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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	.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	resnet50_ewconplus_wzc	.490
43	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.481
44	convnext_tiny_sup	.478
45	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.476
46	cvt_cvt-21-224-in1k_4_LucyV4	.460
47	convnext_small_imagenet_100_seed-0	.456
48	convnext_small_imagenet_full_seed-0	.452
49	cvt_cvt-21-384-in1k_4_LucyV4	.445
50	convnext_tiny_imagenet_full_seed-0	.433
51	cvt_cvt-13-384-in1k_4_LucyV4	.431
52	resnet50-VITO-8deg-cc	.430
53	vit_relpos_base_patch16_clsgap_224:sw_in1k	.429
54	vit_base_patch16_clip_224:openai_ft_in1k	.418
55	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.416
56	vit_large_patch14_clip_336:laion2b_ft_in1k	.413
57	resnet50-moclr8deg	.403
58	resnet50_moco_v2	.400
59	resnet101	.399
60	BiT-S-R50x3	.397
61	resnet_152_v2	.376
62	antialias-resnet152	.370
63	resnet50_byol	.367
64	inception_v3	.351
65	convnext_base:clip_laiona_augreg_ft_in1k_384	.349
66	antialiased-rnext101_32x8d	.347
67	convnext_base_imagenet_full_seed-0	.347
68	resnet50	.345
69	fixres_resnext101_32x48d_wsl	.344
70	resnet152_imagenet_full	.340
71	resnet50_imagenet_full	.335
72	cv_18_dagger_408_pretrained	.332
73	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.329
74	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.328
75	densenet-169-keras	.324
76	convnext_large_imagenet_full_seed-0	.322
77	densenet-121-keras	.317
78	deit_base_imagenet_full_seed-0	.309
79	antialiased-r50	.308
80	deit_small_imagenet_full_seed-0	.308
81	densenet-201-keras	.302
82	resnet_101_v1	.301
83	BiT-S-R152x4	.294
84	blt_vs	.283
85	resnet34_imagenet_full	.276
86	resnet50_random_l2_perturb	.274
87	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.271
88	AlexNet_SIN_fov4	.270
89	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.269
90	inception_v2-tf	.265
91	BiT-S-R101x3	.261
92	resnet50_simclr	.260
93	densenet-169	.254
94	densenet-201	.253
95	densenet_201_pytorch	.253
96	densenet-121	.250
97	resnet_152_v1	.246
98	inception_v1-tf	.242
99	vit_large_patch14_clip_224:openai_ft_in1k	.241
100	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.240
101	nasnet_mobile	.237
102	resnet50-SIN_IN_IN	.229
103	resnet_SIN_IN_FT_IN	.229
104	Res2Net50_26w_4s	.228
105	resnet-50-pytorch	.221
106	resnet50-sup	.221
107	resnet50_tutorial	.221
108	resnet_50_v1	.221
109	grcnn_109	.220
110	efficientnet_b0	.219
111	resnet-101_v1-tf	.216
112	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.215
113	deit_large_imagenet_full_seed-0	.206
114	convnext_large:fb_in22k_ft_in1k	.204
115	alexnet_fov4	.204
116	AT_efficientnet-b2	.200
117	AdvProp_efficientnet-b2	.200
118	resnet50-SIN_IN	.193
119	BiT-S-R152x2	.191
120	resnet_50_v1_spiking	.190
121	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.186
122	grcnn	.184
123	alexnet	.182
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_workflow_trigger	.123
169	alexnet-baseline	.123
170	alexnet	.123
171	alexnet	.123
172	alexnet	.123
173	alexnet	.123
174	alexnet	.123
175	alexnet	.123
176	alexnet	.123
177	alexnet	.123
178	alexnet	.123
179	alexnet	.123
180	texture_shape_resnet50_trained_on_SIN	.122
181	resnet50_l2_3_robust	.121
182	vgg_16	.121
183	resnet50-meshes-lt-100-original-scratch	.120
184	mobilenet_v1_0.25_192	.120
185	vonegrcnn_62e_nobn	.120
186	mobilenet_v2_0.5_128	.119
187	voneresnet-50-robust	.119
188	resnet50_linf_4_robust	.119
189	mobilevit_small	.118
190	alexnet_fov16	.118
191	alexnet_robust_correct	.117
192	resnet50_linf_8_robust	.116
193	alexnet_l2_3_robust	.115
194	vgg-19-keras	.114
195	vonegrcnn_62e_nobn	.113
196	resnet-50-robust	.113
197	imagenet_l2_3_0	.113
198	resnet50_robust_l2_eps3	.113
199	resnet50_robust_l2_eps1	.113
200	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
201	alexnet_linf_8_robust	.109
202	imagenet_l2_10_0	.108
203	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
204	ReAlnet09	.107
205	mobilenet_v2_1_0_128	.107
206	ReAlnet07	.107
207	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
208	resnet50_imagenet_10_seed-0	.106
209	deit_small_imagenet_10_seed-0	.106
210	deit_small_imagenet_1_seed-0	.106
211	mobilenet_v2_0_5_192	.105
212	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
213	ReAlnet06	.102
214	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
215	vonealexnet_gaussian_noise_std4_fixed	.101
216	alexnet_training_seed_05	.101
217	alexnet_training_seed_01	.101
218	alexnet_training_seed_01_fov4	.101
219	alexnet_training_seed_01_fov12	.101
220	alexnet_training_seed_01_fov16	.101
221	barlow-twins-resnet50	.099
222	ReAlnet10	.097
223	alexnet_fov12	.097
224	vonegrcnn_47e	.096
225	konkle_alexnetgn_ipcl_ref01_primary_model	.094
226	resnet50-vicreg	.094
227	mobilenet_v1_0.5_192	.094
228	mobilenet_v2_1.0_96	.093
229	mobilenet_v2_0.35_128	.091
230	mobilenet_v2_1_0_160	.088
231	mobilenet_v2_0.35_192	.088
232	resnet18_imagenet_full	.087
233	resnet50-SIN	.085
234	mobilenet_v1_0.5_128	.083
235	mobilenet_v1_1.0_224	.081
236	mobilenet_v2_0.35_160	.081
237	squeezenet1_0	.081
238	mobilenet_v2_1.0_128-tf	.080
239	AdvProp_efficientnet-b8	.079
240	resnet50_eMMCR_Vanilla	.079
241	alexnet_early_checkpoint	.075
242	inception_v1	.074
243	resnet50-vicregl0p9	.072
244	mobilenet_v1_0.5_160	.072
245	mobilenet_v2_1_0_192	.071
246	mobilenet_v2_1.0_160-tf	.070
247	resnet50_FractalDB	.070
248	ReAlnet01	.070
249	mobilenet_v2_1.0_224-tf	.070
250	efficientnet_b0_imagenet_full	.069
251	voneresnet-50	.069
252	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.064
253	alexnet_training_seed_09	.058
254	mobilenet_v2_1.0_192-tf	.056
255	alexnet_training_seed_07	.054
256	CORnet-S	.052
257	mobilenet_v2_0.5_192-tf	.052
258	mobilenet_v2_1.4_224-tf	.050
259	mobilenet_v1_0.75_160	.050
260	resnet_50_v1_spiking_l4	.049
261	mobilenet_v2_0.5_160	.049
262	resnet50_imagenet_1_seed-0	.048
263	resnet-34	.047
264	mobilenet_v1_1.0_160	.046
265	mobilenet_v1_1.0_192	.045
266	resnet50-vicregl0p75	.044
267	efficientnet_b1_imagenet_full	.044
268	AdvProp_efficientnet-b0	.044
269	mobilenet_v2_0_75_192	.044
270	custom_model_cv_18_dagger_408	.044
271	mobilenet_v2_0.35_224	.043
272	mobilenet_v1_0.5_224	.043
273	resnet_50_v1_spiking_l3	.038
274	resnet50-barlow	.037
275	resnet50_imagenet_100_seed-0	.035
276	resnet_50_v1_spiking_l4a	.033
277	mobilenet_v2_0_75_160	.033
278	mobilenet_v1_0.75_128	.033
279	mobilenet_v2_0.75_128	.032
280	my-model	.030
281	AlexNet_SIN_fov12	.027
282	mobilenet_v2_1_3_224	.025
283	convnext_small_imagenet_10_seed-0	.025
284	mobilenet_v1_0.75_192	.024
285	mobilenet_v2_0_75_224	.024
286	AlexNet_SIN_fov16	.023
287	AlexNet_SIN	.022
288	alexnet_training_seed_03	.022
289	mobilenet_v1_0.25_224	.022
290	deit_small_imagenet_100_seed-0	.021
291	mobilenet_v2_0.75_96	.020
292	squeezenet1_1	.020
293	convnext_small_imagenet_1_seed-0	.020
294	alexnet_training_seed_08	.020
295	mobilenet_v2_1.3_224-tf	.019
296	mobilenet_v2_0.75_160-tf	.018
297	artResNet18_1	.018
298	resnet50_random_linf8_perturb	.017
299	mobilenet_v2_0.5_96	.017
300	mobilenet_v1_1.0_128	.016
301	mobilenet_v1_0.25_160	.016
302	alexnet_training_seed_06	.016
303	texture_shape_alexnet_trained_on_SIN	.015
304	mobilenet_v2_0.35_96	.015
305	mobilenet_v2_0.75_224-tf	.014
306	mobilenet_v2_0.75_192-tf	.013
307	regnet_y_400mf	.013
308	resnet-50_untrained	.012
309	resnet50-meshes-lt-100-original-pretrained	.012
310	mobilenet_v1_0.75_224	.011
311	resnet50-simclr-vissl	.008
312	shufflenet_v2_x1_0	.006
313	0.5x_resnet-18_LC_w_sh_1_iter	.006
314	resnet-18-LC_w_sh_100_iter_conv_init_m
315	ViT-B/32
316	BiT-M-R50x3
317	BiT-M-R50x1
318	BiT-M-R152x2
319	resnet-18-LC_w_sh_1_iter_conv_init_m
320	resnet-18-LC_w_sh_1_iter_m
321	resnet-18-LC_w_sh_10_iter_m
322	resnet-18-LC_w_sh_100_iter_m
323	resnet-18-LC_w_sh_10_iter_conv_init_m
324	FrankRobWobv0
325	resnet18-local_aggregation
326	0.5x_resnet-18_LC
327	CORnetZ_CIFAR10
328	resnet18-deepcluster
329	resnet18-colorization
330	r3m_resnet18
331	CORnet-Z
332	resnet18-instance_recognition
333	my-model
334	vggface
335	0.5x_resnet-18_LC_w_sh_10_iter
336	r3m_resnet50
337	r3m_resnet34
338	resnet-18-LC_m
339	r3m_resnet50_nocrop
340	resnet18-supervised
341	resnet-18-LC_1st_conv_m
342	resnet-18-LC_conv_init_m
343	resnet18-contrastive_multiview
344	0.5x_resnet-18
345	RN50
346	0.5x_resnet-18_LC_w_sh_100_iter
347	RN50
348	resnet18-relative_position
349	resnet18-autoencoder
350	resnet18-simclr
351	ViT_L_32
352	ViT_B_32
353	resnet18-depth_prediction
354	ViT_B_16
355	r3m_resnet34_nocrop
356	hmax
357	resnet18-contrastive_predictive
358	omnivore_swinT
359	resnet-18-LC_untrained
360	resnet-18_untrained
361	resnet-50x2_untrained
362	dcgan
363	v1-pyr-nodown
364	unet_entire
365	CORnetZ_CIFAR10_bs32_20_04
366	pixels
367	dorinet_cornet_z
368	resnet50
369	openclip
370	resnet50-cifar
371	resnet50_pretrained_with_retinal_waves
372	resnet50_ImageNet
373	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
374	yudixie_resnet50_random_0_240908
375	yudixie_resnet50_imagenet1kpret_0_240908
376	yudixie_resnet50_cat_obj_class_all_latents_0_240908
377	yudixie_resnet50_object_class_0_240908
378	yudixie_resnet50_category_class_0_240908
379	resnet18_imagenet21kP
380	ConvLSTM
381	PredRNN
382	yudixie_resnet50_distance_reg_0_240908
383	SimVP
384	TAU
385	MIM
386	ReAlnet01_cornet
387	inception_v3_pytorch
388	yolos_tiny
389	vit_base_patch16_dinov3:lvd1689m
390	vit_base_patch14_reg4_dinov2:lvd142m
391	yudixie_resnet50_imagenet1kpret_0_240312
392	yudixie_resnet18_translation_reg_0_240719
393	briaai_rmbg_1_4
394	yudixie_resnet18_distance_reg_0_240719
395	ReAlnet03_cornet
396	yudixie_resnet18_rotation_reg_0_240719
397	yudixie_resnet18_imagenet1kpret_0_240719
398	compact_resnet50_V4
399	ReAlnet02_cornet
400	resnet18_ecoset_full
401	resnet34_ecoset_full
402	VOneCORnet-S
403	resnet50_ecoset_full
404	compact_resnet50_robust_V4
405	resnet101_ecoset_full
406	ReAlnet01_cornet_fov12
407	yudixie_resnet50_distance_translation_0_240908
408	yudixie_resnet18_distance_translation_0_240719
409	ReAlnet04_cornet
410	yudixie_resnet18_translation_rotation_0_240719
411	vit_base_patch14_dinov2:lvd142m
412	yudixie_resnet18_category_class_0_240719
413	yudixie_resnet50_distance_rotation_0_240908
414	compact_vgg19_V4
415	compact_resnet50_IT
416	ReAlnet05_cornet
417	yudixie_resnet50_rotation_reg_0_240908
418	yudixie_resnet18_random_0_240719
419	yudixie_resnet18_cat_obj_class_all_latents_0_240719
420	resnet-18_test_m
421	ReAlnet01_cornet_fov4
422	SeLaVi-AVE
423	yudixie_resnet18_object_class_0_240719
424	yudixie_resnet50_translation_reg_0_240908
425	ReAlnet06_cornet
426	ReAlnet01_cornet_fov16
427	compact_vgg19_IT
428	r3d_18
429	yudixie_resnet50_translation_rotation_0_240908
430	mc3_18
431	ReAlnet01_ALL
432	ReAlnet07_cornet
433	s3d
434	mvit_v1_b
435	ReAlnet02_ALL
436	ReAlnet08_cornet
437	compact_cornets_V4
438	omnivore_swinB
439	ReAlnet04_ALL
440	SeLaVi-Kinetics400
441	ReAlnet09_cornet
442	SeLaVi-VGG-Sound
443	compact_cornets_IT
444	ReAlnet05_ALL
445	SeLaVi-Kinetics-Sound
446	omnivore_swinS
447	r2plus1d_18
448	mvit_v2_s
449	compact_resnet50_robust_IT
450	ReAlnet10_cornet
451	resnet152_ecoset_full
452	ReAlnet07_ALL
453	yudixie_resnet18_distance_rotation_0_240719
454	ReAlnet08_ALL
455	yudixie_resnet50_distance_translation_rotation_0_240908
456	yudixie_resnet18_distance_translation_rotation_0_240719
457	ReAlnet09_ALL
458	resnet50_primary_visual_cortex
459	ReAlnet10_ALL
460	ReAlnet03_ALL
461	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