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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-baseline	.123
169	alexnet	.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	texture_shape_resnet50_trained_on_SIN	.122
180	resnet50_l2_3_robust	.121
181	vgg_16	.121
182	resnet50-meshes-lt-100-original-scratch	.120
183	mobilenet_v1_0.25_192	.120
184	vonegrcnn_62e_nobn	.120
185	mobilenet_v2_0.5_128	.119
186	voneresnet-50-robust	.119
187	resnet50_linf_4_robust	.119
188	mobilevit_small	.118
189	alexnet_fov16	.118
190	alexnet_robust_correct	.117
191	resnet50_linf_8_robust	.116
192	alexnet_l2_3_robust	.115
193	vgg-19-keras	.114
194	vonegrcnn_62e_nobn	.113
195	resnet-50-robust	.113
196	imagenet_l2_3_0	.113
197	resnet50_robust_l2_eps3	.113
198	resnet50_robust_l2_eps1	.113
199	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.110
200	alexnet_linf_8_robust	.109
201	imagenet_l2_10_0	.108
202	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.108
203	ReAlnet09	.107
204	mobilenet_v2_1_0_128	.107
205	ReAlnet07	.107
206	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.106
207	resnet50_imagenet_10_seed-0	.106
208	deit_small_imagenet_10_seed-0	.106
209	deit_small_imagenet_1_seed-0	.106
210	mobilenet_v2_0_5_192	.105
211	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.103
212	ReAlnet06	.102
213	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.102
214	vonealexnet_gaussian_noise_std4_fixed	.101
215	alexnet_training_seed_05	.101
216	alexnet_training_seed_01	.101
217	alexnet_training_seed_01_fov4	.101
218	alexnet_training_seed_01_fov12	.101
219	alexnet_training_seed_01_fov16	.101
220	barlow-twins-resnet50	.099
221	ReAlnet10	.097
222	alexnet_fov12	.097
223	vonegrcnn_47e	.096
224	konkle_alexnetgn_ipcl_ref01_primary_model	.094
225	resnet50-vicreg	.094
226	mobilenet_v1_0.5_192	.094
227	mobilenet_v2_1.0_96	.093
228	mobilenet_v2_0.35_128	.091
229	mobilenet_v2_1_0_160	.088
230	mobilenet_v2_0.35_192	.088
231	resnet18_imagenet_full	.087
232	resnet50-SIN	.085
233	mobilenet_v1_0.5_128	.083
234	mobilenet_v1_1.0_224	.081
235	mobilenet_v2_0.35_160	.081
236	squeezenet1_0	.081
237	mobilenet_v2_1.0_128-tf	.080
238	AdvProp_efficientnet-b8	.079
239	resnet50_eMMCR_Vanilla	.079
240	alexnet_early_checkpoint	.075
241	inception_v1	.074
242	resnet50-vicregl0p9	.072
243	mobilenet_v1_0.5_160	.072
244	mobilenet_v2_1_0_192	.071
245	mobilenet_v2_1.0_160-tf	.070
246	resnet50_FractalDB	.070
247	ReAlnet01	.070
248	mobilenet_v2_1.0_224-tf	.070
249	efficientnet_b0_imagenet_full	.069
250	voneresnet-50	.069
251	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.064
252	alexnet_training_seed_09	.058
253	mobilenet_v2_1.0_192-tf	.056
254	alexnet_training_seed_07	.054
255	CORnet-S	.052
256	mobilenet_v2_0.5_192-tf	.052
257	mobilenet_v2_1.4_224-tf	.050
258	mobilenet_v1_0.75_160	.050
259	resnet_50_v1_spiking_l4	.049
260	mobilenet_v2_0.5_160	.049
261	resnet50_imagenet_1_seed-0	.048
262	resnet-34	.047
263	mobilenet_v1_1.0_160	.046
264	mobilenet_v1_1.0_192	.045
265	resnet50-vicregl0p75	.044
266	efficientnet_b1_imagenet_full	.044
267	AdvProp_efficientnet-b0	.044
268	mobilenet_v2_0_75_192	.044
269	custom_model_cv_18_dagger_408	.044
270	mobilenet_v2_0.35_224	.043
271	mobilenet_v1_0.5_224	.043
272	resnet_50_v1_spiking_l3	.038
273	resnet50-barlow	.037
274	resnet50_imagenet_100_seed-0	.035
275	resnet_50_v1_spiking_l4a	.033
276	mobilenet_v2_0_75_160	.033
277	mobilenet_v1_0.75_128	.033
278	mobilenet_v2_0.75_128	.032
279	my-model	.030
280	AlexNet_SIN_fov12	.027
281	mobilenet_v2_1_3_224	.025
282	convnext_small_imagenet_10_seed-0	.025
283	mobilenet_v1_0.75_192	.024
284	mobilenet_v2_0_75_224	.024
285	AlexNet_SIN_fov16	.023
286	AlexNet_SIN	.022
287	alexnet_training_seed_03	.022
288	mobilenet_v1_0.25_224	.022
289	deit_small_imagenet_100_seed-0	.021
290	mobilenet_v2_0.75_96	.020
291	squeezenet1_1	.020
292	convnext_small_imagenet_1_seed-0	.020
293	alexnet_training_seed_08	.020
294	mobilenet_v2_1.3_224-tf	.019
295	mobilenet_v2_0.75_160-tf	.018
296	artResNet18_1	.018
297	resnet50_random_linf8_perturb	.017
298	mobilenet_v2_0.5_96	.017
299	mobilenet_v1_1.0_128	.016
300	mobilenet_v1_0.25_160	.016
301	alexnet_training_seed_06	.016
302	texture_shape_alexnet_trained_on_SIN	.015
303	mobilenet_v2_0.35_96	.015
304	mobilenet_v2_0.75_224-tf	.014
305	mobilenet_v2_0.75_192-tf	.013
306	regnet_y_400mf	.013
307	resnet-50_untrained	.012
308	resnet50-meshes-lt-100-original-pretrained	.012
309	mobilenet_v1_0.75_224	.011
310	resnet50-simclr-vissl	.008
311	shufflenet_v2_x1_0	.006
312	0.5x_resnet-18_LC_w_sh_1_iter	.006
313	resnet-18-LC_w_sh_100_iter_conv_init_m
314	ViT-B/32
315	BiT-M-R50x3
316	BiT-M-R50x1
317	BiT-M-R152x2
318	resnet-18-LC_w_sh_1_iter_conv_init_m
319	resnet-18-LC_w_sh_1_iter_m
320	resnet-18-LC_w_sh_10_iter_m
321	resnet-18-LC_w_sh_100_iter_m
322	resnet-18-LC_w_sh_10_iter_conv_init_m
323	FrankRobWobv0
324	resnet18-local_aggregation
325	0.5x_resnet-18_LC
326	CORnetZ_CIFAR10
327	resnet18-deepcluster
328	resnet18-colorization
329	r3m_resnet18
330	CORnet-Z
331	resnet18-instance_recognition
332	my-model
333	vggface
334	0.5x_resnet-18_LC_w_sh_10_iter
335	r3m_resnet50
336	r3m_resnet34
337	resnet-18-LC_m
338	r3m_resnet50_nocrop
339	resnet18-supervised
340	resnet-18-LC_1st_conv_m
341	resnet-18-LC_conv_init_m
342	resnet18-contrastive_multiview
343	0.5x_resnet-18
344	RN50
345	0.5x_resnet-18_LC_w_sh_100_iter
346	RN50
347	resnet18-relative_position
348	resnet18-autoencoder
349	resnet18-simclr
350	ViT_L_32
351	ViT_B_32
352	resnet18-depth_prediction
353	ViT_B_16
354	r3m_resnet34_nocrop
355	hmax
356	resnet18-contrastive_predictive
357	omnivore_swinT
358	resnet-18-LC_untrained
359	resnet-18_untrained
360	resnet-50x2_untrained
361	dcgan
362	v1-pyr-nodown
363	unet_entire
364	CORnetZ_CIFAR10_bs32_20_04
365	pixels
366	dorinet_cornet_z
367	resnet50
368	openclip
369	resnet50-cifar
370	resnet50_pretrained_with_retinal_waves
371	resnet50_ImageNet
372	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
373	yudixie_resnet50_random_0_240908
374	yudixie_resnet50_imagenet1kpret_0_240908
375	yudixie_resnet50_cat_obj_class_all_latents_0_240908
376	yudixie_resnet50_object_class_0_240908
377	yudixie_resnet50_category_class_0_240908
378	resnet18_imagenet21kP
379	ConvLSTM
380	PredRNN
381	yudixie_resnet50_distance_reg_0_240908
382	SimVP
383	TAU
384	MIM
385	ReAlnet01_cornet
386	inception_v3_pytorch
387	yolos_tiny
388	yudixie_resnet50_imagenet1kpret_0_240312
389	yudixie_resnet18_translation_reg_0_240719
390	briaai_rmbg_1_4
391	yudixie_resnet18_distance_reg_0_240719
392	ReAlnet03_cornet
393	yudixie_resnet18_rotation_reg_0_240719
394	yudixie_resnet18_imagenet1kpret_0_240719
395	compact_resnet50_V4
396	ReAlnet02_cornet
397	resnet18_ecoset_full
398	resnet34_ecoset_full
399	VOneCORnet-S
400	resnet50_ecoset_full
401	compact_resnet50_robust_V4
402	resnet101_ecoset_full
403	ReAlnet01_cornet_fov12
404	yudixie_resnet50_distance_translation_0_240908
405	yudixie_resnet18_distance_translation_0_240719
406	ReAlnet04_cornet
407	yudixie_resnet18_translation_rotation_0_240719
408	yudixie_resnet18_category_class_0_240719
409	yudixie_resnet50_distance_rotation_0_240908
410	compact_vgg19_V4
411	compact_resnet50_IT
412	ReAlnet05_cornet
413	yudixie_resnet50_rotation_reg_0_240908
414	yudixie_resnet18_random_0_240719
415	yudixie_resnet18_cat_obj_class_all_latents_0_240719
416	resnet-18_test_m
417	ReAlnet01_cornet_fov4
418	SeLaVi-AVE
419	yudixie_resnet18_object_class_0_240719
420	yudixie_resnet50_translation_reg_0_240908
421	ReAlnet06_cornet
422	ReAlnet01_cornet_fov16
423	compact_vgg19_IT
424	r3d_18
425	yudixie_resnet50_translation_rotation_0_240908
426	mc3_18
427	ReAlnet01_ALL
428	ReAlnet07_cornet
429	s3d
430	mvit_v1_b
431	ReAlnet02_ALL
432	ReAlnet08_cornet
433	compact_cornets_V4
434	omnivore_swinB
435	ReAlnet04_ALL
436	SeLaVi-Kinetics400
437	ReAlnet09_cornet
438	SeLaVi-VGG-Sound
439	compact_cornets_IT
440	ReAlnet05_ALL
441	SeLaVi-Kinetics-Sound
442	omnivore_swinS
443	r2plus1d_18
444	mvit_v2_s
445	compact_resnet50_robust_IT
446	ReAlnet10_cornet
447	resnet152_ecoset_full
448	ReAlnet07_ALL
449	yudixie_resnet18_distance_rotation_0_240719
450	ReAlnet08_ALL
451	yudixie_resnet50_distance_translation_rotation_0_240908
452	yudixie_resnet18_distance_translation_rotation_0_240719
453	ReAlnet09_ALL
454	resnet50_primary_visual_cortex
455	ReAlnet10_ALL
456	ReAlnet03_ALL
457	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