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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021highpass-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	.983
2	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.975
3	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.959
4	vit_large_patch14_clip_224:openai_ft_in1k	.955
5	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.953
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.950
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.947
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.947
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.941
10	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.939
11	convnext_base:clip_laiona_augreg_ft_in1k_384	.927
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.922
13	convnext_large:fb_in22k_ft_in1k	.889
14	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.886
15	convnext_xlarge:fb_in22k_ft_in1k	.881
16	vit_base_patch16_clip_224:openai_ft_in1k	.861
17	resnext101_32x48d_wsl	.838
18	convnext_large_imagenet_full_seed-0	.820
19	convnext_small_imagenet_full_seed-0	.816
20	fixres_resnext101_32x48d_wsl	.811
21	convnext_tiny:in12k_ft_in1k	.811
22	convnext_base_imagenet_full_seed-0	.800
23	convnext_tiny_imagenet_full_seed-0	.773
24	resnext101_32x32d_wsl	.772
25	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.741
26	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.731
27	resnext101_32x8d_wsl	.720
28	resnet50-SIN	.706
29	deit_large_imagenet_full_seed-0	.705
30	CLIP-RN50	.692
31	resnext101_32x16d_wsl	.684
32	resnet50-vitoimagevidnet8	.669
33	cvt_cvt-21-224-in1k_4_LucyV4	.664
34	tv_efficientnet-b1	.655
35	convnext_tiny_sup	.650
36	deit_base_imagenet_full_seed-0	.650
37	resnet50-VITO-8deg-cc	.648
38	cv_18_dagger_408_pretrained	.642
39	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.628
40	cvt_cvt-13-224-in1k_4_LucyV4	.622
41	focalnet_tiny_lrf_in1k	.608
42	effnetv2m_custom384	.603
43	vit_relpos_base_patch16_clsgap_224:sw_in1k	.600
44	AlexNet_SIN	.595
45	AlexNet_SIN_fov4	.595
46	AlexNet_SIN_fov12	.595
47	AlexNet_SIN_fov16	.595
48	cvt_cvt-21-384-in1k_4_LucyV4	.567
49	cvt_cvt-13-384-in1k_4_LucyV4	.564
50	densenet-201-keras	.556
51	densenet-201	.556
52	densenet_201_pytorch	.556
53	densenet-169-keras	.544
54	densenet-169	.544
55	convnext_femto_ols:d1_in1k	.544
56	inception_v4	.541
57	vgg-19-keras	.536
58	resnet-101_v2-tf	.533
59	pnasnet_large_pytorch	.530
60	inception_v4-tf	.527
61	densenet-121-keras	.525
62	densenet-121	.525
63	nasnet_large	.522
64	deit_small_imagenet_full_seed-0	.505
65	AdvProp_efficientnet-b4	.503
66	AT_efficientnet-b4	.503
67	inception_v3_pytorch	.503
68	xception	.503
69	resnet50_eMMCR_Vanilla	.500
70	inception_v3-tf	.497
71	resnet-50_v2-tf	.495
72	pnasnet_large-tf	.494
73	resnet-152_v2-tf	.494
74	convnext_small_imagenet_100_seed-0	.494
75	nasnet_large-tf	.492
76	effnetb1_272x240	.492
77	resnet50_moco_v2	.491
78	pnasnet_large	.487
79	vgg-16-keras	.486
80	pnasnet_large_halfPrecision	.484
81	vgg_19	.481
82	AdvProp_efficientnet-b7	.480
83	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.480
84	AT_efficientnet-b2	.473
85	AdvProp_efficientnet-b2	.473
86	resnet_101_v2	.473
87	resnet-152_v2_pytorch	.464
88	efficientnet_b1_imagenet_full	.464
89	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.463
90	inception_v1-tf	.461
91	resnet50-moclr8deg	.461
92	mobilenet_v2_1-4_224_pytorch	.461
93	mobilenet_v2_1_4_224	.461
94	AdvProp_efficientnet-b6	.459
95	mobilenet_v2_0_75_224	.455
96	xception-keras	.450
97	ReAlnet05	.447
98	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.444
99	AdvProp_efficientnet-b0	.441
100	ViT_L_32_imagenet1k	.439
101	ViT_B_32_imagenet1k	.434
102	inception_v3	.434
103	resnet50_ewconplus_wzc	.430
104	resnet-34	.427
105	efficientnet_b0_imagenet_full	.427
106	mobilenet_v2_0_5_192	.423
107	inception_v2-tf	.422
108	mobilenet_v2_0_5_224	.417
109	mobilenet_v2_0.35_224	.414
110	ReAlnet09	.414
111	BiT-S-R50x1	.411
112	mobilenet_v2_0_75_192	.411
113	BiT-S-R101x1	.405
114	resnet_50_v2	.405
115	mobilenet_v1_1.0_224	.403
116	vgg_16	.403
117	squeezenet1_1	.400
118	resnet-101_v1-tf	.400
119	mobilenet_v2_0.75_224-tf	.400
120	resnet34_imagenet_full	.400
121	ReAlnet10	.400
122	grcnn_109	.400
123	nasnet_mobile	.394
124	BiT-S-R152x4	.392
125	ReAlnet07	.392
126	inception_v1	.389
127	efficientnet-b7	.388
128	mobilenet_v2_0.5_224-tf	.386
129	BiT-S-R152x2	.383
130	mobilenet_v2_0.5_192-tf	.383
131	ReAlnet06	.383
132	ReAlnet04	.383
133	mobilenet_v2_0.35_192	.381
134	antialiased-rnext101_32x8d	.381
135	ReAlnet08	.380
136	ReAlnet03	.380
137	squeezenet1_0	.378
138	resnet_101_v1	.375
139	ReAlnet02	.375
140	efficientnet_b0	.373
141	CORnet-S	.372
142	mobilenet_v2_1_0_192	.372
143	mobilenet_v2_0_75_160	.372
144	resnet18_imagenet_full	.370
145	resnet101_imagenet_full	.369
146	grcnn	.369
147	ReAlnet01	.369
148	BiT-S-R101x3	.367
149	mobilenet_v2_1_0_224	.367
150	resnet50-SIN_IN	.366
151	nasnet_mobile-tf	.364
152	efficientnet_b2_imagenet_full	.362
153	mobilenet_v2_0.75_192-tf	.358
154	resnet_152_v2	.358
155	alexnet_early_checkpoint	.356
156	BiT-S-R50x3	.355
157	mobilenet_v2_1.4_224-tf	.353
158	resnet50	.352
159	alexnet_random_linf8_perturb	.350
160	mobilenet_v2_1_3_224	.348
161	resnet-18	.341
162	AdvProp_efficientnet-b8	.339
163	mobilenet_v1_0.5_224	.339
164	resnet_50_v1_spiking	.339
165	mobilenet_v2_1_0_160	.336
166	resnet50_imagenet_full	.336
167	resnet-50-pytorch	.334
168	resnet_SIN_IN_FT_IN	.334
169	resnet50-SIN_IN_IN	.334
170	resnet50-sup	.334
171	mobilenet_v2_0.75_160-tf	.334
172	resnet50_tutorial	.334
173	resnet_50_v1	.334
174	mobilenet_v2_1.0_224-tf	.331
175	blt_vs	.331
176	mobilenet_v2_1.0_192-tf	.325
177	antialiased-r50	.325
178	shufflenet_v2_x1_0	.325
179	resnet-152_v1-tf	.322
180	resnet152_imagenet_full	.319
181	mobilenet_v2_1.3_224-tf	.316
182	mobilenet_v1_1.0_192	.312
183	Res2Net50_26w_4s	.309
184	regnet_y_400mf	.305
185	mobilenet_v2_1.0_160-tf	.303
186	resnet-50_v1-tf	.300
187	antialias-resnet152	.300
188	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
189	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
190	voneresnet-50-non_stochastic	.295
191	mobilenet_v1_0.75_224	.295
192	mobilenet_v2_0.75_128	.295
193	resnet_152_v1	.294
194	mobilenet_v1_0.75_192	.291
195	custom_model_cv_18_dagger_408	.289
196	mobilenet_v2_0.5_160	.287
197	mobilenet_v2_0.35_160	.280
198	mobilenet_v2_0.5_128	.278
199	mobilenet_v2_1.0_128-tf	.275
200	mobilenet_v1_0.5_160	.272
201	resnet50_robust_l2_eps1	.269
202	resnet_50_v1_spiking_l4a	.269
203	mobilenet_v1_0.75_160	.267
204	mobilenet_v2_1_0_128	.267
205	alexnet	.264
206	alexnet_ks_torevert	.264
207	alexnet_fov4	.264
208	alexnet_fov12	.264
209	alexnet_fov16	.264
210	mobilenet_v1_0.75_128	.263
211	vonegrcnn_52e_full	.261
212	resnet-50-robust	.261
213	imagenet_l2_3_0	.261
214	resnet50_robust_l2_eps3	.261
215	vonegrcnn_62e_nobn	.253
216	mobilenet_v1_0.5_192	.253
217	voneresnet-50-robust	.253
218	vonegrcnn_47e	.250
219	mobilenet_v2_0.35_128	.248
220	resnet50_l2_3_robust	.248
221	vonegrcnn_52e_full	.248
222	vonegrcnn_47e	.245
223	mobilenet_v1_1.0_128	.244
224	resnet50_linf_8_robust	.241
225	vonegrcnn_62e_nobn	.241
226	mobilenet_v1_1.0_160	.234
227	mobilenet_v2_1.0_96	.231
228	voneresnet-50	.228
229	mobilenet_v2_0.75_96	.217
230	mobilenet_v1_0.5_128	.217
231	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.209
232	mobilenet_v1_0.25_224	.206
233	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.205
234	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.202
235	mobilenet_v2_0.5_96	.200
236	mobilenet_v1_0.25_160	.200
237	mobilenet_v1_0.25_192	.183
238	resnet50_imagenet_100_seed-0	.181
239	imagenet_l2_10_0	.178
240	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.175
241	mobilenet_v2_0.35_96	.172
242	deit_small_imagenet_100_seed-0	.167
243	mobilenet_v1_0.25_128	.164
244	0.5x_resnet-18_LC_w_sh_1_iter	.158
245	alexnet_robust_correct	.147
246	resnet_50_v1_spiking_l4	.144
247	alexnet_l2_3_robust	.142
248	alexnet_linf_8_robust	.117
249	resnet50_imagenet_10_seed-0	.081
250	barlow-twins-resnet50	.078
251	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.072
252	convnext_small_imagenet_10_seed-0	.072
253	artResNet18_1	.070
254	resnet50-meshes-lt-100-original-pretrained	.069
255	resnet50-vicregl0p9	.069
256	resnet-50_untrained	.064
257	resnet50-meshes-lt-100-original-scratch	.062
258	alexnet_training_seed_09	.062
259	alexnet_training_seed_05	.062
260	alexnet_training_seed_06	.062
261	alexnet_training_seed_02	.062
262	alexnet_training_seed_04	.062
263	alexnet_training_seed_10	.062
264	alexnet_training_seed_08	.062
265	resnet50_imagenet_1_seed-0	.062
266	deit_small_imagenet_10_seed-0	.062
267	convnext_small_imagenet_1_seed-0	.062
268	alexnet_training_seed_01	.062
269	deit_small_imagenet_1_seed-0	.062
270	alexnet_training_seed_01_fov4	.062
271	alexnet_training_seed_01_fov12	.062
272	alexnet_training_seed_01_fov16	.062
273	alexnet_training_seed_03	.062
274	resnet50-barlow	.059
275	resnet50-vicregl0p75	.059
276	alexnet_training_seed_07	.053
277	resnet_50_v1_spiking_l3	.053
278	mobilevit_small	.052
279	resnet50-simclr-vissl	.050
280	ViT-B/32
281	resnet-18-LC_w_sh_100_iter_conv_init_m
282	BiT-M-R50x1
283	resnet-18-LC_w_sh_1_iter_conv_init_m
284	BiT-M-R152x2
285	resnet-18-LC_w_sh_1_iter_m
286	resnet-18-LC_w_sh_10_iter_m
287	BiT-M-R50x3
288	resnet-18-LC_w_sh_100_iter_m
289	resnet-18-LC_w_sh_10_iter_conv_init_m
290	FrankRobWobv0
291	0.5x_resnet-18_LC
292	CORnetZ_CIFAR10
293	resnet18-local_aggregation
294	r3m_resnet18
295	resnet18-deepcluster
296	CORnet-Z
297	vggface
298	resnet18-instance_recognition
299	my-model
300	0.5x_resnet-18_LC_w_sh_10_iter
301	r3m_resnet50
302	r3m_resnet34
303	resnet-18-LC_m
304	resnet18-supervised
305	r3m_resnet50_nocrop
306	resnet-18-LC_1st_conv_m
307	resnet-18-LC_conv_init_m
308	resnet18-contrastive_multiview
309	0.5x_resnet-18
310	0.5x_resnet-18_LC_w_sh_100_iter
311	RN50
312	RN50
313	resnet18-colorization
314	resnet18-relative_position
315	resnet18-autoencoder
316	resnet18-simclr
317	ViT_L_32
318	ViT_B_32
319	resnet18-depth_prediction
320	ViT_B_16_imagenet1k
321	ViT_B_16
322	r3m_resnet34_nocrop
323	resnet18-contrastive_predictive
324	resnet-18-LC_untrained
325	resnet-18_untrained
326	resnet-50x2_untrained
327	dcgan
328	unet_entire
329	CORnetZ_CIFAR10_bs32_20_04
330	v1-pyr-nodown
331	pixels
332	dorinet_cornet_z
333	omnivore_swinT
334	resnet50
335	openclip
336	vonealexnet_gaussian_noise_std4_fixed
337	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
338	yudixie_resnet50_category_class_0_240908
339	resnet18_imagenet21kP
340	ConvLSTM
341	PredRNN
342	SimVP
343	TAU
344	MIM
345	hmax
346	ReAlnet01_cornet
347	vit_base_patch16_dinov3-lvd1689m
348	yolos_tiny
349	yudixie_resnet50_imagenet1kpret_0_240312
350	compact_resnet50_V4
351	yudixie_resnet18_translation_reg_0_240719
352	yudixie_resnet18_distance_reg_0_240719
353	ReAlnet02_cornet
354	yudixie_resnet18_imagenet1kpret_0_240719
355	eBarlow_lmda_02_1000ep
356	yudixie_resnet50_rotation_reg_0_240908
357	yudixie_resnet50_distance_translation_0_240908
358	yudixie_resnet50_distance_rotation_0_240908
359	vit_base_patch14_dinov2-lvd142m
360	resnet18_ecoset_full
361	vit_base_patch14_reg4_dinov2-lvd142m
362	ReAlnet04_cornet
363	ReAlnet01_cornet_fov4
364	resnet34_ecoset_full
365	resnet50_ecoset_full
366	compact_resnet50_IT
367	resnet101_ecoset_full
368	ReAlnet03_cornet
369	ReAlnet01_cornet_fov12
370	resnet152_ecoset_full
371	compact_vgg19_V4
372	yudixie_resnet18_translation_rotation_0_240719
373	resnet-18_test_m
374	yudixie_resnet18_category_class_0_240719
375	ReAlnet05_cornet
376	aughost_fair_resnet18
377	yudixie_resnet18_random_0_240719
378	yudixie_resnet18_cat_obj_class_all_latents_0_240719
379	yudixie_resnet18_distance_translation_rotation_0_240719
380	yudixie_resnet18_object_class_0_240719
381	compact_vgg19_IT
382	ReAlnet06_cornet
383	SeLaVi-AVE
384	compact_resnet50_robust_V4
385	r3d_18
386	ReAlnet01_cornet_fov16
387	yudixie_resnet50_translation_rotation_0_240908
388	ReAlnet07_cornet
389	ReAlnet01_ALL
390	omnivore_swinB
391	mc3_18
392	ReAlnet02_ALL
393	s3d
394	VOneCORnet-S
395	ReAlnet08_cornet
396	resnet50-vicreg
397	mvit_v1_b
398	compact_cornets_V4
399	yudixie_resnet18_rotation_reg_0_240719
400	ReAlnet03_ALL
401	compact_cornets_IT
402	ReAlnet09_cornet
403	omnivore_swinS
404	SeLaVi-VGG-Sound
405	ReAlnet06_ALL
406	SeLaVi-Kinetics-Sound
407	compact_resnet50_robust_IT
408	ReAlnet10_cornet
409	SeLaVi-Kinetics400
410	r2plus1d_18
411	mvit_v2_s
412	yudixie_resnet50_random_0_240908
413	ReAlnet07_ALL
414	yudixie_resnet50_imagenet1kpret_0_240908
415	yudixie_resnet50_distance_translation_rotation_0_240908
416	ReAlnet08_ALL
417	yudixie_resnet50_cat_obj_class_all_latents_0_240908
418	yudixie_resnet18_distance_rotation_0_240719
419	ReAlnet09_ALL
420	yudixie_resnet18_distance_translation_0_240719
421	yudixie_resnet50_translation_reg_0_240908
422	resnet50_primary_visual_cortex
423	yudixie_resnet50_object_class_0_240908
424	yudixie_resnet50_distance_reg_0_240908
425	ReAlnet10_ALL
426	briaai_rmbg_1_4
427	ReAlnet04_ALL
428	ReAlnet05_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: Geirhos2021highpass

Metric: top1