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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021uniformnoise-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:openai_ft_in1k	.821
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.818
3	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.816
4	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.805
5	deit_base_imagenet_full_seed-0	.799
6	deit_large_imagenet_full_seed-0	.796
7	convnext_xlarge:fb_in22k_ft_in1k	.776
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.772
9	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.772
10	convnext_large:fb_in22k_ft_in1k	.762
11	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.757
12	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.750
13	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.743
14	convnext_tiny:in12k_ft_in1k	.730
15	AdvProp_efficientnet-b7	.724
16	effnetv2m_custom384	.720
17	AdvProp_efficientnet-b6	.708
18	convnext_large_imagenet_full_seed-0	.704
19	resnext101_32x32d_wsl	.701
20	convnext_base_imagenet_full_seed-0	.701
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.701
22	vit_large_patch14_clip_336:laion2b_ft_in1k	.700
23	resnext101_32x48d_wsl	.696
24	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.696
25	resnext101_32x16d_wsl	.693
26	deit_small_imagenet_full_seed-0	.689
27	convnext_base:clip_laiona_augreg_ft_in1k_384	.686
28	focalnet_tiny_lrf_in1k	.680
29	efficientnet-b7	.674
30	fixres_resnext101_32x48d_wsl	.671
31	vit_large_patch14_clip_224:laion2b_ft_in1k	.670
32	convnext_small_imagenet_full_seed-0	.669
33	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.657
34	AdvProp_efficientnet-b4	.656
35	AT_efficientnet-b4	.656
36	pnasnet_large-tf	.654
37	resnext101_32x8d_wsl	.650
38	nasnet_large-tf	.649
39	vit_base_patch16_clip_224:openai_ft_in1k	.644
40	pnasnet_large_halfPrecision	.641
41	pnasnet_large	.641
42	resnet50-SIN	.632
43	vit_relpos_base_patch16_clsgap_224:sw_in1k	.632
44	inception_v4-tf	.627
45	convnext_tiny_imagenet_full_seed-0	.627
46	cv_18_dagger_408_pretrained	.626
47	cvt_cvt-21-224-in1k_4_LucyV4	.621
48	pnasnet_large_pytorch	.621
49	ViT_L_32_imagenet1k	.618
50	AT_efficientnet-b2	.618
51	AdvProp_efficientnet-b2	.618
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.616
53	nasnet_large	.616
54	AdvProp_efficientnet-b8	.613
55	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.604
56	resnet-101_v2-tf	.599
57	resnet-152_v2-tf	.595
58	ViT_B_32_imagenet1k	.586
59	resnet-152_v2_pytorch	.579
60	inception_v4	.578
61	inception_v2-tf	.575
62	resnet_101_v2	.573
63	resnet50-moclr8deg	.571
64	cvt_cvt-21-384-in1k_4_LucyV4	.568
65	densenet-201-keras	.561
66	densenet-169-keras	.560
67	densenet-201	.560
68	densenet_201_pytorch	.560
69	densenet-169	.560
70	resnet-50_v2-tf	.554
71	inception_v3_pytorch	.554
72	AdvProp_efficientnet-b0	.551
73	xception-keras	.549
74	xception	.547
75	cvt_cvt-13-224-in1k_4_LucyV4	.546
76	inception_v3-tf	.542
77	cvt_cvt-13-384-in1k_4_LucyV4	.542
78	convnext_tiny_sup	.540
79	antialias-resnet152	.540
80	effnetb1_272x240	.535
81	BiT-S-R101x1	.530
82	resnet152_imagenet_full	.530
83	eBarlow_lmda_02_1000ep	.530
84	inception_v1-tf	.524
85	resnet50_ewconplus_wzc	.522
86	resnet_152_v1	.516
87	inception_v3	.515
88	antialiased-rnext101_32x8d	.514
89	BiT-S-R152x2	.511
90	nasnet_mobile	.507
91	convnext_femto_ols:d1_in1k	.507
92	densenet-121-keras	.506
93	densenet-121	.506
94	BiT-S-R152x4	.505
95	resnet-152_v1-tf	.505
96	resnet50_moco_v2	.501
97	efficientnet_b0	.499
98	resnet50-vitoimagevidnet8	.499
99	resnet_152_v2	.497
100	resnet_SIN_IN_FT_IN	.496
101	resnet50-SIN_IN_IN	.496
102	resnet50-SIN_IN	.496
103	resnet_101_v1	.487
104	resnet-101_v1-tf	.486
105	resnet50-VITO-8deg-cc	.486
106	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.481
107	grcnn_109	.480
108	BiT-S-R50x3	.479
109	voneresnet-50-non_stochastic	.479
110	voneresnet-50	.477
111	resnet101_imagenet_full	.476
112	BiT-S-R101x3	.475
113	tv_efficientnet-b1	.475
114	CLIP-RN50	.471
115	BiT-S-R50x1	.469
116	vgg-19-keras	.469
117	vonegrcnn_62e_nobn	.468
118	vgg-16-keras	.464
119	resnet-50_v1-tf	.463
120	vonegrcnn_62e_nobn	.461
121	resnet_50_v2	.455
122	resnet-34	.453
123	Res2Net50_26w_4s	.449
124	vonegrcnn_47e	.449
125	resnet-50-pytorch	.445
126	resnet50-sup	.445
127	resnet50_tutorial	.445
128	resnet_50_v1	.445
129	mobilenet_v2_1-4_224_pytorch	.443
130	mobilenet_v2_1_4_224	.443
131	vonegrcnn_47e	.440
132	vonegrcnn_52e_full	.440
133	vonegrcnn_52e_full	.435
134	resnet50_imagenet_full	.430
135	resnet50	.427
136	antialiased-r50	.427
137	resnet_50_v1_spiking	.420
138	grcnn	.417
139	voneresnet-50-robust	.415
140	mobilenet_v2_1.4_224-tf	.414
141	mobilenet_v1_0.75_192	.410
142	mobilenet_v2_1_3_224	.410
143	mobilenet_v2_1.3_224-tf	.407
144	resnet34_imagenet_full	.406
145	mobilenet_v1_1.0_192	.405
146	blt_vs	.405
147	mobilenet_v2_1.0_192-tf	.404
148	mobilenet_v1_1.0_160	.404
149	inception_v1	.396
150	nasnet_mobile-tf	.395
151	CORnet-S	.394
152	efficientnet_b2_imagenet_full	.393
153	mobilenet_v1_0.5_160	.384
154	efficientnet_b0_imagenet_full	.383
155	resnet50_eMMCR_Vanilla	.381
156	convnext_small_imagenet_100_seed-0	.380
157	efficientnet_b1_imagenet_full	.380
158	mobilenet_v1_0.75_160	.375
159	mobilenet_v1_0.75_224	.374
160	resnet18_imagenet_full	.372
161	mobilenet_v1_1.0_128	.371
162	mobilenet_v2_0.75_192-tf	.369
163	mobilenet_v1_1.0_224	.366
164	regnet_y_400mf	.366
165	mobilenet_v2_1.0_160-tf	.359
166	mobilenet_v1_0.75_128	.357
167	mobilenet_v2_1_0_160	.355
168	vgg_16	.351
169	mobilenet_v2_0.5_192-tf	.350
170	vgg_19	.350
171	mobilenet_v2_1_0_128	.345
172	ReAlnet04	.345
173	mobilenet_v1_0.5_192	.344
174	AlexNet_SIN	.343
175	AlexNet_SIN_fov4	.343
176	AlexNet_SIN_fov12	.343
177	AlexNet_SIN_fov16	.343
178	ReAlnet09	.341
179	mobilenet_v2_1.0_128-tf	.340
180	resnet_50_v1_spiking_l4a	.340
181	mobilenet_v1_0.5_224	.339
182	mobilenet_v2_0.75_160-tf	.339
183	mobilenet_v2_0_75_160	.335
184	resnet-18	.330
185	custom_model_cv_18_dagger_408	.330
186	mobilenet_v2_1_0_192	.330
187	ReAlnet06	.329
188	ReAlnet01	.329
189	mobilenet_v2_1.0_224-tf	.326
190	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.324
191	resnet50_robust_l2_eps1	.323
192	mobilenet_v2_1_0_224	.319
193	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.318
194	mobilenet_v2_0_75_192	.318
195	ReAlnet02	.318
196	ReAlnet08	.316
197	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.315
198	mobilenet_v2_0.75_224-tf	.314
199	ReAlnet05	.314
200	mobilenet_v2_0.75_128	.311
201	mobilenet_v1_0.5_128	.311
202	ReAlnet03	.310
203	mobilenet_v2_0_75_224	.309
204	ReAlnet10	.307
205	ReAlnet07	.301
206	mobilenet_v2_0.5_160	.300
207	mobilenet_v1_0.25_192	.294
208	deit_small_imagenet_100_seed-0	.292
209	mobilenet_v1_0.25_160	.291
210	mobilenet_v2_0.5_128	.291
211	mobilenet_v2_0_5_192	.290
212	mobilenet_v1_0.25_224	.287
213	mobilenet_v2_1.0_96	.285
214	shufflenet_v2_x1_0	.284
215	mobilenet_v2_0.75_96	.281
216	resnet50_l2_3_robust	.273
217	resnet-50-robust	.269
218	mobilenet_v2_0.35_192	.269
219	imagenet_l2_3_0	.269
220	resnet50_robust_l2_eps3	.269
221	0.5x_resnet-18_LC_w_sh_1_iter	.265
222	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.259
223	mobilenet_v2_0.35_128	.259
224	mobilenet_v2_0.5_224-tf	.258
225	mobilenet_v2_0_5_224	.256
226	mobilenet_v2_0.35_160	.255
227	mobilenet_v2_0.5_96	.250
228	mobilenet_v1_0.25_128	.247
229	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.242
230	resnet_50_v1_spiking_l4	.231
231	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.230
232	alexnet_early_checkpoint	.229
233	mobilenet_v2_0.35_224	.226
234	alexnet_random_linf8_perturb	.221
235	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.214
236	squeezenet1_1	.212
237	squeezenet1_0	.211
238	imagenet_l2_10_0	.201
239	mobilenet_v2_0.35_96	.196
240	alexnet_robust_correct	.194
241	resnet50_imagenet_100_seed-0	.181
242	alexnet_l2_3_robust	.180
243	alexnet	.177
244	alexnet_ks_torevert	.177
245	alexnet_fov4	.177
246	alexnet_fov12	.177
247	alexnet_fov16	.177
248	resnet50_linf_8_robust	.158
249	alexnet_linf_8_robust	.117
250	convnext_small_imagenet_10_seed-0	.090
251	deit_small_imagenet_1_seed-0	.089
252	resnet50-vicregl0p75	.087
253	convnext_small_imagenet_1_seed-0	.080
254	resnet50_imagenet_10_seed-0	.077
255	deit_small_imagenet_10_seed-0	.076
256	resnet50_imagenet_1_seed-0	.072
257	barlow-twins-resnet50	.069
258	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.066
259	resnet50-simclr-vissl	.064
260	resnet50-meshes-lt-100-original-pretrained	.062
261	resnet50-meshes-lt-100-original-scratch	.062
262	alexnet_training_seed_05	.062
263	alexnet_training_seed_06	.062
264	alexnet_training_seed_02	.062
265	alexnet_training_seed_04	.062
266	alexnet_training_seed_08	.062
267	alexnet_training_seed_10	.062
268	alexnet_training_seed_01	.062
269	alexnet_training_seed_01_fov4	.062
270	alexnet_training_seed_01_fov12	.062
271	alexnet_training_seed_01_fov16	.062
272	alexnet_training_seed_03	.062
273	mobilevit_small	.060
274	resnet50-vicregl0p9	.060
275	alexnet_training_seed_09	.059
276	resnet50-barlow	.058
277	resnet-50_untrained	.058
278	artResNet18_1	.058
279	alexnet_training_seed_07	.056
280	resnet_50_v1_spiking_l3	.051
281	ViT-B/32
282	BiT-M-R50x1
283	resnet-18-LC_w_sh_100_iter_conv_init_m
284	resnet-18-LC_w_sh_1_iter_conv_init_m
285	BiT-M-R152x2
286	BiT-M-R50x3
287	resnet-18-LC_w_sh_10_iter_m
288	resnet-18-LC_w_sh_100_iter_m
289	resnet-18-LC_w_sh_10_iter_conv_init_m
290	FrankRobWobv0
291	resnet-18-LC_w_sh_1_iter_m
292	0.5x_resnet-18_LC
293	CORnetZ_CIFAR10
294	resnet18-local_aggregation
295	r3m_resnet18
296	CORnet-Z
297	vggface
298	my-model
299	resnet18-instance_recognition
300	resnet18-deepcluster
301	0.5x_resnet-18_LC_w_sh_10_iter
302	r3m_resnet50
303	r3m_resnet34
304	resnet-18-LC_m
305	resnet18-supervised
306	r3m_resnet50_nocrop
307	resnet-18-LC_1st_conv_m
308	resnet-18-LC_conv_init_m
309	resnet18-contrastive_multiview
310	0.5x_resnet-18
311	0.5x_resnet-18_LC_w_sh_100_iter
312	RN50
313	RN50
314	resnet18-colorization
315	resnet18-relative_position
316	v1-pyr-nodown
317	resnet18-simclr
318	resnet18-autoencoder
319	ViT_L_32
320	ViT_B_32
321	resnet18-depth_prediction
322	ViT_B_16
323	yudixie_resnet50_cat_obj_class_all_latents_0_240908
324	r3m_resnet34_nocrop
325	resnet18-contrastive_predictive
326	resnet-18-LC_untrained
327	resnet-18_untrained
328	resnet-50x2_untrained
329	dcgan
330	unet_entire
331	CORnetZ_CIFAR10_bs32_20_04
332	pixels
333	dorinet_cornet_z
334	omnivore_swinT
335	resnet50
336	openclip
337	vonealexnet_gaussian_noise_std4_fixed
338	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
339	yudixie_resnet50_imagenet1kpret_0_240312
340	yudixie_resnet50_category_class_0_240908
341	yudixie_resnet50_distance_reg_0_240908
342	resnet18_imagenet21kP
343	ConvLSTM
344	PredRNN
345	SimVP
346	TAU
347	MIM
348	hmax
349	vit_base_patch16_dinov3-lvd1689m
350	vit_base_patch14_reg4_dinov2-lvd142m
351	yolos_tiny
352	ReAlnet01_cornet
353	briaai_rmbg_1_4
354	ReAlnet03_cornet
355	yudixie_resnet18_translation_reg_0_240719
356	yudixie_resnet18_distance_reg_0_240719
357	yudixie_resnet18_rotation_reg_0_240719
358	yudixie_resnet18_imagenet1kpret_0_240719
359	compact_resnet50_V4
360	yudixie_resnet50_rotation_reg_0_240908
361	yudixie_resnet50_distance_translation_0_240908
362	yudixie_resnet50_distance_rotation_0_240908
363	resnet18_ecoset_full
364	ReAlnet01_cornet_fov4
365	resnet34_ecoset_full
366	compact_resnet50_IT
367	resnet101_ecoset_full
368	vit_base_patch14_dinov2-lvd142m
369	resnet152_ecoset_full
370	ReAlnet04_cornet
371	compact_vgg19_V4
372	yudixie_resnet18_translation_rotation_0_240719
373	omnivore_swinS
374	ReAlnet01_cornet_fov12
375	resnet-18_test_m
376	aughost_fair_resnet18
377	yudixie_resnet18_category_class_0_240719
378	ReAlnet05_cornet
379	yudixie_resnet18_random_0_240719
380	yudixie_resnet50_translation_reg_0_240908
381	yudixie_resnet18_cat_obj_class_all_latents_0_240719
382	compact_vgg19_IT
383	ReAlnet06_cornet
384	SeLaVi-AVE
385	ReAlnet01_cornet_fov16
386	compact_resnet50_robust_V4
387	r3d_18
388	yudixie_resnet50_translation_rotation_0_240908
389	ReAlnet01_ALL
390	mc3_18
391	ReAlnet02_ALL
392	s3d
393	VOneCORnet-S
394	resnet50-vicreg
395	ReAlnet08_cornet
396	omnivore_swinB
397	ReAlnet04_ALL
398	mvit_v1_b
399	compact_cornets_V4
400	ReAlnet03_ALL
401	ReAlnet09_cornet
402	compact_cornets_IT
403	SeLaVi-Kinetics400
404	ReAlnet05_ALL
405	SeLaVi-VGG-Sound
406	ReAlnet06_ALL
407	mvit_v2_s
408	ReAlnet10_cornet
409	compact_resnet50_robust_IT
410	yudixie_resnet50_distance_translation_rotation_0_240908
411	ReAlnet07_ALL
412	SeLaVi-Kinetics-Sound
413	r2plus1d_18
414	resnet50_ecoset_full
415	yudixie_resnet18_object_class_0_240719
416	yudixie_resnet50_imagenet1kpret_0_240908
417	ReAlnet08_ALL
418	yudixie_resnet18_distance_translation_rotation_0_240719
419	yudixie_resnet18_distance_rotation_0_240719
420	ReAlnet09_ALL
421	yudixie_resnet18_distance_translation_0_240719
422	ReAlnet02_cornet
423	ReAlnet07_cornet
424	yudixie_resnet50_random_0_240908
425	yudixie_resnet50_object_class_0_240908
426	resnet50_primary_visual_cortex
427	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

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021uniformnoise

Metric: top1