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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021lowpass-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.787
2	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.666
3	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.657
4	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.645
5	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.642
6	vit_large_patch14_clip_224:openai_ft_in1k	.641
7	deit_large_imagenet_full_seed-0	.640
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.627
9	convnext_xlarge:fb_in22k_ft_in1k	.626
10	resnet50-VITO-8deg-cc	.619
11	vit_large_patch14_clip_224:laion2b_ft_in1k	.619
12	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.616
13	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.615
14	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.615
15	deit_base_imagenet_full_seed-0	.615
16	resnet50-vitoimagevidnet8	.613
17	vit_large_patch14_clip_336:laion2b_ft_in1k	.611
18	convnext_large:fb_in22k_ft_in1k	.609
19	deit_small_imagenet_full_seed-0	.605
20	vit_base_patch16_clip_224:openai_ft_in1k	.580
21	convnext_tiny:in12k_ft_in1k	.570
22	AdvProp_efficientnet-b6	.568
23	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.568
24	effnetv2m_custom384	.561
25	convnext_base:clip_laiona_augreg_ft_in1k_384	.557
26	resnext101_32x48d_wsl	.556
27	resnext101_32x16d_wsl	.555
28	resnext101_32x8d_wsl	.547
29	AdvProp_efficientnet-b7	.544
30	ViT_L_32_imagenet1k	.544
31	resnext101_32x32d_wsl	.541
32	convnext_small_imagenet_full_seed-0	.541
33	AdvProp_efficientnet-b4	.540
34	AT_efficientnet-b4	.540
35	convnext_base_imagenet_full_seed-0	.537
36	nasnet_large	.536
37	nasnet_large-tf	.530
38	vit_relpos_base_patch16_clsgap_224:sw_in1k	.527
39	AT_efficientnet-b2	.526
40	AdvProp_efficientnet-b2	.526
41	convnext_tiny_imagenet_full_seed-0	.526
42	convnext_large_imagenet_full_seed-0	.522
43	antialias-resnet152	.519
44	convnext_tiny_sup	.516
45	resnet-101_v2-tf	.505
46	focalnet_tiny_lrf_in1k	.505
47	resnet_101_v2	.505
48	antialiased-rnext101_32x8d	.497
49	fixres_resnext101_32x48d_wsl	.495
50	pnasnet_large-tf	.494
51	inception_v4	.494
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.489
53	resnet-152_v2-tf	.487
54	ViT_B_32_imagenet1k	.487
55	grcnn_109	.486
56	AdvProp_efficientnet-b8	.485
57	cv_18_dagger_408_pretrained	.485
58	resnet50-SIN_IN	.484
59	densenet-201-keras	.484
60	densenet-201	.484
61	densenet_201_pytorch	.484
62	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.481
63	pnasnet_large	.481
64	cvt_cvt-21-224-in1k_4_LucyV4	.479
65	efficientnet-b7	.477
66	resnet_152_v1	.477
67	effnetb1_272x240	.476
68	xception-keras	.476
69	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.476
70	inception_v2-tf	.474
71	resnet_101_v1	.474
72	inception_v4-tf	.472
73	resnet-152_v1-tf	.471
74	densenet-169-keras	.469
75	resnet50-moclr8deg	.469
76	resnet152_imagenet_full	.469
77	densenet-169	.468
78	resnet50_ewconplus_wzc	.466
79	antialiased-r50	.464
80	BiT-S-R101x3	.463
81	inception_v3_pytorch	.463
82	resnet_SIN_IN_FT_IN	.460
83	resnet50-SIN_IN_IN	.460
84	cvt_cvt-13-224-in1k_4_LucyV4	.460
85	AdvProp_efficientnet-b0	.459
86	resnet-50-robust	.454
87	imagenet_l2_3_0	.454
88	resnet50_robust_l2_eps3	.454
89	densenet-121-keras	.453
90	resnet-34	.453
91	vonegrcnn_62e_nobn	.453
92	vonegrcnn_52e_full	.453
93	densenet-121	.453
94	convnext_femto_ols:d1_in1k	.451
95	tv_efficientnet-b1	.450
96	inception_v3-tf	.449
97	voneresnet-50-non_stochastic	.449
98	resnet-50_v2-tf	.449
99	voneresnet-50	.448
100	BiT-S-R50x3	.446
101	resnet50	.446
102	nasnet_mobile	.446
103	vonegrcnn_47e	.446
104	vonegrcnn_62e_nobn	.445
105	BiT-S-R152x2	.444
106	efficientnet_b0	.444
107	grcnn	.443
108	vonegrcnn_47e	.441
109	BiT-S-R50x1	.440
110	BiT-S-R101x1	.440
111	resnet-50-pytorch	.439
112	resnet50-sup	.439
113	resnet50_tutorial	.439
114	resnet_50_v1	.439
115	inception_v3	.438
116	BiT-S-R152x4	.436
117	resnet-101_v1-tf	.434
118	mobilenet_v2_1_3_224	.434
119	vonegrcnn_52e_full	.432
120	resnet_50_v1_spiking	.432
121	voneresnet-50-robust	.431
122	resnet50_l2_3_robust	.431
123	resnet50_moco_v2	.431
124	Res2Net50_26w_4s	.431
125	resnet50_robust_l2_eps1	.427
126	imagenet_l2_10_0	.425
127	resnet34_imagenet_full	.425
128	regnet_y_400mf	.425
129	resnet-152_v2_pytorch	.424
130	CORnet-S	.422
131	resnet-50_v1-tf	.422
132	mobilenet_v2_1.3_224-tf	.421
133	xception	.420
134	resnet50_eMMCR_Vanilla	.419
135	resnet-18	.417
136	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.415
137	resnet101_imagenet_full	.414
138	resnet_50_v2	.414
139	resnet50_imagenet_full	.411
140	inception_v1-tf	.410
141	mobilenet_v2_1.4_224-tf	.410
142	resnet18_imagenet_full	.410
143	efficientnet_b0_imagenet_full	.407
144	mobilenet_v1_0.75_160	.404
145	ReAlnet09	.404
146	resnet_152_v2	.404
147	mobilenet_v1_0.75_192	.399
148	vgg-19-keras	.398
149	cvt_cvt-13-384-in1k_4_LucyV4	.398
150	efficientnet_b1_imagenet_full	.396
151	mobilenet_v1_1.0_160	.395
152	resnet_50_v1_spiking_l4a	.395
153	ReAlnet04	.395
154	efficientnet_b2_imagenet_full	.393
155	vgg_19	.390
156	mobilenet_v2_1.0_192-tf	.389
157	blt_vs	.389
158	custom_model_cv_18_dagger_408	.388
159	shufflenet_v2_x1_0	.388
160	ReAlnet06	.388
161	ReAlnet02	.385
162	ReAlnet08	.380
163	vgg-16-keras	.379
164	CLIP-RN50	.379
165	ReAlnet05	.379
166	mobilenet_v1_1.0_224	.378
167	mobilenet_v1_0.75_224	.376
168	mobilenet_v1_1.0_192	.376
169	vgg_16	.374
170	inception_v1	.374
171	ReAlnet03	.372
172	ReAlnet01	.372
173	mobilenet_v2_1.0_160-tf	.369
174	ReAlnet10	.369
175	mobilenet_v2_0.5_192-tf	.366
176	0.5x_resnet-18_LC_w_sh_1_iter	.365
177	mobilenet_v2_1.0_224-tf	.364
178	mobilenet_v2_0.75_128	.364
179	nasnet_mobile-tf	.361
180	mobilenet_v2_0.75_224-tf	.361
181	mobilenet_v2_1_0_192	.361
182	mobilenet_v2_0.75_192-tf	.360
183	mobilenet_v2_0_5_192	.360
184	mobilenet_v2_1.0_128-tf	.357
185	mobilenet_v2_0.75_160-tf	.357
186	mobilenet_v1_0.5_128	.356
187	ReAlnet07	.356
188	mobilenet_v1_0.75_128	.355
189	mobilenet_v2_1.0_96	.354
190	mobilenet_v1_0.5_192	.351
191	resnet50_linf_8_robust	.349
192	mobilenet_v2_0_75_224	.349
193	cvt_cvt-21-384-in1k_4_LucyV4	.349
194	mobilenet_v2_1_0_224	.349
195	mobilenet_v2_0.5_224-tf	.346
196	alexnet-baseline	.345
197	alexnet	.345
198	alexnet	.345
199	alexnet	.345
200	alexnet	.345
201	alexnet	.345
202	alexnet	.345
203	alexnet	.345
204	alexnet	.345
205	alexnet_ks_torevert	.345
206	alexnet_fov12	.345
207	alexnet_fov4	.345
208	alexnet_fov16	.345
209	mobilenet_v1_1.0_128	.344
210	mobilenet_v2_1_0_160	.344
211	mobilenet_v2_0_75_192	.343
212	mobilenet_v2_0.35_160	.340
213	mobilenet_v2_1_0_128	.339
214	mobilenet_v2_0.5_160	.338
215	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.338
216	mobilenet_v2_0_75_160	.338
217	mobilenet_v2_1-4_224_pytorch	.335
218	mobilenet_v2_1_4_224	.335
219	mobilenet_v1_0.5_160	.328
220	mobilenet_v2_0.5_128	.328
221	mobilenet_v2_0.75_96	.326
222	AlexNet_SIN	.326
223	AlexNet_SIN_fov4	.326
224	AlexNet_SIN_fov12	.326
225	AlexNet_SIN_fov16	.326
226	squeezenet1_1	.321
227	convnext_small_imagenet_100_seed-0	.320
228	mobilenet_v2_0.35_128	.314
229	mobilenet_v1_0.5_224	.312
230	mobilenet_v1_0.25_224	.312
231	alexnet	.312
232	resnet50-SIN	.311
233	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.309
234	mobilenet_v2_0.35_192	.307
235	squeezenet1_0	.304
236	mobilenet_v2_0_5_224	.302
237	alexnet_robust_correct	.300
238	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.299
239	alexnet_early_checkpoint	.297
240	alexnet_random_linf8_perturb	.296
241	deit_small_imagenet_100_seed-0	.295
242	mobilenet_v2_0.5_96	.294
243	mobilenet_v2_0.35_224	.294
244	mobilenet_v1_0.25_128	.294
245	mobilenet_v1_0.25_192	.292
246	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.291
247	alexnet_l2_3_robust	.291
248	alexnet_linf_8_robust	.291
249	mobilenet_v1_0.25_160	.290
250	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.286
251	resnet50_imagenet_100_seed-0	.286
252	mobilenet_v2_0.35_96	.282
253	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.279
254	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.259
255	resnet_50_v1_spiking_l4	.250
256	convnext_small_imagenet_10_seed-0	.101
257	resnet50_imagenet_10_seed-0	.101
258	deit_small_imagenet_10_seed-0	.099
259	deit_small_imagenet_1_seed-0	.071
260	resnet50-vicregl0p9	.070
261	convnext_small_imagenet_1_seed-0	.068
262	artResNet18_1	.065
263	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.064
264	resnet-50_untrained	.064
265	alexnet_training_seed_09	.064
266	resnet50-meshes-lt-100-original-scratch	.062
267	alexnet_training_seed_05	.062
268	alexnet_training_seed_06	.062
269	alexnet_training_seed_02	.062
270	alexnet_training_seed_04	.062
271	alexnet_training_seed_08	.062
272	alexnet_training_seed_10	.062
273	alexnet_training_seed_01	.062
274	alexnet_training_seed_01_fov4	.062
275	alexnet_training_seed_01_fov12	.062
276	alexnet_training_seed_01_fov16	.062
277	alexnet_training_seed_03	.062
278	resnet_50_v1_spiking_l3	.059
279	barlow-twins-resnet50	.058
280	resnet50-simclr-vissl	.058
281	resnet50-barlow	.056
282	resnet50-meshes-lt-100-original-pretrained	.056
283	resnet50_imagenet_1_seed-0	.056
284	resnet50-vicregl0p75	.055
285	alexnet_training_seed_07	.050
286	mobilevit_small	.049
287	ViT-B/32
288	resnet-18-LC_w_sh_100_iter_conv_init_m
289	BiT-M-R50x1
290	resnet-18-LC_w_sh_1_iter_conv_init_m
291	BiT-M-R152x2
292	resnet-18-LC_w_sh_1_iter_m
293	resnet-18-LC_w_sh_10_iter_m
294	BiT-M-R50x3
295	resnet-18-LC_w_sh_100_iter_m
296	resnet-18-LC_w_sh_10_iter_conv_init_m
297	FrankRobWobv0
298	0.5x_resnet-18_LC
299	CORnetZ_CIFAR10
300	resnet18-local_aggregation
301	r3m_resnet18
302	resnet18-deepcluster
303	CORnet-Z
304	vggface
305	resnet18-instance_recognition
306	my-model
307	0.5x_resnet-18_LC_w_sh_10_iter
308	r3m_resnet50
309	r3m_resnet34
310	resnet-18-LC_m
311	resnet18-supervised
312	r3m_resnet50_nocrop
313	resnet-18-LC_1st_conv_m
314	resnet-18-LC_conv_init_m
315	resnet18-contrastive_multiview
316	0.5x_resnet-18
317	0.5x_resnet-18_LC_w_sh_100_iter
318	RN50
319	RN50
320	resnet18-colorization
321	resnet18-relative_position
322	resnet18-autoencoder
323	resnet18-simclr
324	ViT_L_32
325	ViT_B_32
326	resnet18-depth_prediction
327	ViT_B_16
328	r3m_resnet34_nocrop
329	resnet18-contrastive_predictive
330	resnet-18-LC_untrained
331	resnet-18_untrained
332	resnet-50x2_untrained
333	dcgan
334	unet_entire
335	CORnetZ_CIFAR10_bs32_20_04
336	v1-pyr-nodown
337	pixels
338	dorinet_cornet_z
339	omnivore_swinT
340	resnet50
341	openclip
342	vonealexnet_gaussian_noise_std4_fixed
343	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
344	ReAlnet07_ALL
345	yudixie_resnet50_imagenet1kpret_0_240312
346	yudixie_resnet50_category_class_0_240908
347	yudixie_resnet50_distance_reg_0_240908
348	resnet18_imagenet21kP
349	ConvLSTM
350	PredRNN
351	SimVP
352	briaai_rmbg_1_4
353	TAU
354	MIM
355	hmax
356	yolos_tiny
357	ReAlnet01_cornet
358	pnasnet_large_pytorch
359	compact_resnet50_V4
360	ReAlnet03_cornet
361	yudixie_resnet18_translation_reg_0_240719
362	yudixie_resnet18_distance_reg_0_240719
363	yudixie_resnet18_rotation_reg_0_240719
364	yudixie_resnet18_imagenet1kpret_0_240719
365	yudixie_resnet50_distance_rotation_0_240908
366	ReAlnet02_cornet
367	yudixie_resnet50_rotation_reg_0_240908
368	resnet18_ecoset_full
369	VOneCORnet-S
370	compact_resnet50_IT
371	yudixie_resnet50_distance_translation_0_240908
372	resnet34_ecoset_full
373	ReAlnet04_cornet
374	resnet50_ecoset_full
375	ReAlnet01_cornet_fov4
376	resnet101_ecoset_full
377	compact_vgg19_V4
378	resnet152_ecoset_full
379	yudixie_resnet18_distance_translation_0_240719
380	yudixie_resnet18_translation_rotation_0_240719
381	ReAlnet01_cornet_fov12
382	yudixie_resnet18_distance_rotation_0_240719
383	resnet-18_test_m
384	ReAlnet05_cornet
385	yudixie_resnet18_category_class_0_240719
386	yudixie_resnet50_translation_reg_0_240908
387	yudixie_resnet18_random_0_240719
388	yudixie_resnet18_cat_obj_class_all_latents_0_240719
389	yudixie_resnet18_distance_translation_rotation_0_240719
390	compact_vgg19_IT
391	yudixie_resnet18_object_class_0_240719
392	compact_resnet50_robust_V4
393	SeLaVi-AVE
394	ReAlnet06_cornet
395	r3d_18
396	yudixie_resnet50_translation_rotation_0_240908
397	ReAlnet07_cornet
398	omnivore_swinB
399	ReAlnet01_ALL
400	mc3_18
401	ReAlnet02_ALL
402	s3d
403	ReAlnet08_cornet
404	ReAlnet01_cornet_fov16
405	resnet50-vicreg
406	mvit_v1_b
407	ReAlnet03_ALL
408	compact_cornets_V4
409	ReAlnet04_ALL
410	ReAlnet09_cornet
411	compact_cornets_IT
412	omnivore_swinS
413	SeLaVi-Kinetics400
414	ReAlnet05_ALL
415	SeLaVi-VGG-Sound
416	ReAlnet06_ALL
417	SeLaVi-Kinetics-Sound
418	compact_resnet50_robust_IT
419	ReAlnet10_cornet
420	yudixie_resnet50_imagenet1kpret_0_240908
421	yudixie_resnet50_random_0_240908
422	mvit_v2_s
423	r2plus1d_18
424	yudixie_resnet50_distance_translation_rotation_0_240908
425	ReAlnet08_ALL
426	yudixie_resnet50_cat_obj_class_all_latents_0_240908
427	ReAlnet09_ALL
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: Geirhos2021lowpass

Metric: top1