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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	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	resnet50-SIN_IN_IN	.229
101	resnet_SIN_IN_FT_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_pytorch	.166
133	mobilenet_v2_1_4_224	.166
134	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
135	BiT-S-R50x1	.158
136	nasnet_mobile-tf	.158
137	alexnet_training_seed_02	.156
138	alexnet_training_seed_04	.156
139	alexnet_training_seed_10	.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-baseline	.123
166	alexnet	.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	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	resnet-50-robust	.113
191	imagenet_l2_3_0	.113
192	resnet50_robust_l2_eps3	.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	resnet50_imagenet_10_seed-0	.106
203	deit_small_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_05	.101
211	alexnet_training_seed_01	.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	resnet50_eMMCR_Vanilla	.079
231	alexnet_early_checkpoint	.075
232	inception_v1	.074
233	resnet50-vicregl0p9	.072
234	mobilenet_v1_0.5_160	.072
235	mobilenet_v2_1_0_192	.071
236	mobilenet_v2_1.0_160-tf	.070
237	resnet50_FractalDB	.070
238	ReAlnet01	.070
239	mobilenet_v2_1.0_224-tf	.070
240	efficientnet_b0_imagenet_full	.069
241	voneresnet-50	.069
242	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.064
243	alexnet_training_seed_09	.058
244	mobilenet_v2_1.0_192-tf	.056
245	alexnet_training_seed_07	.054
246	CORnet-S	.052
247	mobilenet_v2_0.5_192-tf	.052
248	mobilenet_v2_1.4_224-tf	.050
249	mobilenet_v1_0.75_160	.050
250	resnet_50_v1_spiking_l4	.049
251	mobilenet_v2_0.5_160	.049
252	resnet50_imagenet_1_seed-0	.048
253	resnet-34	.047
254	mobilenet_v1_1.0_160	.046
255	mobilenet_v1_1.0_192	.045
256	resnet50-vicregl0p75	.044
257	efficientnet_b1_imagenet_full	.044
258	AdvProp_efficientnet-b0	.044
259	mobilenet_v2_0_75_192	.044
260	custom_model_cv_18_dagger_408	.044
261	mobilenet_v2_0.35_224	.043
262	mobilenet_v1_0.5_224	.043
263	resnet_50_v1_spiking_l3	.038
264	resnet50-barlow	.037
265	resnet50_imagenet_100_seed-0	.035
266	resnet_50_v1_spiking_l4a	.033
267	mobilenet_v2_0_75_160	.033
268	mobilenet_v1_0.75_128	.033
269	mobilenet_v2_0.75_128	.032
270	my-model	.030
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	.022
276	alexnet_training_seed_03	.022
277	mobilenet_v1_0.25_224	.022
278	deit_small_imagenet_100_seed-0	.021
279	mobilenet_v2_0.75_96	.020
280	squeezenet1_1	.020
281	convnext_small_imagenet_1_seed-0	.020
282	alexnet_training_seed_08	.020
283	mobilenet_v2_1.3_224-tf	.019
284	mobilenet_v2_0.75_160-tf	.018
285	artResNet18_1	.018
286	resnet50_random_linf8_perturb	.017
287	mobilenet_v2_0.5_96	.017
288	mobilenet_v1_1.0_128	.016
289	mobilenet_v1_0.25_160	.016
290	alexnet_training_seed_06	.016
291	texture_shape_alexnet_trained_on_SIN	.015
292	mobilenet_v2_0.35_96	.015
293	mobilenet_v2_0.75_224-tf	.014
294	mobilenet_v2_0.75_192-tf	.013
295	regnet_y_400mf	.013
296	resnet-50_untrained	.012
297	resnet50-meshes-lt-100-original-pretrained	.012
298	mobilenet_v1_0.75_224	.011
299	resnet50-simclr-vissl	.008
300	shufflenet_v2_x1_0	.006
301	0.5x_resnet-18_LC_w_sh_1_iter	.006
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	ViT-B/32
304	BiT-M-R50x3
305	BiT-M-R50x1
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_1_iter_conv_init_m
308	resnet-18-LC_w_sh_1_iter_m
309	resnet-18-LC_w_sh_10_iter_m
310	resnet-18-LC_w_sh_100_iter_m
311	resnet-18-LC_w_sh_10_iter_conv_init_m
312	FrankRobWobv0
313	resnet18-local_aggregation
314	0.5x_resnet-18_LC
315	CORnetZ_CIFAR10
316	resnet18-deepcluster
317	resnet18-colorization
318	r3m_resnet18
319	CORnet-Z
320	resnet18-instance_recognition
321	my-model
322	vggface
323	0.5x_resnet-18_LC_w_sh_10_iter
324	r3m_resnet50
325	r3m_resnet34
326	resnet-18-LC_m
327	r3m_resnet50_nocrop
328	resnet18-supervised
329	resnet-18-LC_1st_conv_m
330	resnet-18-LC_conv_init_m
331	resnet18-contrastive_multiview
332	0.5x_resnet-18
333	RN50
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	resnet18-relative_position
337	resnet18-autoencoder
338	resnet18-simclr
339	ViT_L_32
340	ViT_B_32
341	resnet18-depth_prediction
342	ViT_B_16
343	r3m_resnet34_nocrop
344	hmax
345	resnet18-contrastive_predictive
346	omnivore_swinT
347	resnet-18-LC_untrained
348	resnet-18_untrained
349	resnet-50x2_untrained
350	dcgan
351	v1-pyr-nodown
352	unet_entire
353	CORnetZ_CIFAR10_bs32_20_04
354	pixels
355	dorinet_cornet_z
356	resnet50
357	openclip
358	resnet50-cifar
359	resnet50_pretrained_with_retinal_waves
360	resnet50_ImageNet
361	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
362	yudixie_resnet50_random_0_240908
363	yudixie_resnet50_imagenet1kpret_0_240908
364	yudixie_resnet50_cat_obj_class_all_latents_0_240908
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_category_class_0_240908
367	resnet18_imagenet21kP
368	ConvLSTM
369	PredRNN
370	yudixie_resnet50_distance_reg_0_240908
371	SimVP
372	TAU
373	MIM
374	ReAlnet01_cornet
375	inception_v3_pytorch
376	yolos_tiny
377	yudixie_resnet50_imagenet1kpret_0_240312
378	yudixie_resnet18_translation_reg_0_240719
379	briaai_rmbg_1_4
380	yudixie_resnet18_distance_reg_0_240719
381	ReAlnet03_cornet
382	yudixie_resnet18_rotation_reg_0_240719
383	yudixie_resnet18_imagenet1kpret_0_240719
384	ReAlnet02_cornet
385	resnet18_ecoset_full
386	resnet34_ecoset_full
387	VOneCORnet-S
388	resnet50_ecoset_full
389	resnet101_ecoset_full
390	yudixie_resnet50_distance_translation_0_240908
391	yudixie_resnet18_distance_translation_0_240719
392	ReAlnet04_cornet
393	yudixie_resnet18_translation_rotation_0_240719
394	yudixie_resnet18_category_class_0_240719
395	yudixie_resnet50_distance_rotation_0_240908
396	ReAlnet05_cornet
397	yudixie_resnet50_rotation_reg_0_240908
398	yudixie_resnet18_random_0_240719
399	yudixie_resnet18_cat_obj_class_all_latents_0_240719
400	resnet-18_test_m
401	SeLaVi-AVE
402	yudixie_resnet18_object_class_0_240719
403	yudixie_resnet50_translation_reg_0_240908
404	ReAlnet06_cornet
405	r3d_18
406	yudixie_resnet50_translation_rotation_0_240908
407	mc3_18
408	ReAlnet01_ALL
409	ReAlnet07_cornet
410	s3d
411	mvit_v1_b
412	ReAlnet02_ALL
413	ReAlnet08_cornet
414	omnivore_swinB
415	ReAlnet04_ALL
416	SeLaVi-Kinetics400
417	ReAlnet09_cornet
418	SeLaVi-VGG-Sound
419	ReAlnet05_ALL
420	SeLaVi-Kinetics-Sound
421	omnivore_swinS
422	r2plus1d_18
423	mvit_v2_s
424	ReAlnet10_cornet
425	resnet152_ecoset_full
426	ReAlnet07_ALL
427	yudixie_resnet18_distance_rotation_0_240719
428	ReAlnet08_ALL
429	yudixie_resnet50_distance_translation_rotation_0_240908
430	yudixie_resnet18_distance_translation_rotation_0_240719
431	ReAlnet09_ALL
432	resnet50_primary_visual_cortex
433	ReAlnet10_ALL
434	ReAlnet03_ALL
435	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