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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021uniformnoise-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.781
2	resnext101_32x32d_wsl	.754
3	resnext101_32x48d_wsl	.743
4	vit_base_patch16_clip_224:openai_ft_in1k	.734
5	resnext101_32x16d_wsl	.731
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.728
7	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.723
8	fixres_resnext101_32x48d_wsl	.720
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.719
10	convnext_large:fb_in22k_ft_in1k	.686
11	vision_transformer_vit_large_patch16_224	.685
12	SWSL_resnext101_32x8d	.675
13	resnet-152_v2_pytorch	.674
14	resnet_152_v2	.670
15	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.661
16	resnet_101_v2	.647
17	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.647
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.645
19	vit_large_patch14_clip_224:openai_ft_in1k	.639
20	resnext101_32x8d_wsl	.637
21	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.635
22	CLIP_resnet50	.627
23	CLIP_resnet50_float32	.627
24	convnext_femto_ols:d1_in1k	.617
25	cvt_cvt-13-384-in1k_4_LucyV4	.614
26	vit_large_patch14_clip_336:laion2b_ft_in1k	.604
27	convnext_xlarge:fb_in22k_ft_in1k	.601
28	focalnet_tiny_lrf_in1k	.601
29	SWSL_resnet50	.599
30	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.591
31	convnext_tiny:in12k_ft_in1k	.590
32	cvt_cvt-13-224-in1k_4_LucyV4	.587
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.583
34	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.577
35	convnext_base:clip_laiona_augreg_ft_in1k_384	.576
36	vit_relpos_base_patch16_clsgap_224:sw_in1k	.576
37	CLIP_ViT-B_32	.572
38	nasnet_large	.555
39	CLIP-RN50	.537
40	ViT_L_32_imagenet1k	.524
41	convnext_tiny_imagenet_full_seed-0	.522
42	convnext_base_imagenet_full_seed-0	.520
43	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.520
44	cvt_cvt-21-384-in1k_4_LucyV4	.515
45	convnext_small_imagenet_full_seed-0	.509
46	pnasnet_large_halfPrecision	.499
47	cvt_cvt-21-224-in1k_4_LucyV4	.493
48	pnasnet_large	.493
49	ViT_B_16_imagenet1k	.476
50	effnetv2m_custom384	.475
51	pnasnet_large-tf	.473
52	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.462
53	resnet_50_v2	.458
54	ViT_B_32_imagenet1k	.455
55	convnext_large_imagenet_full_seed-0	.443
56	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.441
57	nasnet_large-tf	.418
58	inception_v4	.404
59	AdvProp_efficientnet-b6	.401
60	resnet-152_v2-tf	.386
61	resnet50-VITO-8deg-cc	.381
62	inception_v3	.377
63	cv_18_dagger_408_pretrained	.354
64	inception_v3-tf	.348
65	xception-keras	.344
66	resnet101	.340
67	tv_efficientnet-b1	.338
68	inception_v4-tf	.330
69	deit_large_imagenet_full_seed-0	.328
70	xception	.322
71	AdvProp_efficientnet-b4	.309
72	AT_efficientnet-b4	.309
73	antialias-resnet152	.309
74	densenet-201	.289
75	densenet_201_pytorch	.289
76	effnetb1_272x240	.285
77	resnet-50_v2-tf	.284
78	resnet50_ewconplus_wzc	.282
79	antialiased-rnext101_32x8d	.279
80	densenet-201-keras	.272
81	resnet50-moclr8deg	.266
82	BiT-S-R50x3	.265
83	AdvProp_efficientnet-b7	.260
84	deit_base_imagenet_full_seed-0	.257
85	deit_small_imagenet_full_seed-0	.257
86	efficientnet-b7	.254
87	resnet_101_v1	.253
88	AT_efficientnet-b2	.250
89	AdvProp_efficientnet-b2	.250
90	efficientnet_b0	.249
91	resnet50_random_l2_perturb	.245
92	resnet50	.244
93	Res2Net50_26w_4s	.243
94	resnet50_byol	.231
95	resnet_152_v1	.231
96	antialiased-r50	.224
97	resnet_50_v1_spiking	.209
98	resnet50_moco_v2	.209
99	convnext_small_imagenet_100_seed-0	.202
100	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
101	BiT-S-R101x3	.194
102	resnet-50-pytorch	.194
103	resnet50-sup	.194
104	resnet50_tutorial	.194
105	resnet_50_v1	.194
106	BiT-S-R152x2	.188
107	convnext_tiny_sup	.188
108	vonegrcnn_62e_nobn	.183
109	grcnn	.182
110	AdvProp_efficientnet-b8	.180
111	nasnet_mobile	.179
112	vonegrcnn_47e	.174
113	densenet-121-keras	.173
114	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
115	BiT-S-R152x4	.172
116	densenet-121	.166
117	resnet50-SIN_IN_IN	.162
118	resnet_SIN_IN_FT_IN	.162
119	vonegrcnn_62e_nobn	.157
120	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
121	grcnn_109	.156
122	densenet-169	.153
123	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
124	vonegrcnn_52e_full	.144
125	densenet-169-keras	.143
126	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
127	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
128	BiT-S-R101x1	.134
129	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
130	voneresnet-50-non_stochastic	.132
131	resnet101_imagenet_full	.131
132	alexnet_training_seed_05	.130
133	alexnet_training_seed_01	.130
134	alexnet_training_seed_01_fov4	.130
135	alexnet_training_seed_01_fov12	.130
136	alexnet_training_seed_01_fov16	.130
137	resnet152_imagenet_full	.129
138	vonegrcnn_47e	.127
139	resnet50-simclr-vissl	.127
140	vonegrcnn_52e_full	.125
141	resnet50_random_linf8_perturb	.122
142	BiT-S-R50x1	.121
143	voneresnet-50-robust	.121
144	mobilenet_v2_1_0_224	.121
145	resnet50-vitoimagevidnet8	.119
146	blt_vs	.119
147	eBarlow_lmda_02_1000ep	.118
148	resnet50-SIN_IN	.116
149	inception_v2-tf	.112
150	imagenet_l2_10_0	.105
151	resnet50_imagenet_full	.105
152	resnet50_simclr	.103
153	alexnet_fov16	.102
154	alexnet_fov12	.101
155	alexnet_robust_correct	.099
156	alexnet_l2_3_robust	.098
157	custom_model_cv_18_dagger_408	.097
158	vgg-19-keras	.091
159	mobilenet_v1_0.25_128	.090
160	resnet50_imagenet_10_seed-0	.089
161	deit_small_imagenet_10_seed-0	.089
162	deit_small_imagenet_1_seed-0	.089
163	resnet34_imagenet_full	.088
164	alexnet_training_seed_02	.088
165	alexnet_training_seed_04	.088
166	alexnet_training_seed_08	.088
167	alexnet_training_seed_10	.088
168	resnet50-meshes-lt-100-original-pretrained	.088
169	vgg_19	.088
170	inception_v1	.080
171	voneresnet-50	.079
172	konkle_alexnetgn_ipcl_ref01_primary_model	.079
173	nasnet_mobile-tf	.078
174	mobilenet_v2_0.5_160	.078
175	mobilenet_v1_0.5_128	.078
176	alexnet_training_seed_07	.077
177	mobilenet_v2_1_3_224	.076
178	mobilenet_v2_0.5_128	.075
179	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
180	mobilenet_v2_1_0_192	.075
181	deit_small_imagenet_100_seed-0	.073
182	AdvProp_efficientnet-b0	.073
183	ReAlnet04	.071
184	mobilenet_v2_0.35_160	.069
185	mobilenet_v2_0.35_192	.068
186	ReAlnet08	.068
187	inception_v1-tf	.067
188	mobilenet_v1_0.5_160	.067
189	ReAlnet03	.067
190	ReAlnet09	.064
191	mobilenet_v2_0.35_128	.062
192	alexnet_fov4	.061
193	resnet50_eMMCR_Vanilla	.061
194	mobilenet_v1_0.75_128	.061
195	squeezenet1_0	.059
196	mobilenet_v1_0.5_224	.059
197	mobilenet_v2_0.75_96	.058
198	convnext_small_imagenet_1_seed-0	.058
199	AlexNet_SIN_fov4	.058
200	resnet50-vicregl0p9	.057
201	resnet50-barlow	.057
202	mobilenet_v2_0.5_192-tf	.057
203	mobilenet_v2_1-4_224_pytorch	.057
204	mobilenet_v2_1_4_224	.057
205	ReAlnet05	.056
206	mobilenet_v2_0.5_224-tf	.056
207	resnet50_imagenet_1_seed-0	.054
208	mobilenet_v1_0.25_192	.054
209	resnet50_linf_4_robust	.050
210	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
211	mobilenet_v1_0.25_160	.049
212	ReAlnet01	.049
213	ReAlnet06	.049
214	mobilenet_v2_0.35_224	.048
215	mobilenet_v2_0.75_192-tf	.048
216	ReAlnet07	.048
217	mobilenet_v2_1_0_160	.048
218	mobilenet_v2_0.75_224-tf	.047
219	alexnet_random_l2_3_perturb	.047
220	resnet-34	.047
221	mobilenet_v1_0.5_192	.046
222	mobilenet_v2_1.0_224-tf	.046
223	mobilenet_v1_0.75_160	.046
224	resnet50_l2_3_robust	.046
225	resnet_50_v1_spiking_l4	.046
226	resnet-152_v1-tf	.045
227	vgg-16-keras	.045
228	resnet50_imagenet_100_seed-0	.045
229	resnet50_FractalDB	.044
230	mobilenet_v1_0.75_192	.044
231	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
232	alexnet_linf_8_robust	.043
233	alexnet_random_linf8_perturb	.041
234	resnet-101_v1-tf	.041
235	alexnet_early_checkpoint	.040
236	vgg_16	.039
237	resnet-18	.039
238	alexnet	.038
239	alexnet_ks_torevert	.038
240	resnet-50-robust	.038
241	imagenet_l2_3_0	.038
242	resnet50_robust_l2_eps3	.038
243	resnet18_imagenet_full	.038
244	mobilenet_v2_0.75_160-tf	.038
245	efficientnet_b1_imagenet_full	.036
246	mobilenet_v2_0.75_128	.036
247	mobilenet_v2_1.0_128-tf	.036
248	texture_shape_resnet50_trained_on_SIN	.036
249	mobilenet_v1_1.0_160	.036
250	efficientnet_b0_imagenet_full	.035
251	shufflenet_v2_x1_0	.034
252	resnet_50_v1_spiking_l4a	.034
253	resnet50_robust_l2_eps1	.033
254	mobilenet_v1_0.25_224	.033
255	alexnet_reduced_aliasing_early_checkpoint	.031
256	squeezenet1_1	.031
257	mobilenet_v1_1.0_192	.030
258	mobilenet_v2_1.0_96	.028
259	resnet50-meshes-lt-100-original-scratch	.028
260	alexnet_training_seed_09	.027
261	regnet_y_400mf	.027
262	resnet50_linf_8_robust	.026
263	alexnet_training_seed_03	.024
264	mobilenet_v2_0.5_96	.024
265	resnet50-SIN	.023
266	mobilenet_v2_1_0_128	.022
267	mobilenet_v1_1.0_224	.022
268	convnext_small_imagenet_10_seed-0	.022
269	resnet50-vicregl0p75	.022
270	resnet-50_untrained	.021
271	0.5x_resnet-18_LC_w_sh_1_iter	.021
272	mobilenet_v2_1.0_160-tf	.020
273	mobilenet_v1_0.75_224	.017
274	mobilenet_v1_1.0_128	.016
275	mobilenet_v2_0_75_224	.016
276	AlexNet_SIN_fov12	.014
277	mobilenet_v2_0.35_96	.013
278	AlexNet_SIN_fov16	.012
279	ReAlnet02	.011
280	cornet_s	.011
281	ReAlnet10	.011
282	mobilenet_v2_1.3_224-tf	.010
283	vonealexnet_gaussian_noise_std4_fixed	.010
284	resnet_50_v1_spiking_l3	.009
285	mobilenet_v2_1.4_224-tf	.008
286	barlow-twins-resnet50	.006
287	mobilenet_v2_0_5_192	.006
288	efficientnet_b2_imagenet_full	.006
289	CORnet-S	.005
290	mobilenet_v2_0_75_160	.005
291	alexnet_training_seed_06	.005
292	texture_shape_alexnet_trained_on_SIN	.005
293	artResNet18_1	.004
294	mobilenet_v2_1.0_192-tf	.004
295	resnet50-vicreg	.003
296	mobilenet_v2_0_75_192	.003
297	AlexNet_SIN	.002
298	mobilenet_v2_0_5_224	.001
299	resnet-50_v1-tf	.000
300	resnet-18-LC_w_sh_100_iter_conv_init_m
301	ViT-B/32
302	BiT-M-R50x3
303	BiT-M-R50x1
304	resnet-18-LC_w_sh_1_iter_conv_init_m
305	resnet-18-LC_w_sh_1_iter_m
306	BiT-M-R152x2
307	resnet-18-LC_w_sh_10_iter_m
308	ReAlnet09_ALL
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	FrankRobWobv0
312	0.5x_resnet-18_LC
313	CORnetZ_CIFAR10
314	resnet18-local_aggregation
315	r3m_resnet18
316	CORnet-Z
317	resnet18-deepcluster
318	vggface
319	my-model
320	resnet18-instance_recognition
321	0.5x_resnet-18_LC_w_sh_10_iter
322	r3m_resnet50
323	r3m_resnet34
324	resnet-18-LC_m
325	r3m_resnet50_nocrop
326	resnet18-supervised
327	resnet-18-LC_1st_conv_m
328	resnet-18-LC_conv_init_m
329	resnet18-contrastive_multiview
330	0.5x_resnet-18
331	RN50
332	0.5x_resnet-18_LC_w_sh_100_iter
333	RN50
334	resnet18-colorization
335	resnet18-relative_position
336	resnet18-autoencoder
337	resnet18-simclr
338	ViT_L_32
339	ViT_B_32
340	resnet18-depth_prediction
341	ViT_B_16
342	r3m_resnet34_nocrop
343	resnet18-contrastive_predictive
344	resnet-18-LC_untrained
345	resnet-18_untrained
346	resnet-50x2_untrained
347	dcgan
348	unet_entire
349	v1-pyr-nodown
350	CORnetZ_CIFAR10_bs32_20_04
351	pixels
352	dorinet_cornet_z
353	omnivore_swinT
354	my-model
355	resnet50
356	openclip
357	resnet50-cifar
358	resnet50_pretrained_with_retinal_waves
359	resnet50_ImageNet
360	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
361	ReAlnet08_ALL
362	yudixie_resnet50_random_0_240908
363	yudixie_resnet50_imagenet1kpret_0_240908
364	yudixie_resnet50_imagenet1kpret_0_240312
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	yudixie_resnet50_distance_reg_0_240908
370	ConvLSTM
371	PredRNN
372	SimVP
373	TAU
374	vit_base_patch16_dinov3-lvd1689m
375	MIM
376	hmax
377	briaai_rmbg_1_4
378	pnasnet_large_pytorch
379	inception_v3_pytorch
380	vit_base_patch14_reg4_dinov2-lvd142m
381	ReAlnet01_cornet
382	yolos_tiny
383	yudixie_resnet18_translation_reg_0_240719
384	ReAlnet02_cornet
385	yudixie_resnet18_distance_reg_0_240719
386	yudixie_resnet18_rotation_reg_0_240719
387	yudixie_resnet18_imagenet1kpret_0_240719
388	compact_resnet50_V4
389	vit_base_patch14_dinov2-lvd142m
390	compact_resnet50_IT
391	resnet18_ecoset_full
392	ReAlnet03_cornet
393	ReAlnet01_cornet_fov4
394	resnet34_ecoset_full
395	VOneCORnet-S
396	resnet50_ecoset_full
397	resnet101_ecoset_full
398	aughost_fair_resnet18
399	yudixie_resnet50_distance_translation_0_240908
400	compact_vgg19_V4
401	yudixie_resnet18_distance_translation_0_240719
402	ReAlnet01_cornet_fov12
403	yudixie_resnet18_translation_rotation_0_240719
404	ReAlnet04_cornet
405	omnivore_swinB
406	yudixie_resnet18_distance_rotation_0_240719
407	yudixie_resnet18_category_class_0_240719
408	yudixie_resnet50_rotation_reg_0_240908
409	ReAlnet05_cornet
410	yudixie_resnet18_random_0_240719
411	resnet-18_test_m
412	yudixie_resnet50_distance_rotation_0_240908
413	yudixie_resnet18_cat_obj_class_all_latents_0_240719
414	SeLaVi-AVE
415	yudixie_resnet18_distance_translation_rotation_0_240719
416	yudixie_resnet18_object_class_0_240719
417	compact_resnet50_robust_V4
418	compact_vgg19_IT
419	ReAlnet06_cornet
420	yudixie_resnet50_translation_reg_0_240908
421	r3d_18
422	ReAlnet07_cornet
423	mc3_18
424	ReAlnet02_ALL
425	s3d
426	ReAlnet08_cornet
427	mvit_v1_b
428	ReAlnet04_ALL
429	compact_cornets_V4
430	ReAlnet01_cornet_fov16
431	ReAlnet03_ALL
432	compact_cornets_IT
433	ReAlnet09_cornet
434	SeLaVi-Kinetics400
435	ReAlnet05_ALL
436	SeLaVi-VGG-Sound
437	ReAlnet06_ALL
438	omnivore_swinS
439	r2plus1d_18
440	compact_resnet50_robust_IT
441	ReAlnet07_ALL
442	mvit_v2_s
443	ReAlnet10_cornet
444	SeLaVi-Kinetics-Sound
445	resnet152_ecoset_full
446	yudixie_resnet50_distance_translation_rotation_0_240908
447	yudixie_resnet50_translation_rotation_0_240908
448	resnet50_primary_visual_cortex
449	ReAlnet10_ALL
450	ReAlnet01_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

0.43.

Note that scores are relative to this ceiling.

Data: Geirhos2021uniformnoise

Metric: error_consistency