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	cvt_cvt-21-224-in1k_4_LucyV4	.493
47	pnasnet_large	.493
48	ViT_B_16_imagenet1k	.476
49	effnetv2m_custom384	.475
50	pnasnet_large-tf	.473
51	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.462
52	resnet_50_v2	.458
53	ViT_B_32_imagenet1k	.455
54	convnext_large_imagenet_full_seed-0	.443
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.441
56	nasnet_large-tf	.418
57	inception_v4	.404
58	AdvProp_efficientnet-b6	.401
59	resnet-152_v2-tf	.386
60	resnet50-VITO-8deg-cc	.381
61	inception_v3	.377
62	cv_18_dagger_408_pretrained	.354
63	inception_v3-tf	.348
64	xception-keras	.344
65	resnet101	.340
66	inception_v4-tf	.330
67	deit_large_imagenet_full_seed-0	.328
68	xception	.322
69	AdvProp_efficientnet-b4	.309
70	AT_efficientnet-b4	.309
71	antialias-resnet152	.309
72	densenet_201_pytorch	.289
73	densenet-201	.289
74	effnetb1_272x240	.285
75	resnet-50_v2-tf	.284
76	antialiased-rnext101_32x8d	.279
77	densenet-201-keras	.272
78	resnet50-moclr8deg	.266
79	BiT-S-R50x3	.265
80	AdvProp_efficientnet-b7	.260
81	deit_base_imagenet_full_seed-0	.257
82	deit_small_imagenet_full_seed-0	.257
83	efficientnet-b7	.254
84	resnet_101_v1	.253
85	AT_efficientnet-b2	.250
86	AdvProp_efficientnet-b2	.250
87	efficientnet_b0	.249
88	resnet50_random_l2_perturb	.245
89	resnet50	.244
90	Res2Net50_26w_4s	.243
91	resnet50_byol	.231
92	resnet_152_v1	.231
93	antialiased-r50	.224
94	resnet50_moco_v2	.209
95	convnext_small_imagenet_100_seed-0	.202
96	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
97	BiT-S-R101x3	.194
98	resnet-50-pytorch	.194
99	resnet50-sup	.194
100	resnet50_tutorial	.194
101	resnet_50_v1	.194
102	BiT-S-R152x2	.188
103	convnext_tiny_sup	.188
104	vonegrcnn_62e_nobn	.183
105	grcnn	.182
106	AdvProp_efficientnet-b8	.180
107	nasnet_mobile	.179
108	vonegrcnn_47e	.174
109	densenet-121-keras	.173
110	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
111	BiT-S-R152x4	.172
112	densenet-121	.166
113	resnet_SIN_IN_FT_IN	.162
114	resnet50-SIN_IN_IN	.162
115	vonegrcnn_62e_nobn	.157
116	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
117	grcnn_109	.156
118	densenet-169	.153
119	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
120	vonegrcnn_52e_full	.144
121	densenet-169-keras	.143
122	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
123	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
124	BiT-S-R101x1	.134
125	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
126	voneresnet-50-non_stochastic	.132
127	resnet101_imagenet_full	.131
128	alexnet_training_seed_01	.130
129	alexnet_training_seed_05	.130
130	resnet152_imagenet_full	.129
131	vonegrcnn_47e	.127
132	resnet50-simclr-vissl	.127
133	vonegrcnn_52e_full	.125
134	resnet50_random_linf8_perturb	.122
135	BiT-S-R50x1	.121
136	voneresnet-50-robust	.121
137	mobilenet_v2_1_0_224	.121
138	resnet50-vitoimagevidnet8	.119
139	blt_vs	.119
140	resnet50-SIN_IN	.116
141	inception_v2-tf	.112
142	imagenet_l2_10_0	.105
143	resnet50_imagenet_full	.105
144	resnet50_simclr	.103
145	alexnet_robust_correct	.099
146	alexnet_l2_3_robust	.098
147	custom_model_cv_18_dagger_408	.097
148	vgg-19-keras	.091
149	mobilenet_v1_0.25_128	.090
150	resnet50_imagenet_10_seed-0	.089
151	deit_small_imagenet_10_seed-0	.089
152	deit_small_imagenet_1_seed-0	.089
153	resnet34_imagenet_full	.088
154	alexnet_training_seed_08	.088
155	alexnet_training_seed_10	.088
156	alexnet_training_seed_04	.088
157	alexnet_training_seed_02	.088
158	resnet50-meshes-lt-100-original-pretrained	.088
159	vgg_19	.088
160	inception_v1	.080
161	voneresnet-50	.079
162	konkle_alexnetgn_ipcl_ref01_primary_model	.079
163	nasnet_mobile-tf	.078
164	mobilenet_v2_0.5_160	.078
165	mobilenet_v1_0.5_128	.078
166	alexnet_training_seed_07	.077
167	mobilenet_v2_1_3_224	.076
168	mobilenet_v2_0.5_128	.075
169	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
170	mobilenet_v2_1_0_192	.075
171	deit_small_imagenet_100_seed-0	.073
172	AdvProp_efficientnet-b0	.073
173	ReAlnet04	.071
174	mobilenet_v2_0.35_160	.069
175	mobilenet_v2_0.35_192	.068
176	ReAlnet08	.068
177	inception_v1-tf	.067
178	mobilenet_v1_0.5_160	.067
179	ReAlnet03	.067
180	ReAlnet09	.064
181	mobilenet_v2_0.35_128	.062
182	mobilenet_v1_0.75_128	.061
183	squeezenet1_0	.059
184	mobilenet_v1_0.5_224	.059
185	mobilenet_v2_0.75_96	.058
186	convnext_small_imagenet_1_seed-0	.058
187	resnet50-vicregl0p9	.057
188	resnet50-barlow	.057
189	mobilenet_v2_0.5_192-tf	.057
190	mobilenet_v2_1_4_224	.057
191	mobilenet_v2_1-4_224_pytorch	.057
192	ReAlnet05	.056
193	mobilenet_v2_0.5_224-tf	.056
194	resnet50_imagenet_1_seed-0	.054
195	mobilenet_v1_0.25_192	.054
196	resnet50_linf_4_robust	.050
197	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
198	mobilenet_v1_0.25_160	.049
199	ReAlnet01	.049
200	ReAlnet06	.049
201	mobilenet_v2_0.35_224	.048
202	mobilenet_v2_0.75_192-tf	.048
203	ReAlnet07	.048
204	mobilenet_v2_1_0_160	.048
205	mobilenet_v2_0.75_224-tf	.047
206	alexnet_random_l2_3_perturb	.047
207	resnet-34	.047
208	mobilenet_v1_0.5_192	.046
209	mobilenet_v2_1.0_224-tf	.046
210	mobilenet_v1_0.75_160	.046
211	resnet50_l2_3_robust	.046
212	resnet-152_v1-tf	.045
213	vgg-16-keras	.045
214	resnet50_imagenet_100_seed-0	.045
215	resnet50_FractalDB	.044
216	mobilenet_v1_0.75_192	.044
217	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
218	alexnet_linf_8_robust	.043
219	alexnet_random_linf8_perturb	.041
220	resnet-101_v1-tf	.041
221	alexnet_early_checkpoint	.040
222	vgg_16	.039
223	resnet-18	.039
224	alexnet	.038
225	alexnet	.038
226	alexnet	.038
227	alexnet-baseline	.038
228	alexnet	.038
229	alexnet	.038
230	alexnet	.038
231	alexnet	.038
232	alexnet	.038
233	alexnet	.038
234	alexnet_ks_torevert	.038
235	resnet50_robust_l2_eps3	.038
236	resnet-50-robust	.038
237	imagenet_l2_3_0	.038
238	resnet18_imagenet_full	.038
239	mobilenet_v2_0.75_160-tf	.038
240	efficientnet_b1_imagenet_full	.036
241	mobilenet_v2_0.75_128	.036
242	mobilenet_v2_1.0_128-tf	.036
243	texture_shape_resnet50_trained_on_SIN	.036
244	mobilenet_v1_1.0_160	.036
245	efficientnet_b0_imagenet_full	.035
246	shufflenet_v2_x1_0	.034
247	alexnet	.034
248	resnet50_robust_l2_eps1	.033
249	mobilenet_v1_0.25_224	.033
250	alexnet_reduced_aliasing_early_checkpoint	.031
251	squeezenet1_1	.031
252	mobilenet_v1_1.0_192	.030
253	mobilenet_v2_1.0_96	.028
254	resnet50-meshes-lt-100-original-scratch	.028
255	alexnet_training_seed_09	.027
256	regnet_y_400mf	.027
257	resnet50_linf_8_robust	.026
258	alexnet_training_seed_03	.024
259	mobilenet_v2_0.5_96	.024
260	resnet50-SIN	.023
261	mobilenet_v2_1_0_128	.022
262	mobilenet_v1_1.0_224	.022
263	convnext_small_imagenet_10_seed-0	.022
264	resnet50-vicregl0p75	.022
265	resnet-50_untrained	.021
266	0.5x_resnet-18_LC_w_sh_1_iter	.021
267	mobilenet_v2_1.0_160-tf	.020
268	mobilenet_v1_0.75_224	.017
269	mobilenet_v1_1.0_128	.016
270	mobilenet_v2_0_75_224	.016
271	mobilenet_v2_0.35_96	.013
272	ReAlnet02	.011
273	cornet_s	.011
274	ReAlnet10	.011
275	mobilenet_v2_1.3_224-tf	.010
276	vonealexnet_gaussian_noise_std4_fixed	.010
277	mobilenet_v2_1.4_224-tf	.008
278	barlow-twins-resnet50	.006
279	mobilenet_v2_0_5_192	.006
280	efficientnet_b2_imagenet_full	.006
281	CORnet-S	.005
282	mobilenet_v2_0_75_160	.005
283	alexnet_training_seed_06	.005
284	texture_shape_alexnet_trained_on_SIN	.005
285	artResNet18_1	.004
286	mobilenet_v2_1.0_192-tf	.004
287	resnet50-vicreg	.003
288	mobilenet_v2_0_75_192	.003
289	AlexNet_SIN	.002
290	mobilenet_v2_0_5_224	.001
291	resnet-50_v1-tf	.000
292	yudixie_resnet18_random_0_240719	X
293	resnet-18-LC_w_sh_100_iter_conv_init_m	X
294	ViT-B/32	X
295	BiT-M-R50x3	X
296	BiT-M-R50x1	X
297	resnet-18-LC_w_sh_1_iter_conv_init_m	X
298	resnet-18-LC_w_sh_1_iter_m	X
299	BiT-M-R152x2	X
300	resnet-18-LC_w_sh_10_iter_m	X
301	resnet-18-LC_w_sh_100_iter_m	X
302	resnet-18-LC_w_sh_10_iter_conv_init_m	X
303	FrankRobWobv0	X
304	0.5x_resnet-18_LC	X
305	CORnetZ_CIFAR10	X
306	resnet18-local_aggregation	X
307	r3m_resnet18	X
308	CORnet-Z	X
309	resnet18-deepcluster	X
310	vggface	X
311	my-model	X
312	resnet18-instance_recognition	X
313	0.5x_resnet-18_LC_w_sh_10_iter	X
314	r3m_resnet50	X
315	r3m_resnet34	X
316	resnet-18-LC_m	X
317	r3m_resnet50_nocrop	X
318	resnet18-supervised	X
319	resnet-18-LC_1st_conv_m	X
320	resnet-18-LC_conv_init_m	X
321	resnet18-contrastive_multiview	X
322	0.5x_resnet-18	X
323	RN50	X
324	0.5x_resnet-18_LC_w_sh_100_iter	X
325	RN50	X
326	resnet18-colorization	X
327	resnet18-relative_position	X
328	resnet18-autoencoder	X
329	resnet18-simclr	X
330	ViT_L_32	X
331	ViT_B_32	X
332	resnet18-depth_prediction	X
333	ViT_B_16	X
334	r3m_resnet34_nocrop	X
335	resnet18-contrastive_predictive	X
336	resnet-18-LC_untrained	X
337	resnet-18_untrained	X
338	resnet-50x2_untrained	X
339	dcgan	X
340	unet_entire	X
341	v1-pyr-nodown	X
342	CORnetZ_CIFAR10_bs32_20_04	X
343	pixels	X
344	dorinet_cornet_z	X
345	omnivore_swinT	X
346	my-model	X
347	resnet50	X
348	openclip	X
349	resnet50-cifar	X
350	resnet50_pretrained_with_retinal_waves	X
351	resnet50_ImageNet	X
352	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4	X
353	yudixie_resnet50_random_0_240908	X
354	yudixie_resnet50_imagenet1kpret_0_240908	X
355	yudixie_resnet50_imagenet1kpret_0_240312	X
356	yudixie_resnet50_cat_obj_class_all_latents_0_240908	X
357	yudixie_resnet50_object_class_0_240908	X
358	yudixie_resnet50_category_class_0_240908	X
359	resnet18_imagenet21kP	X
360	yudixie_resnet50_distance_reg_0_240908	X
361	ConvLSTM	X
362	PredRNN	X
363	SimVP	X
364	TAU	X
365	MIM	X
366	hmax	X
367	pnasnet_large_pytorch	X
368	inception_v3_pytorch	X
369	ReAlnet01_cornet	X
370	yudixie_resnet18_translation_reg_0_240719	X
371	ReAlnet02_cornet	X
372	yudixie_resnet18_distance_reg_0_240719	X
373	yudixie_resnet18_rotation_reg_0_240719	X
374	yudixie_resnet18_imagenet1kpret_0_240719	X
375	resnet18_ecoset_full	X
376	ReAlnet03_cornet	X
377	resnet34_ecoset_full	X
378	VOneCORnet-S	X
379	resnet50_ecoset_full	X
380	resnet101_ecoset_full	X
381	yudixie_resnet50_distance_translation_0_240908	X
382	resnet152_ecoset_full	X
383	yudixie_resnet18_distance_translation_0_240719	X
384	yudixie_resnet18_translation_rotation_0_240719	X
385	ReAlnet04_cornet	X
386	omnivore_swinB	X
387	yudixie_resnet18_distance_rotation_0_240719	X
388	yudixie_resnet18_category_class_0_240719	X
389	yudixie_resnet50_rotation_reg_0_240908	X
390	ReAlnet05_cornet	X
391	resnet-18_test_m	X
392	yudixie_resnet50_distance_rotation_0_240908	X
393	yudixie_resnet18_cat_obj_class_all_latents_0_240719	X
394	yudixie_resnet18_distance_translation_rotation_0_240719	X
395	yudixie_resnet18_object_class_0_240719	X
396	ReAlnet06_cornet	X
397	yudixie_resnet50_translation_reg_0_240908	X
398	yudixie_resnet50_translation_rotation_0_240908	X
399	ReAlnet07_cornet	X
400	ReAlnet08_cornet	X
401	ReAlnet09_cornet	X
402	omnivore_swinS	X
403	ReAlnet10_cornet	X
404	yudixie_resnet50_distance_translation_rotation_0_240908	X

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