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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	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.269
89	inception_v2-tf	.265
90	BiT-S-R101x3	.261
91	resnet50_simclr	.260
92	densenet-169	.254
93	densenet-201	.253
94	densenet_201_pytorch	.253
95	densenet-121	.250
96	resnet_152_v1	.246
97	inception_v1-tf	.242
98	vit_large_patch14_clip_224:openai_ft_in1k	.241
99	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.240
100	nasnet_mobile	.237
101	resnet50-SIN_IN_IN	.229
102	resnet_SIN_IN_FT_IN	.229
103	Res2Net50_26w_4s	.228
104	resnet-50-pytorch	.221
105	resnet50-sup	.221
106	resnet50_tutorial	.221
107	resnet_50_v1	.221
108	grcnn_109	.220
109	efficientnet_b0	.219
110	resnet-101_v1-tf	.216
111	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.215
112	deit_large_imagenet_full_seed-0	.206
113	convnext_large:fb_in22k_ft_in1k	.204
114	AT_efficientnet-b2	.200
115	AdvProp_efficientnet-b2	.200
116	resnet50-SIN_IN	.193
117	BiT-S-R152x2	.191
118	resnet_50_v1_spiking	.190
119	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.186
120	grcnn	.184
121	alexnet	.182
122	resnet-50_v1-tf	.182
123	resnet-152_v1-tf	.182
124	BiT-S-R101x1	.181
125	AdvProp_efficientnet-b6	.179
126	vgg_19	.178
127	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.176
128	mobilenet_v2_0_5_224	.176
129	resnet101_imagenet_full	.171
130	vonegrcnn_52e_full	.171
131	efficientnet-b7	.166
132	efficientnet_b2_imagenet_full	.166
133	mobilenet_v2_1-4_224_pytorch	.166
134	mobilenet_v2_1_4_224	.166
135	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
136	BiT-S-R50x1	.158
137	nasnet_mobile-tf	.158
138	alexnet_training_seed_02	.156
139	alexnet_training_seed_04	.156
140	alexnet_training_seed_10	.156
141	vgg-16-keras	.155
142	alexnet_random_linf8_perturb	.155
143	AdvProp_efficientnet-b4	.155
144	AT_efficientnet-b4	.155
145	ReAlnet05	.154
146	mobilenet_v2_0.5_224-tf	.152
147	mobilenet_v1_0.25_128	.152
148	vonegrcnn_47e	.151
149	alexnet_random_l2_3_perturb	.148
150	ReAlnet02	.147
151	resnet50-vitoimagevidnet8	.147
152	resnet-18	.147
153	convnext_xlarge:fb_in22k_ft_in1k	.145
154	ReAlnet04	.144
155	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.143
156	mobilenet_v2_1_0_224	.140
157	voneresnet-50-non_stochastic	.140
158	AdvProp_efficientnet-b7	.139
159	cornet_s	.139
160	ReAlnet08	.135
161	ReAlnet03	.133
162	alexnet_reduced_aliasing_early_checkpoint	.131
163	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.131
164	vonegrcnn_52e_full	.125
165	alexnet_ks_torevert	.123
166	alexnet-baseline	.123
167	alexnet	.123
168	alexnet	.123
169	alexnet	.123
170	alexnet	.123
171	alexnet	.123
172	alexnet	.123
173	alexnet	.123
174	alexnet	.123
175	alexnet	.123
176	texture_shape_resnet50_trained_on_SIN	.122
177	resnet50_l2_3_robust	.121
178	vgg_16	.121
179	resnet50-meshes-lt-100-original-scratch	.120
180	mobilenet_v1_0.25_192	.120
181	vonegrcnn_62e_nobn	.120
182	mobilenet_v2_0.5_128	.119
183	voneresnet-50-robust	.119
184	resnet50_linf_4_robust	.119
185	mobilevit_small	.118
186	alexnet_robust_correct	.117
187	resnet50_linf_8_robust	.116
188	alexnet_l2_3_robust	.115
189	vgg-19-keras	.114
190	vonegrcnn_62e_nobn	.113
191	resnet-50-robust	.113
192	imagenet_l2_3_0	.113
193	resnet50_robust_l2_eps3	.113
194	resnet50_robust_l2_eps1	.113
195	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
196	alexnet_linf_8_robust	.109
197	imagenet_l2_10_0	.108
198	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
199	ReAlnet09	.107
200	mobilenet_v2_1_0_128	.107
201	ReAlnet07	.107
202	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
203	resnet50_imagenet_10_seed-0	.106
204	deit_small_imagenet_10_seed-0	.106
205	deit_small_imagenet_1_seed-0	.106
206	mobilenet_v2_0_5_192	.105
207	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
208	ReAlnet06	.102
209	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
210	vonealexnet_gaussian_noise_std4_fixed	.101
211	alexnet_training_seed_05	.101
212	alexnet_training_seed_01	.101
213	barlow-twins-resnet50	.099
214	ReAlnet10	.097
215	vonegrcnn_47e	.096
216	konkle_alexnetgn_ipcl_ref01_primary_model	.094
217	resnet50-vicreg	.094
218	mobilenet_v1_0.5_192	.094
219	mobilenet_v2_1.0_96	.093
220	mobilenet_v2_0.35_128	.091
221	mobilenet_v2_1_0_160	.088
222	mobilenet_v2_0.35_192	.088
223	resnet18_imagenet_full	.087
224	resnet50-SIN	.085
225	mobilenet_v1_0.5_128	.083
226	mobilenet_v1_1.0_224	.081
227	mobilenet_v2_0.35_160	.081
228	squeezenet1_0	.081
229	mobilenet_v2_1.0_128-tf	.080
230	AdvProp_efficientnet-b8	.079
231	resnet50_eMMCR_Vanilla	.079
232	alexnet_early_checkpoint	.075
233	inception_v1	.074
234	resnet50-vicregl0p9	.072
235	mobilenet_v1_0.5_160	.072
236	mobilenet_v2_1_0_192	.071
237	mobilenet_v2_1.0_160-tf	.070
238	resnet50_FractalDB	.070
239	ReAlnet01	.070
240	mobilenet_v2_1.0_224-tf	.070
241	efficientnet_b0_imagenet_full	.069
242	voneresnet-50	.069
243	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.064
244	alexnet_training_seed_09	.058
245	mobilenet_v2_1.0_192-tf	.056
246	alexnet_training_seed_07	.054
247	CORnet-S	.052
248	mobilenet_v2_0.5_192-tf	.052
249	mobilenet_v2_1.4_224-tf	.050
250	mobilenet_v1_0.75_160	.050
251	resnet_50_v1_spiking_l4	.049
252	mobilenet_v2_0.5_160	.049
253	resnet50_imagenet_1_seed-0	.048
254	resnet-34	.047
255	mobilenet_v1_1.0_160	.046
256	mobilenet_v1_1.0_192	.045
257	resnet50-vicregl0p75	.044
258	efficientnet_b1_imagenet_full	.044
259	AdvProp_efficientnet-b0	.044
260	mobilenet_v2_0_75_192	.044
261	custom_model_cv_18_dagger_408	.044
262	mobilenet_v2_0.35_224	.043
263	mobilenet_v1_0.5_224	.043
264	resnet_50_v1_spiking_l3	.038
265	resnet50-barlow	.037
266	resnet50_imagenet_100_seed-0	.035
267	resnet_50_v1_spiking_l4a	.033
268	mobilenet_v2_0_75_160	.033
269	mobilenet_v1_0.75_128	.033
270	mobilenet_v2_0.75_128	.032
271	my-model	.030
272	mobilenet_v2_1_3_224	.025
273	convnext_small_imagenet_10_seed-0	.025
274	mobilenet_v1_0.75_192	.024
275	mobilenet_v2_0_75_224	.024
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	ReAlnet01_cornet
376	inception_v3_pytorch
377	yolos_tiny
378	yudixie_resnet50_imagenet1kpret_0_240312
379	yudixie_resnet18_translation_reg_0_240719
380	briaai_rmbg_1_4
381	yudixie_resnet18_distance_reg_0_240719
382	ReAlnet03_cornet
383	yudixie_resnet18_rotation_reg_0_240719
384	yudixie_resnet18_imagenet1kpret_0_240719
385	compact_resnet50_V4
386	ReAlnet02_cornet
387	resnet18_ecoset_full
388	resnet34_ecoset_full
389	VOneCORnet-S
390	resnet50_ecoset_full
391	compact_resnet50_robust_V4
392	resnet101_ecoset_full
393	yudixie_resnet50_distance_translation_0_240908
394	yudixie_resnet18_distance_translation_0_240719
395	ReAlnet04_cornet
396	yudixie_resnet18_translation_rotation_0_240719
397	yudixie_resnet18_category_class_0_240719
398	yudixie_resnet50_distance_rotation_0_240908
399	compact_vgg19_V4
400	compact_resnet50_IT
401	ReAlnet05_cornet
402	yudixie_resnet50_rotation_reg_0_240908
403	yudixie_resnet18_random_0_240719
404	yudixie_resnet18_cat_obj_class_all_latents_0_240719
405	resnet-18_test_m
406	SeLaVi-AVE
407	yudixie_resnet18_object_class_0_240719
408	yudixie_resnet50_translation_reg_0_240908
409	ReAlnet06_cornet
410	compact_vgg19_IT
411	r3d_18
412	yudixie_resnet50_translation_rotation_0_240908
413	mc3_18
414	ReAlnet01_ALL
415	ReAlnet07_cornet
416	s3d
417	mvit_v1_b
418	ReAlnet02_ALL
419	ReAlnet08_cornet
420	compact_cornets_V4
421	omnivore_swinB
422	ReAlnet04_ALL
423	SeLaVi-Kinetics400
424	ReAlnet09_cornet
425	SeLaVi-VGG-Sound
426	compact_cornets_IT
427	ReAlnet05_ALL
428	SeLaVi-Kinetics-Sound
429	omnivore_swinS
430	r2plus1d_18
431	mvit_v2_s
432	compact_resnet50_robust_IT
433	ReAlnet10_cornet
434	resnet152_ecoset_full
435	ReAlnet07_ALL
436	yudixie_resnet18_distance_rotation_0_240719
437	ReAlnet08_ALL
438	yudixie_resnet50_distance_translation_rotation_0_240908
439	yudixie_resnet18_distance_translation_rotation_0_240719
440	ReAlnet09_ALL
441	resnet50_primary_visual_cortex
442	ReAlnet10_ALL
443	ReAlnet03_ALL
444	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