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