183	vonegrcnn_52e_full	.125
184	alexnet_ks_torevert	.123
185	alexnet	.123
186	vit_large_clip336_openai	.122
187	texture_shape_resnet50_trained_on_SIN	.122
188	resnet50_l2_3_robust	.121
189	vgg_16	.121
190	mobilenet_v1_0.25_192	.120
191	vonegrcnn_62e_nobn	.120
192	mobilenet_v2_0.5_128	.119
193	voneresnet-50-robust	.119
194	resnet50_linf_4_robust	.119
195	mobilevit_small	.118
196	alexnet_fov16	.118
197	vit_huge_clip336_laion	.117
198	alexnet_robust_correct	.117
199	resnet50_linf_8_robust	.116
200	dinov2_vitg14_reg_lc	.116
201	alexnet_l2_3_robust	.115
202	vgg-19-keras	.114
203	vonegrcnn_62e_nobn	.113
204	resnet-50-robust	.113
205	imagenet_l2_3_0	.113
206	resnet50_robust_l2_eps3	.113
207	resnet50_robust_l2_eps1	.113
208	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
209	alexnet_linf_8_robust	.109
210	convnext_large_clip_laion2b_finelayers	.109
211	imagenet_l2_10_0	.108
212	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
213	ReAlnet09	.107
214	mobilenet_v2_1_0_128	.107
215	ReAlnet07	.107
216	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
217	resnet50_imagenet_10_seed-0	.106
218	deit_small_imagenet_10_seed-0	.106
219	deit_small_imagenet_1_seed-0	.106
220	mobilenet_v2_0_5_192	.105
221	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
222	ReAlnet06	.102
223	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
224	vonealexnet_gaussian_noise_std4_fixed	.101
225	alexnet_training_seed_05	.101
226	alexnet_training_seed_01	.101
227	alexnet_training_seed_01_fov4	.101
228	alexnet_training_seed_01_fov12	.101
229	alexnet_training_seed_01_fov16	.101
230	barlow-twins-resnet50	.099
231	ReAlnet10	.097
232	alexnet_fov12	.097
233	vonegrcnn_47e	.096
234	konkle_alexnetgn_ipcl_ref01_primary_model	.094
235	resnet50-vicreg	.094
236	mobilenet_v1_0.5_192	.094
237	mobilenet_v2_1.0_96	.093
238	mobilenet_v2_0.35_128	.091
239	mobilenet_v2_1_0_160	.088
240	mobilenet_v2_0.35_192	.088
241	resnet18_imagenet_full	.087
242	eva02_large_in22k	.086
243	resnet50-SIN	.085
244	mobilenet_v1_0.5_128	.083
245	mobilenet_v1_1.0_224	.081
246	mobilenet_v2_0.35_160	.081
247	squeezenet1_0	.081
248	mobilenet_v2_1.0_128-tf	.080
249	AdvProp_efficientnet-b8	.079
250	resnet50_eMMCR_Vanilla	.079
251	alexnet_early_checkpoint	.075
252	inception_v1	.074
253	dinov2_vitl14_lc_res336	.073
254	resnet50-vicregl0p9	.072
255	mobilenet_v1_0.5_160	.072
256	mobilenet_v2_1_0_192	.071
257	mobilenet_v2_1.0_160-tf	.070
258	resnet50_FractalDB	.070
259	ReAlnet01	.070
260	mobilenet_v2_1.0_224-tf	.070
261	efficientnet_b0_imagenet_full	.069
262	voneresnet-50	.069
263	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.064
264	alexnet_training_seed_09	.058
265	mobilenet_v2_1.0_192-tf	.056
266	alexnet_training_seed_07	.054
267	CORnet-S	.052
268	mobilenet_v2_0.5_192-tf	.052
269	mobilenet_v2_1.4_224-tf	.050
270	mobilenet_v1_0.75_160	.050
271	resnet_50_v1_spiking_l4	.049
272	mobilenet_v2_0.5_160	.049
273	resnet50_imagenet_1_seed-0	.048
274	resnet-34	.047
275	mobilenet_v1_1.0_160	.046
276	mobilenet_v1_1.0_192	.045
277	resnet50-vicregl0p75	.044
278	efficientnet_b1_imagenet_full	.044
279	AdvProp_efficientnet-b0	.044
280	mobilenet_v2_0_75_192	.044
281	custom_model_cv_18_dagger_408	.044
282	mobilenet_v2_0.35_224	.043
283	mobilenet_v1_0.5_224	.043
284	resnet_50_v1_spiking_l3	.038
285	resnet50-barlow	.037
286	resnet50_imagenet_100_seed-0	.035
287	resnet_50_v1_spiking_l4a	.033
288	mobilenet_v2_0_75_160	.033
289	mobilenet_v1_0.75_128	.033
290	mobilenet_v2_0.75_128	.032
291	my-model	.030
292	AlexNet_SIN_fov12	.027
293	mobilenet_v2_1_3_224	.025
294	convnext_small_imagenet_10_seed-0	.025
295	mobilenet_v1_0.75_192	.024
296	mobilenet_v2_0_75_224	.024
297	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.024
298	AlexNet_SIN_fov16	.023
299	AlexNet_SIN	.022
300	alexnet_training_seed_03	.022
301	mobilenet_v1_0.25_224	.022
302	deit_small_imagenet_100_seed-0	.021
303	mobilenet_v2_0.75_96	.020
304	squeezenet1_1	.020
305	convnext_small_imagenet_1_seed-0	.020
306	alexnet_training_seed_08	.020
307	mobilenet_v2_1.3_224-tf	.019
308	mobilenet_v2_0.75_160-tf	.018
309	artResNet18_1	.018
310	resnet50_random_linf8_perturb	.017
311	mobilenet_v2_0.5_96	.017
312	mobilenet_v1_1.0_128	.016
313	mobilenet_v1_0.25_160	.016
314	alexnet_training_seed_06	.016
315	texture_shape_alexnet_trained_on_SIN	.015
316	mobilenet_v2_0.35_96	.015
317	mobilenet_v2_0.75_224-tf	.014
318	mobilenet_v2_0.75_192-tf	.013
319	regnet_y_400mf	.013
320	resnet-50_untrained	.012
321	mobilenet_v1_0.75_224	.011
322	resnet50-simclr-vissl	.008
323	shufflenet_v2_x1_0	.006
324	0.5x_resnet-18_LC_w_sh_1_iter	.006
325	resnet-18-LC_w_sh_100_iter_conv_init_m
326	ViT-B/32
327	BiT-M-R50x3
328	BiT-M-R50x1
329	BiT-M-R152x2
330	resnet-18-LC_w_sh_1_iter_conv_init_m
331	resnet-18-LC_w_sh_1_iter_m
332	resnet-18-LC_w_sh_10_iter_m
333	resnet-18-LC_w_sh_100_iter_m
334	resnet-18-LC_w_sh_10_iter_conv_init_m
335	FrankRobWobv0
336	resnet18-local_aggregation
337	0.5x_resnet-18_LC
338	CORnetZ_CIFAR10
339	resnet18-deepcluster
340	resnet18-colorization
341	r3m_resnet18
342	CORnet-Z
343	resnet18-instance_recognition
344	my-model
345	vggface
346	0.5x_resnet-18_LC_w_sh_10_iter
347	r3m_resnet50
348	r3m_resnet34
349	resnet-18-LC_m
350	r3m_resnet50_nocrop
351	resnet18-supervised
352	resnet-18-LC_1st_conv_m
353	resnet-18-LC_conv_init_m
354	resnet18-contrastive_multiview
355	0.5x_resnet-18
356	RN50
357	0.5x_resnet-18_LC_w_sh_100_iter
358	RN50
359	resnet18-relative_position
360	resnet18-autoencoder
361	resnet18-simclr
362	ViT_L_32
363	ViT_B_32
364	resnet18-depth_prediction
365	ViT_B_16
366	r3m_resnet34_nocrop
367	hmax
368	resnet18-contrastive_predictive
369	omnivore_swinT
370	resnet-18-LC_untrained
371	resnet-18_untrained
372	resnet-50x2_untrained
373	dcgan
374	v1-pyr-nodown
375	unet_entire
376	CORnetZ_CIFAR10_bs32_20_04
377	pixels
378	dorinet_cornet_z
379	openclip
380	resnet50-cifar
381	resnet50_pretrained_with_retinal_waves
382	resnet50_ImageNet
383	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_imagenet1kpret_0_240908
386	yudixie_resnet50_cat_obj_class_all_latents_0_240908
387	yudixie_resnet50_object_class_0_240908
388	yudixie_resnet50_category_class_0_240908
389	resnet18_imagenet21kP
390	ConvLSTM
391	PredRNN
392	yudixie_resnet50_distance_reg_0_240908
393	SimVP
394	TAU
395	MIM
396	vit_base_patch14_reg4_dinov2-lvd142m
397	ReAlnet01_cornet
398	inception_v3_pytorch
399	yolos_tiny
400	yudixie_resnet50_imagenet1kpret_0_240312
401	yudixie_resnet18_translation_reg_0_240719
402	briaai_rmbg_1_4
403	yudixie_resnet18_distance_reg_0_240719
404	ReAlnet03_cornet
405	yudixie_resnet18_rotation_reg_0_240719
406	yudixie_resnet18_imagenet1kpret_0_240719
407	eBarlow_lmda_02_1000ep
408	compact_resnet50_V4
409	ReAlnet02_cornet
410	vit_base_patch16_dinov3-lvd1689m
411	resnet18_ecoset_full
412	resnet34_ecoset_full
413	vit_base_patch14_dinov2-lvd142m
414	VOneCORnet-S
415	resnet50_ecoset_full
416	compact_resnet50_robust_V4
417	resnet101_ecoset_full
418	ReAlnet01_cornet_fov12
419	yudixie_resnet50_distance_translation_0_240908
420	yudixie_resnet18_distance_translation_0_240719
421	ReAlnet04_cornet
422	yudixie_resnet18_translation_rotation_0_240719
423	yudixie_resnet18_category_class_0_240719
424	yudixie_resnet50_distance_rotation_0_240908
425	compact_vgg19_V4
426	compact_resnet50_IT
427	ReAlnet05_cornet
428	yudixie_resnet50_rotation_reg_0_240908
429	yudixie_resnet18_random_0_240719
430	yudixie_resnet18_cat_obj_class_all_latents_0_240719
431	resnet-18_test_m
432	ReAlnet01_cornet_fov4
433	SeLaVi-AVE
434	yudixie_resnet18_object_class_0_240719
435	yudixie_resnet50_translation_reg_0_240908
436	ReAlnet06_cornet
437	ReAlnet01_cornet_fov16
438	compact_vgg19_IT
439	r3d_18
440	yudixie_resnet50_translation_rotation_0_240908
441	mc3_18
442	ReAlnet01_ALL
443	ReAlnet07_cornet
444	s3d
445	mvit_v1_b
446	aughost_fair_resnet18
447	ReAlnet02_ALL
448	ReAlnet08_cornet
449	compact_cornets_V4
450	omnivore_swinB
451	ReAlnet04_ALL
452	SeLaVi-Kinetics400
453	ReAlnet09_cornet
454	SeLaVi-VGG-Sound
455	compact_cornets_IT
456	ReAlnet05_ALL
457	SeLaVi-Kinetics-Sound
458	omnivore_swinS
459	r2plus1d_18
460	mvit_v2_s
461	compact_resnet50_robust_IT
462	ReAlnet10_cornet
463	resnet152_ecoset_full
464	ReAlnet07_ALL
465	yudixie_resnet18_distance_rotation_0_240719
466	ReAlnet08_ALL
467	yudixie_resnet50_distance_translation_rotation_0_240908
468	yudixie_resnet18_distance_translation_rotation_0_240719
469	ReAlnet09_ALL
470	resnet50_primary_visual_cortex
471	ReAlnet10_ALL
472	ReAlnet03_ALL
473	ReAlnet06_ALL
474	resnet18_fair
475	pr1_stl10_disentangled_alpha04
476	predictive_coding_pc
477	yudixie_wbh_object_class_scaling_0415_run_0
478	yudixie_wbh_object_class_scaling_0415_run_3
479	yudixie_wbh_object_class_scaling_0415_imagenet
480	yudixie_wbh_object_class_scaling_0415_run_2
481	yudixie_wbh_object_class_scaling_0415_run_1
482	deepeisnn_ei_cifar10_vgg19
483	deepeisnn_ei_cifar10_resnet18
484	deepeisnn_ei_imagenet_resnet18
485	dinov2_vitl14_reg_lc_featread
486	convnext_xxlarge_clip

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