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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	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	tv_efficientnet-b1	.338
67	inception_v4-tf	.330
68	deit_large_imagenet_full_seed-0	.328
69	xception	.322
70	AdvProp_efficientnet-b4	.309
71	AT_efficientnet-b4	.309
72	antialias-resnet152	.309
73	densenet-201	.289
74	densenet_201_pytorch	.289
75	effnetb1_272x240	.285
76	resnet-50_v2-tf	.284
77	antialiased-rnext101_32x8d	.279
78	densenet-201-keras	.272
79	resnet50-moclr8deg	.266
80	BiT-S-R50x3	.265
81	AdvProp_efficientnet-b7	.260
82	deit_base_imagenet_full_seed-0	.257
83	deit_small_imagenet_full_seed-0	.257
84	efficientnet-b7	.254
85	resnet_101_v1	.253
86	AT_efficientnet-b2	.250
87	AdvProp_efficientnet-b2	.250
88	efficientnet_b0	.249
89	resnet50_random_l2_perturb	.245
90	resnet50	.244
91	Res2Net50_26w_4s	.243
92	resnet50_byol	.231
93	resnet_152_v1	.231
94	antialiased-r50	.224
95	resnet_50_v1_spiking	.209
96	resnet50_moco_v2	.209
97	convnext_small_imagenet_100_seed-0	.202
98	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
99	BiT-S-R101x3	.194
100	resnet-50-pytorch	.194
101	resnet50-sup	.194
102	resnet50_tutorial	.194
103	resnet_50_v1	.194
104	BiT-S-R152x2	.188
105	convnext_tiny_sup	.188
106	vonegrcnn_62e_nobn	.183
107	grcnn	.182
108	AdvProp_efficientnet-b8	.180
109	nasnet_mobile	.179
110	vonegrcnn_47e	.174
111	densenet-121-keras	.173
112	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
113	BiT-S-R152x4	.172
114	densenet-121	.166
115	resnet50-SIN_IN_IN	.162
116	resnet_SIN_IN_FT_IN	.162
117	vonegrcnn_62e_nobn	.157
118	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
119	grcnn_109	.156
120	densenet-169	.153
121	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
122	vonegrcnn_52e_full	.144
123	densenet-169-keras	.143
124	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
125	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
126	BiT-S-R101x1	.134
127	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
128	voneresnet-50-non_stochastic	.132
129	resnet101_imagenet_full	.131
130	alexnet_training_seed_05	.130
131	alexnet_training_seed_01	.130
132	resnet152_imagenet_full	.129
133	vonegrcnn_47e	.127
134	resnet50-simclr-vissl	.127
135	vonegrcnn_52e_full	.125
136	resnet50_random_linf8_perturb	.122
137	BiT-S-R50x1	.121
138	voneresnet-50-robust	.121
139	mobilenet_v2_1_0_224	.121
140	resnet50-vitoimagevidnet8	.119
141	blt_vs	.119
142	resnet50-SIN_IN	.116
143	inception_v2-tf	.112
144	imagenet_l2_10_0	.105
145	resnet50_imagenet_full	.105
146	resnet50_simclr	.103
147	alexnet_robust_correct	.099
148	alexnet_l2_3_robust	.098
149	custom_model_cv_18_dagger_408	.097
150	vgg-19-keras	.091
151	mobilenet_v1_0.25_128	.090
152	resnet50_imagenet_10_seed-0	.089
153	deit_small_imagenet_10_seed-0	.089
154	deit_small_imagenet_1_seed-0	.089
155	resnet34_imagenet_full	.088
156	alexnet_training_seed_02	.088
157	alexnet_training_seed_04	.088
158	alexnet_training_seed_08	.088
159	alexnet_training_seed_10	.088
160	resnet50-meshes-lt-100-original-pretrained	.088
161	vgg_19	.088
162	inception_v1	.080
163	voneresnet-50	.079
164	konkle_alexnetgn_ipcl_ref01_primary_model	.079
165	nasnet_mobile-tf	.078
166	mobilenet_v2_0.5_160	.078
167	mobilenet_v1_0.5_128	.078
168	alexnet_training_seed_07	.077
169	mobilenet_v2_1_3_224	.076
170	mobilenet_v2_0.5_128	.075
171	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
172	mobilenet_v2_1_0_192	.075
173	deit_small_imagenet_100_seed-0	.073
174	AdvProp_efficientnet-b0	.073
175	ReAlnet04	.071
176	mobilenet_v2_0.35_160	.069
177	mobilenet_v2_0.35_192	.068
178	ReAlnet08	.068
179	inception_v1-tf	.067
180	mobilenet_v1_0.5_160	.067
181	ReAlnet03	.067
182	ReAlnet09	.064
183	mobilenet_v2_0.35_128	.062
184	resnet50_eMMCR_Vanilla	.061
185	mobilenet_v1_0.75_128	.061
186	squeezenet1_0	.059
187	mobilenet_v1_0.5_224	.059
188	mobilenet_v2_0.75_96	.058
189	convnext_small_imagenet_1_seed-0	.058
190	resnet50-vicregl0p9	.057
191	resnet50-barlow	.057
192	mobilenet_v2_0.5_192-tf	.057
193	mobilenet_v2_1-4_224_pytorch	.057
194	mobilenet_v2_1_4_224	.057
195	ReAlnet05	.056
196	mobilenet_v2_0.5_224-tf	.056
197	resnet50_imagenet_1_seed-0	.054
198	mobilenet_v1_0.25_192	.054
199	resnet50_linf_4_robust	.050
200	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
201	mobilenet_v1_0.25_160	.049
202	ReAlnet01	.049
203	ReAlnet06	.049
204	mobilenet_v2_0.35_224	.048
205	mobilenet_v2_0.75_192-tf	.048
206	ReAlnet07	.048
207	mobilenet_v2_1_0_160	.048
208	mobilenet_v2_0.75_224-tf	.047
209	alexnet_random_l2_3_perturb	.047
210	resnet-34	.047
211	mobilenet_v1_0.5_192	.046
212	mobilenet_v2_1.0_224-tf	.046
213	mobilenet_v1_0.75_160	.046
214	resnet50_l2_3_robust	.046
215	resnet_50_v1_spiking_l4	.046
216	resnet-152_v1-tf	.045
217	vgg-16-keras	.045
218	resnet50_imagenet_100_seed-0	.045
219	resnet50_FractalDB	.044
220	mobilenet_v1_0.75_192	.044
221	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
222	alexnet_linf_8_robust	.043
223	alexnet_random_linf8_perturb	.041
224	resnet-101_v1-tf	.041
225	alexnet_early_checkpoint	.040
226	vgg_16	.039
227	resnet-18	.039
228	alexnet-baseline	.038
229	alexnet	.038
230	alexnet	.038
231	alexnet	.038
232	alexnet	.038
233	alexnet	.038
234	alexnet	.038
235	alexnet	.038
236	alexnet	.038
237	alexnet	.038
238	alexnet_ks_torevert	.038
239	resnet-50-robust	.038
240	imagenet_l2_3_0	.038
241	resnet50_robust_l2_eps3	.038
242	resnet18_imagenet_full	.038
243	mobilenet_v2_0.75_160-tf	.038
244	efficientnet_b1_imagenet_full	.036
245	mobilenet_v2_0.75_128	.036
246	mobilenet_v2_1.0_128-tf	.036
247	texture_shape_resnet50_trained_on_SIN	.036
248	mobilenet_v1_1.0_160	.036
249	efficientnet_b0_imagenet_full	.035
250	shufflenet_v2_x1_0	.034
251	resnet_50_v1_spiking_l4a	.034
252	alexnet	.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	mobilenet_v2_0.35_96	.013
277	ReAlnet02	.011
278	cornet_s	.011
279	ReAlnet10	.011
280	mobilenet_v2_1.3_224-tf	.010
281	vonealexnet_gaussian_noise_std4_fixed	.010
282	resnet_50_v1_spiking_l3	.009
283	mobilenet_v2_1.4_224-tf	.008
284	barlow-twins-resnet50	.006
285	mobilenet_v2_0_5_192	.006
286	efficientnet_b2_imagenet_full	.006
287	CORnet-S	.005
288	mobilenet_v2_0_75_160	.005
289	alexnet_training_seed_06	.005
290	texture_shape_alexnet_trained_on_SIN	.005
291	artResNet18_1	.004
292	mobilenet_v2_1.0_192-tf	.004
293	resnet50-vicreg	.003
294	mobilenet_v2_0_75_192	.003
295	AlexNet_SIN	.002
296	mobilenet_v2_0_5_224	.001
297	resnet-50_v1-tf	.000
298	resnet-18-LC_w_sh_100_iter_conv_init_m
299	ViT-B/32
300	BiT-M-R50x3
301	BiT-M-R50x1
302	resnet-18-LC_w_sh_1_iter_conv_init_m
303	resnet-18-LC_w_sh_1_iter_m
304	BiT-M-R152x2
305	resnet-18-LC_w_sh_10_iter_m
306	ReAlnet09_ALL
307	resnet-18-LC_w_sh_100_iter_m
308	resnet-18-LC_w_sh_10_iter_conv_init_m
309	FrankRobWobv0
310	0.5x_resnet-18_LC
311	CORnetZ_CIFAR10
312	resnet18-local_aggregation
313	r3m_resnet18
314	CORnet-Z
315	resnet18-deepcluster
316	vggface
317	my-model
318	resnet18-instance_recognition
319	0.5x_resnet-18_LC_w_sh_10_iter
320	r3m_resnet50
321	r3m_resnet34
322	resnet-18-LC_m
323	r3m_resnet50_nocrop
324	resnet18-supervised
325	resnet-18-LC_1st_conv_m
326	resnet-18-LC_conv_init_m
327	resnet18-contrastive_multiview
328	0.5x_resnet-18
329	RN50
330	0.5x_resnet-18_LC_w_sh_100_iter
331	RN50
332	resnet18-colorization
333	resnet18-relative_position
334	resnet18-autoencoder
335	resnet18-simclr
336	ViT_L_32
337	ViT_B_32
338	resnet18-depth_prediction
339	ViT_B_16
340	r3m_resnet34_nocrop
341	resnet18-contrastive_predictive
342	resnet-18-LC_untrained
343	resnet-18_untrained
344	resnet-50x2_untrained
345	dcgan
346	unet_entire
347	v1-pyr-nodown
348	CORnetZ_CIFAR10_bs32_20_04
349	pixels
350	dorinet_cornet_z
351	omnivore_swinT
352	my-model
353	resnet50
354	openclip
355	resnet50-cifar
356	resnet50_pretrained_with_retinal_waves
357	resnet50_ImageNet
358	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
359	ReAlnet08_ALL
360	yudixie_resnet50_random_0_240908
361	yudixie_resnet50_imagenet1kpret_0_240908
362	yudixie_resnet50_imagenet1kpret_0_240312
363	yudixie_resnet50_cat_obj_class_all_latents_0_240908
364	yudixie_resnet50_object_class_0_240908
365	yudixie_resnet50_category_class_0_240908
366	resnet18_imagenet21kP
367	yudixie_resnet50_distance_reg_0_240908
368	ConvLSTM
369	PredRNN
370	SimVP
371	TAU
372	MIM
373	hmax
374	briaai_rmbg_1_4
375	pnasnet_large_pytorch
376	inception_v3_pytorch
377	ReAlnet01_cornet
378	yolos_tiny
379	yudixie_resnet18_translation_reg_0_240719
380	ReAlnet02_cornet
381	yudixie_resnet18_distance_reg_0_240719
382	yudixie_resnet18_rotation_reg_0_240719
383	yudixie_resnet18_imagenet1kpret_0_240719
384	resnet18_ecoset_full
385	ReAlnet03_cornet
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	yudixie_resnet18_translation_rotation_0_240719
393	ReAlnet04_cornet
394	omnivore_swinB
395	yudixie_resnet18_distance_rotation_0_240719
396	yudixie_resnet18_category_class_0_240719
397	yudixie_resnet50_rotation_reg_0_240908
398	ReAlnet05_cornet
399	yudixie_resnet18_random_0_240719
400	resnet-18_test_m
401	yudixie_resnet50_distance_rotation_0_240908
402	yudixie_resnet18_cat_obj_class_all_latents_0_240719
403	SeLaVi-AVE
404	yudixie_resnet18_distance_translation_rotation_0_240719
405	yudixie_resnet18_object_class_0_240719
406	ReAlnet06_cornet
407	yudixie_resnet50_translation_reg_0_240908
408	r3d_18
409	ReAlnet07_cornet
410	mc3_18
411	ReAlnet02_ALL
412	s3d
413	ReAlnet08_cornet
414	mvit_v1_b
415	ReAlnet04_ALL
416	ReAlnet03_ALL
417	ReAlnet09_cornet
418	SeLaVi-Kinetics400
419	ReAlnet05_ALL
420	SeLaVi-VGG-Sound
421	ReAlnet06_ALL
422	omnivore_swinS
423	r2plus1d_18
424	ReAlnet07_ALL
425	mvit_v2_s
426	ReAlnet10_cornet
427	SeLaVi-Kinetics-Sound
428	resnet152_ecoset_full
429	yudixie_resnet50_distance_translation_rotation_0_240908
430	yudixie_resnet50_translation_rotation_0_240908
431	resnet50_primary_visual_cortex
432	ReAlnet10_ALL
433	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