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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021lowpass-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	SWSL_resnext101_32x8d	.730
2	resnext101_32x32d_wsl	.695
3	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.690
4	convnext_large:fb_in22k_ft_in1k	.667
5	vit_large_patch14_clip_224:openai_ft_in1k	.663
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.648
7	fixres_resnext101_32x48d_wsl	.630
8	resnet50-VITO-8deg-cc	.625
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.623
10	SWSL_resnet50	.613
11	vit_large_patch14_clip_224:laion2b_ft_in1k	.603
12	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.602
13	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.600
14	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.589
15	resnext101_32x48d_wsl	.579
16	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.578
17	resnext101_32x8d_wsl	.563
18	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.554
19	CLIP_resnet50	.544
20	CLIP_resnet50_float32	.544
21	deit_large_imagenet_full_seed-0	.543
22	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.537
23	resnext101_32x16d_wsl	.535
24	vit_base_patch16_clip_224:openai_ft_in1k	.535
25	convnext_xlarge:fb_in22k_ft_in1k	.527
26	effnetv2m_custom384	.527
27	convnext_base:clip_laiona_augreg_ft_in1k_384	.516
28	vision_transformer_vit_large_patch16_224	.499
29	resnet_152_v2	.498
30	effnetb1_272x240	.497
31	CLIP_ViT-B_32	.496
32	cvt_cvt-13-384-in1k_4_LucyV4	.492
33	convnext_large_imagenet_full_seed-0	.471
34	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.463
35	vit_relpos_base_patch16_clsgap_224:sw_in1k	.463
36	deit_small_imagenet_full_seed-0	.460
37	deit_base_imagenet_full_seed-0	.460
38	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.454
39	focalnet_tiny_lrf_in1k	.436
40	convnext_tiny:in12k_ft_in1k	.435
41	CLIP-RN50	.419
42	convnext_tiny_imagenet_full_seed-0	.419
43	resnet_50_v2	.415
44	pnasnet_large	.410
45	ViT_B_16_imagenet1k	.409
46	ViT_L_32_imagenet1k	.406
47	inception_v4	.400
48	resnet_101_v2	.397
49	nasnet_large	.396
50	cvt_cvt-21-384-in1k_4_LucyV4	.391
51	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.385
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.382
53	convnext_small_imagenet_full_seed-0	.379
54	convnext_femto_ols:d1_in1k	.379
55	resnet-152_v2_pytorch	.371
56	cvt_cvt-21-224-in1k_4_LucyV4	.368
57	cvt_cvt-13-224-in1k_4_LucyV4	.361
58	convnext_base_imagenet_full_seed-0	.354
59	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.352
60	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.338
61	pnasnet_large-tf	.338
62	antialias-resnet152	.326
63	xception-keras	.317
64	cv_18_dagger_408_pretrained	.314
65	resnet101	.313
66	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.311
67	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.306
68	nasnet_large-tf	.305
69	inception_v3-tf	.299
70	inception_v4-tf	.295
71	resnet152_imagenet_full	.290
72	resnet-152_v2-tf	.285
73	resnet50_byol	.274
74	resnet_152_v1	.270
75	resnet_101_v1	.268
76	antialiased-rnext101_32x8d	.268
77	inception_v3	.260
78	convnext_tiny_sup	.260
79	AdvProp_efficientnet-b4	.258
80	AT_efficientnet-b4	.258
81	grcnn_109	.252
82	inception_v3_pytorch	.252
83	resnet50	.251
84	ViT_B_32_imagenet1k	.244
85	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.243
86	resnet50_moco_v2	.232
87	resnet50_ewconplus_wzc	.230
88	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.225
89	AdvProp_efficientnet-b6	.221
90	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.218
91	tv_efficientnet-b1	.215
92	densenet-121-keras	.214
93	antialiased-r50	.214
94	resnet50_imagenet_full	.213
95	voneresnet-50-non_stochastic	.209
96	convnext_small_imagenet_100_seed-0	.207
97	densenet-169-keras	.203
98	AdvProp_efficientnet-b7	.203
99	resnet34_imagenet_full	.203
100	resnet-50_v2-tf	.202
101	densenet-121	.201
102	densenet-201-keras	.197
103	grcnn	.194
104	resnet_50_v1_spiking	.190
105	resnet50-moclr8deg	.187
106	densenet-169	.186
107	mobilenet_v2_1_0_224	.186
108	resnet50_tutorial	.185
109	resnet_50_v1	.185
110	resnet-50-pytorch	.185
111	resnet50-sup	.185
112	efficientnet-b7	.185
113	vgg_19	.184
114	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.180
115	BiT-S-R152x4	.175
116	pnasnet_large_pytorch	.175
117	Res2Net50_26w_4s	.174
118	xception	.168
119	resnet-50_v1-tf	.167
120	resnet101_imagenet_full	.165
121	AT_efficientnet-b2	.165
122	AdvProp_efficientnet-b2	.165
123	resnet18_imagenet_full	.163
124	densenet-201	.161
125	densenet_201_pytorch	.161
126	ReAlnet04	.156
127	custom_model_cv_18_dagger_408	.156
128	resnet-101_v1-tf	.155
129	vonegrcnn_62e_nobn	.153
130	efficientnet_b1_imagenet_full	.153
131	efficientnet_b0	.152
132	inception_v1-tf	.144
133	deit_small_imagenet_100_seed-0	.143
134	vonegrcnn_47e	.141
135	efficientnet_b2_imagenet_full	.140
136	resnet50_random_linf8_perturb	.140
137	vonegrcnn_52e_full	.139
138	resnet-152_v1-tf	.138
139	mobilenet_v2_1.0_224-tf	.135
140	ReAlnet03	.135
141	nasnet_mobile	.133
142	mobilenet_v2_1.4_224-tf	.133
143	voneresnet-50	.132
144	ReAlnet09	.132
145	vonegrcnn_62e_nobn	.130
146	resnet50-SIN_IN	.129
147	BiT-S-R50x3	.129
148	blt_vs	.129
149	mobilenet_v2_1_0_192	.127
150	BiT-S-R101x3	.127
151	ReAlnet07	.127
152	vonegrcnn_52e_full	.126
153	vonegrcnn_47e	.126
154	ReAlnet08	.126
155	resnet50_random_l2_perturb	.126
156	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.125
157	inception_v2-tf	.124
158	cornet_s	.124
159	AdvProp_efficientnet-b8	.123
160	mobilenet_v2_1-4_224_pytorch	.122
161	mobilenet_v2_1_4_224	.122
162	ReAlnet02	.121
163	voneresnet-50-robust	.119
164	mobilenet_v2_0_5_192	.116
165	ReAlnet06	.115
166	nasnet_mobile-tf	.113
167	resnet50-vitoimagevidnet8	.113
168	mobilenet_v1_0.75_224	.112
169	ReAlnet10	.112
170	mobilenet_v2_0_5_224	.111
171	vgg-19-keras	.111
172	resnet50_l2_3_robust	.110
173	resnet50_linf_4_robust	.110
174	inception_v1	.109
175	resnet50-SIN_IN_IN	.109
176	resnet_SIN_IN_FT_IN	.109
177	mobilenet_v2_1.0_160-tf	.109
178	resnet_50_v1_spiking_l4a	.108
179	mobilenet_v2_1_0_160	.108
180	resnet50_linf_8_robust	.107
181	resnet-50-robust	.103
182	imagenet_l2_3_0	.103
183	resnet50_robust_l2_eps3	.103
184	vgg_16	.100
185	ReAlnet01	.100
186	BiT-S-R50x1	.100
187	CORnet-S	.097
188	vgg-16-keras	.097
189	efficientnet_b0_imagenet_full	.096
190	mobilenet_v2_1_3_224	.096
191	mobilenet_v2_0.5_192-tf	.096
192	mobilenet_v2_0.75_192-tf	.095
193	mobilenet_v1_1.0_160	.095
194	ReAlnet05	.094
195	mobilenet_v2_1.3_224-tf	.091
196	resnet50_eMMCR_Vanilla	.090
197	regnet_y_400mf	.087
198	imagenet_l2_10_0	.086
199	mobilenet_v2_1.0_192-tf	.085
200	resnet50_imagenet_10_seed-0	.085
201	deit_small_imagenet_10_seed-0	.085
202	deit_small_imagenet_1_seed-0	.085
203	mobilenet_v2_0.75_160-tf	.085
204	mobilenet_v2_0.5_224-tf	.084
205	mobilenet_v2_0_75_160	.084
206	resnet50_robust_l2_eps1	.082
207	mobilenet_v1_0.75_192	.081
208	alexnet_linf_8_robust	.081
209	resnet-18	.081
210	mobilenet_v2_0.75_96	.081
211	resnet-34	.080
212	alexnet	.079
213	convnext_small_imagenet_10_seed-0	.078
214	mobilenet_v2_1.0_96	.078
215	mobilenet_v2_0_75_192	.077
216	mobilenet_v2_0.35_224	.077
217	mobilenet_v2_0.5_96	.077
218	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
219	alexnet_training_seed_05	.074
220	alexnet_training_seed_01	.074
221	resnet50-meshes-lt-100-original-scratch	.074
222	mobilenet_v2_0.75_128	.072
223	texture_shape_resnet50_trained_on_SIN	.071
224	alexnet_random_l2_3_perturb	.070
225	alexnet_l2_3_robust	.070
226	mobilenet_v2_0.75_224-tf	.069
227	resnet-50_untrained	.067
228	vonealexnet_gaussian_noise_std4_fixed	.066
229	mobilenet_v2_1_0_128	.065
230	resnet50-SIN	.064
231	alexnet_reduced_aliasing_early_checkpoint	.063
232	mobilenet_v2_0.35_96	.061
233	mobilenet_v2_0_75_224	.061
234	mobilenet_v2_0.35_192	.061
235	alexnet_robust_correct	.060
236	shufflenet_v2_x1_0	.060
237	BiT-S-R152x2	.059
238	texture_shape_alexnet_trained_on_SIN	.059
239	alexnet_training_seed_03	.058
240	alexnet_training_seed_09	.057
241	squeezenet1_1	.057
242	mobilenet_v1_0.75_128	.056
243	alexnet_ks_torevert	.056
244	alexnet-baseline	.056
245	alexnet	.056
246	alexnet	.056
247	alexnet	.056
248	alexnet	.056
249	alexnet	.056
250	alexnet	.056
251	alexnet	.056
252	alexnet	.056
253	alexnet	.056
254	AdvProp_efficientnet-b0	.055
255	mobilenet_v1_0.5_224	.055
256	resnet50-simclr-vissl	.054
257	AlexNet_SIN	.054
258	barlow-twins-resnet50	.052
259	alexnet_random_linf8_perturb	.049
260	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.049
261	resnet50_simclr	.047
262	mobilenet_v1_1.0_224	.045
263	mobilenet_v1_1.0_192	.045
264	mobilenet_v2_0.35_128	.044
265	BiT-S-R101x1	.044
266	alexnet_early_checkpoint	.043
267	resnet50-vicreg	.038
268	resnet50-vicregl0p9	.038
269	mobilenet_v2_1.0_128-tf	.037
270	artResNet18_1	.032
271	resnet_50_v1_spiking_l4	.032
272	alexnet_training_seed_02	.031
273	alexnet_training_seed_08	.031
274	alexnet_training_seed_04	.031
275	alexnet_training_seed_10	.031
276	mobilenet_v2_0.5_160	.030
277	mobilenet_v2_0.35_160	.029
278	0.5x_resnet-18_LC_w_sh_1_iter	.028
279	mobilenet_v1_0.25_160	.026
280	resnet50_FractalDB	.026
281	resnet50_imagenet_1_seed-0	.025
282	resnet_50_v1_spiking_l3	.024
283	alexnet_training_seed_06	.024
284	mobilenet_v1_0.25_224	.022
285	mobilenet_v2_0.5_128	.020
286	mobilenet_v1_0.25_192	.019
287	mobilenet_v1_0.25_128	.017
288	resnet50-meshes-lt-100-original-pretrained	.016
289	mobilenet_v1_1.0_128	.016
290	squeezenet1_0	.014
291	mobilenet_v1_0.5_192	.013
292	alexnet_training_seed_07	.013
293	mobilevit_small	.013
294	mobilenet_v1_0.75_160	.013
295	konkle_alexnetgn_ipcl_ref01_primary_model	.013
296	resnet50_imagenet_100_seed-0	.007
297	mobilenet_v1_0.5_160	.006
298	resnet50-barlow	.006
299	convnext_small_imagenet_1_seed-0	.005
300	mobilenet_v1_0.5_128	.003
301	resnet50-vicregl0p75	.003
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_10_iter_m
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	resnet-18-LC_w_sh_1_iter_m
312	FrankRobWobv0
313	0.5x_resnet-18_LC
314	CORnetZ_CIFAR10
315	resnet18-local_aggregation
316	resnet18-deepcluster
317	r3m_resnet18
318	CORnet-Z
319	resnet18-instance_recognition
320	vggface
321	my-model
322	0.5x_resnet-18_LC_w_sh_10_iter
323	r3m_resnet50
324	resnet18-colorization
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	resnet18-contrastive_predictive
345	omnivore_swinT
346	resnet-18-LC_untrained
347	resnet-18_untrained
348	resnet-50x2_untrained
349	dcgan
350	unet_entire
351	v1-pyr-nodown
352	CORnetZ_CIFAR10_bs32_20_04
353	pixels
354	dorinet_cornet_z
355	my-model
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_imagenet1kpret_0_240312
366	yudixie_resnet50_category_class_0_240908
367	resnet18_imagenet21kP
368	yudixie_resnet50_distance_reg_0_240908
369	ConvLSTM
370	PredRNN
371	SimVP
372	TAU
373	MIM
374	hmax
375	ReAlnet01_cornet
376	yolos_tiny
377	resnet152_ecoset_full
378	compact_resnet50_V4
379	yudixie_resnet18_translation_reg_0_240719
380	yudixie_resnet18_distance_reg_0_240719
381	ReAlnet03_cornet
382	yudixie_resnet18_rotation_reg_0_240719
383	ReAlnet02_cornet
384	yudixie_resnet18_imagenet1kpret_0_240719
385	resnet34_ecoset_full
386	compact_resnet50_IT
387	resnet50_ecoset_full
388	resnet101_ecoset_full
389	compact_resnet50_robust_V4
390	yudixie_resnet50_distance_translation_0_240908
391	omnivore_swinB
392	yudixie_resnet18_distance_rotation_0_240719
393	yudixie_resnet50_distance_rotation_0_240908
394	yudixie_resnet18_category_class_0_240719
395	yudixie_resnet18_random_0_240719
396	ReAlnet05_cornet
397	yudixie_resnet18_cat_obj_class_all_latents_0_240719
398	yudixie_resnet50_rotation_reg_0_240908
399	resnet-18_test_m
400	compact_vgg19_V4
401	yudixie_resnet18_distance_translation_rotation_0_240719
402	yudixie_resnet18_object_class_0_240719
403	SeLaVi-AVE
404	r3d_18
405	ReAlnet06_cornet
406	yudixie_resnet50_translation_reg_0_240908
407	yudixie_resnet50_translation_rotation_0_240908
408	ReAlnet01_ALL
409	mc3_18
410	ReAlnet07_cornet
411	s3d
412	mvit_v1_b
413	ReAlnet08_cornet
414	ReAlnet02_ALL
415	ReAlnet03_ALL
416	VOneCORnet-S
417	compact_cornets_V4
418	ReAlnet04_ALL
419	ReAlnet09_cornet
420	SeLaVi-Kinetics400
421	compact_cornets_IT
422	SeLaVi-VGG-Sound
423	ReAlnet05_ALL
424	SeLaVi-Kinetics-Sound
425	compact_vgg19_IT
426	omnivore_swinS
427	mvit_v2_s
428	compact_resnet50_robust_IT
429	ReAlnet06_ALL
430	r2plus1d_18
431	ReAlnet10_cornet
432	ReAlnet07_ALL
433	resnet18_ecoset_full
434	yudixie_resnet50_distance_translation_rotation_0_240908
435	ReAlnet08_ALL
436	yudixie_resnet18_translation_rotation_0_240719
437	yudixie_resnet18_distance_translation_0_240719
438	resnet50_primary_visual_cortex
439	ReAlnet04_cornet
440	ReAlnet09_ALL
441	yudixie_resnet50_object_class_0_240908
442	briaai_rmbg_1_4
443	ReAlnet10_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

0.47.

Note that scores are relative to this ceiling.

Data: Geirhos2021lowpass

Metric: error_consistency