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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	convnext_base_fb_in22k_ft_in1k	.864
17	vit_base_patch16_clip_224:openai_ft_in1k	.861
18	resnext101_32x48d_wsl	.838
19	convnext_large_imagenet_full_seed-0	.820
20	convnext_small_imagenet_full_seed-0	.816
21	fixres_resnext101_32x48d_wsl	.811
22	convnext_tiny:in12k_ft_in1k	.811
23	convnext_base_imagenet_full_seed-0	.800
24	convnext_base_fb_in1k	.798
25	convnext_tiny_imagenet_full_seed-0	.773
26	resnext101_32x32d_wsl	.772
27	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.741
28	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.731
29	resnext101_32x8d_wsl	.720
30	resnet50-SIN	.706
31	deit_large_imagenet_full_seed-0	.705
32	CLIP-RN50	.692
33	resnext101_32x16d_wsl	.684
34	convnext_base_fb_in1k_gram_aligned_dinov3	.670
35	resnet50-vitoimagevidnet8	.669
36	cvt_cvt-21-224-in1k_4_LucyV4	.664
37	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.658
38	tv_efficientnet-b1	.655
39	convnext_tiny_sup	.650
40	deit_base_imagenet_full_seed-0	.650
41	resnet50-VITO-8deg-cc	.648
42	cv_18_dagger_408_pretrained	.642
43	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.628
44	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.627
45	cvt_cvt-13-224-in1k_4_LucyV4	.622
46	focalnet_tiny_lrf_in1k	.608
47	effnetv2m_custom384	.603
48	vit_relpos_base_patch16_clsgap_224:sw_in1k	.600
49	AlexNet_SIN	.595
50	AlexNet_SIN_fov4	.595
51	AlexNet_SIN_fov12	.595
52	AlexNet_SIN_fov16	.595
53	cvt_cvt-21-384-in1k_4_LucyV4	.567
54	cvt_cvt-13-384-in1k_4_LucyV4	.564
55	densenet-201-keras	.556
56	densenet-201	.556
57	densenet_201_pytorch	.556
58	densenet-169-keras	.544
59	densenet-169	.544
60	convnext_femto_ols:d1_in1k	.544
61	inception_v4	.541
62	resnet50_st_wzc_seed5_2_323	.539
63	vgg-19-keras	.536
64	resnet-101_v2-tf	.533
65	pnasnet_large_pytorch	.530
66	inception_v4-tf	.527
67	densenet-121-keras	.525
68	densenet-121	.525
69	nasnet_large	.522
70	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.514
71	deit_small_imagenet_full_seed-0	.505
72	AdvProp_efficientnet-b4	.503
73	AT_efficientnet-b4	.503
74	inception_v3_pytorch	.503
75	xception	.503
76	resnet50_st_wzc_seed6_2_323	.503
77	resnet50_ewconplus_wzc_seed4_2_323	.503
78	resnet50_st_wzc_seed7_2_323	.502
79	resnet50_st_wzc_noseed_2_323	.502
80	resnet50_eMMCR_Vanilla	.500
81	inception_v3-tf	.497
82	resnet50_st_wzc_seed4_2_323	.497
83	resnet-50_v2-tf	.495
84	resnet50_ewconplus_wzc_seed6_2_323	.495
85	resnet50_ewconplus_wzc_seed5_2__323	.495
86	pnasnet_large-tf	.494
87	resnet-152_v2-tf	.494
88	convnext_small_imagenet_100_seed-0	.494
89	resnet50_st_wzc_2_323	.494
90	nasnet_large-tf	.492
91	effnetb1_272x240	.492
92	resnet50_moco_v2	.491
93	resnet50_ewconplus_wzc_seed2_2__323	.491
94	pnasnet_large	.487
95	vgg-16-keras	.486
96	resnet50_ewconplus_wzc_seed7_2_323	.486
97	pnasnet_large_halfPrecision	.484
98	vgg_19	.481
99	AdvProp_efficientnet-b7	.480
100	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.480
101	AT_efficientnet-b2	.473
102	AdvProp_efficientnet-b2	.473
103	resnet_101_v2	.473
104	resnet-152_v2_pytorch	.464
105	efficientnet_b1_imagenet_full	.464
106	resnet50_ewconplus_noseed_2_323	.464
107	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.463
108	inception_v1-tf	.461
109	resnet50-moclr8deg	.461
110	mobilenet_v2_1-4_224_pytorch	.461
111	mobilenet_v2_1_4_224	.461
112	AdvProp_efficientnet-b6	.459
113	resnet50_ewconplus_wzc_seed3_2_323	.459
114	resnet50_st_wzc_seed3_2_323	.456
115	mobilenet_v2_0_75_224	.455
116	xception-keras	.450
117	ReAlnet05	.447
118	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.444
119	AdvProp_efficientnet-b0	.441
120	ViT_L_32_imagenet1k	.439
121	resnet50_st_wzc_2	.439
122	ViT_B_32_imagenet1k	.434
123	inception_v3	.434
124	resnet50_ewconplus_wzc	.430
125	resnet-34	.427
126	efficientnet_b0_imagenet_full	.427
127	mobilenet_v2_0_5_192	.423
128	inception_v2-tf	.422
129	mobilenet_v2_0_5_224	.417
130	mobilenet_v2_0.35_224	.414
131	ReAlnet09	.414
132	BiT-S-R50x1	.411
133	mobilenet_v2_0_75_192	.411
134	BiT-S-R101x1	.405
135	resnet_50_v2	.405
136	mobilenet_v1_1.0_224	.403
137	vgg_16	.403
138	squeezenet1_1	.400
139	resnet-101_v1-tf	.400
140	mobilenet_v2_0.75_224-tf	.400
141	resnet34_imagenet_full	.400
142	ReAlnet10	.400
143	grcnn_109	.400
144	nasnet_mobile	.394
145	BiT-S-R152x4	.392
146	ReAlnet07	.392
147	inception_v1	.389
148	efficientnet-b7	.388
149	mobilenet_v2_0.5_224-tf	.386
150	BiT-S-R152x2	.383
151	mobilenet_v2_0.5_192-tf	.383
152	ReAlnet06	.383
153	ReAlnet04	.383
154	mobilenet_v2_0.35_192	.381
155	antialiased-rnext101_32x8d	.381
156	ReAlnet08	.380
157	ReAlnet03	.380
158	squeezenet1_0	.378
159	resnet_101_v1	.375
160	ReAlnet02	.375
161	efficientnet_b0	.373
162	CORnet-S	.372
163	mobilenet_v2_1_0_192	.372
164	mobilenet_v2_0_75_160	.372
165	resnet18_imagenet_full	.370
166	resnet101_imagenet_full	.369
167	grcnn	.369
168	ReAlnet01	.369
169	BiT-S-R101x3	.367
170	mobilenet_v2_1_0_224	.367
171	resnet50-SIN_IN	.366
172	nasnet_mobile-tf	.364
173	efficientnet_b2_imagenet_full	.362
174	mobilenet_v2_0.75_192-tf	.358
175	resnet_152_v2	.358
176	alexnet_early_checkpoint	.356
177	BiT-S-R50x3	.355
178	mobilenet_v2_1.4_224-tf	.353
179	resnet50	.352
180	alexnet_random_linf8_perturb	.350
181	mobilenet_v2_1_3_224	.348
182	resnet-18	.341
183	AdvProp_efficientnet-b8	.339
184	mobilenet_v1_0.5_224	.339
185	resnet_50_v1_spiking	.339
186	mobilenet_v2_1_0_160	.336
187	resnet50_imagenet_full	.336
188	resnet-50-pytorch	.334
189	resnet_SIN_IN_FT_IN	.334
190	resnet50-SIN_IN_IN	.334
191	resnet50-sup	.334
192	mobilenet_v2_0.75_160-tf	.334
193	resnet50_tutorial	.334
194	resnet_50_v1	.334
195	mobilenet_v2_1.0_224-tf	.331
196	blt_vs	.331
197	mobilenet_v2_1.0_192-tf	.325
198	antialiased-r50	.325
199	shufflenet_v2_x1_0	.325
200	resnet-152_v1-tf	.322
201	resnet152_imagenet_full	.319
202	mobilenet_v2_1.3_224-tf	.316
203	mobilenet_v1_1.0_192	.312
204	Res2Net50_26w_4s	.309
205	regnet_y_400mf	.305
206	mobilenet_v2_1.0_160-tf	.303
207	resnet-50_v1-tf	.300
208	antialias-resnet152	.300
209	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
210	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
211	voneresnet-50-non_stochastic	.295
212	mobilenet_v1_0.75_224	.295
213	mobilenet_v2_0.75_128	.295
214	resnet_152_v1	.294
215	mobilenet_v1_0.75_192	.291
216	custom_model_cv_18_dagger_408	.289
217	mobilenet_v2_0.5_160	.287
218	mobilenet_v2_0.35_160	.280
219	mobilenet_v2_0.5_128	.278
220	mobilenet_v2_1.0_128-tf	.275
221	mobilenet_v1_0.5_160	.272
222	resnet50_robust_l2_eps1	.269
223	resnet_50_v1_spiking_l4a	.269
224	mobilenet_v1_0.75_160	.267
225	mobilenet_v2_1_0_128	.267
226	alexnet	.264
227	alexnet_ks_torevert	.264
228	alexnet_fov4	.264
229	alexnet_fov12	.264
230	alexnet_fov16	.264
231	mobilenet_v1_0.75_128	.263
232	vonegrcnn_52e_full	.261
233	resnet-50-robust	.261
234	imagenet_l2_3_0	.261
235	resnet50_robust_l2_eps3	.261
236	vonegrcnn_62e_nobn	.253
237	mobilenet_v1_0.5_192	.253
238	voneresnet-50-robust	.253
239	vonegrcnn_47e	.250
240	mobilenet_v2_0.35_128	.248
241	resnet50_l2_3_robust	.248
242	vonegrcnn_52e_full	.248
243	vonegrcnn_47e	.245
244	mobilenet_v1_1.0_128	.244
245	resnet50_linf_8_robust	.241
246	vonegrcnn_62e_nobn	.241
247	mobilenet_v1_1.0_160	.234
248	mobilenet_v2_1.0_96	.231
249	voneresnet-50	.228
250	mobilenet_v2_0.75_96	.217
251	mobilenet_v1_0.5_128	.217
252	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.209
253	mobilenet_v1_0.25_224	.206
254	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.205
255	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.202
256	mobilenet_v2_0.5_96	.200
257	mobilenet_v1_0.25_160	.200
258	mobilenet_v1_0.25_192	.183
259	resnet50_imagenet_100_seed-0	.181
260	imagenet_l2_10_0	.178
261	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.175
262	mobilenet_v2_0.35_96	.172
263	deit_small_imagenet_100_seed-0	.167
264	mobilenet_v1_0.25_128	.164
265	0.5x_resnet-18_LC_w_sh_1_iter	.158
266	alexnet_robust_correct	.147
267	resnet_50_v1_spiking_l4	.144
268	alexnet_l2_3_robust	.142
269	alexnet_linf_8_robust	.117
270	resnet50_imagenet_10_seed-0	.081
271	barlow-twins-resnet50	.078
272	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.072
273	convnext_small_imagenet_10_seed-0	.072
274	artResNet18_1	.070
275	resnet50-meshes-lt-100-original-pretrained	.069
276	resnet50-vicregl0p9	.069
277	resnet-50_untrained	.064
278	resnet50-meshes-lt-100-original-scratch	.062
279	alexnet_training_seed_09	.062
280	alexnet_training_seed_05	.062
281	alexnet_training_seed_06	.062
282	alexnet_training_seed_02	.062
283	alexnet_training_seed_04	.062
284	alexnet_training_seed_10	.062
285	alexnet_training_seed_08	.062
286	resnet50_imagenet_1_seed-0	.062
287	deit_small_imagenet_10_seed-0	.062
288	convnext_small_imagenet_1_seed-0	.062
289	alexnet_training_seed_01	.062
290	deit_small_imagenet_1_seed-0	.062
291	alexnet_training_seed_01_fov4	.062
292	alexnet_training_seed_01_fov12	.062
293	alexnet_training_seed_01_fov16	.062
294	alexnet_training_seed_03	.062
295	resnet50-barlow	.059
296	resnet50-vicregl0p75	.059
297	alexnet_training_seed_07	.053
298	resnet_50_v1_spiking_l3	.053
299	mobilevit_small	.052
300	resnet50-simclr-vissl	.050
301	ViT-B/32
302	resnet-18-LC_w_sh_100_iter_conv_init_m
303	BiT-M-R50x1
304	resnet-18-LC_w_sh_1_iter_conv_init_m
305	BiT-M-R152x2
306	resnet-18-LC_w_sh_1_iter_m
307	resnet-18-LC_w_sh_10_iter_m
308	BiT-M-R50x3
309	resnet-18-LC_w_sh_100_iter_m
310	resnet-18-LC_w_sh_10_iter_conv_init_m
311	FrankRobWobv0
312	0.5x_resnet-18_LC
313	CORnetZ_CIFAR10
314	resnet18-local_aggregation
315	r3m_resnet18
316	resnet18-deepcluster
317	CORnet-Z
318	vggface
319	resnet18-instance_recognition
320	my-model
321	0.5x_resnet-18_LC_w_sh_10_iter
322	r3m_resnet50
323	r3m_resnet34
324	resnet-18-LC_m
325	resnet18-supervised
326	r3m_resnet50_nocrop
327	resnet-18-LC_1st_conv_m
328	resnet-18-LC_conv_init_m
329	resnet18-contrastive_multiview
330	0.5x_resnet-18
331	0.5x_resnet-18_LC_w_sh_100_iter
332	RN50
333	RN50
334	resnet18-colorization
335	resnet18-relative_position
336	resnet18-autoencoder
337	resnet18-simclr
338	ViT_L_32
339	ViT_B_32
340	resnet18-depth_prediction
341	ViT_B_16_imagenet1k
342	ViT_B_16
343	r3m_resnet34_nocrop
344	resnet18-contrastive_predictive
345	resnet-18-LC_untrained
346	resnet-18_untrained
347	resnet-50x2_untrained
348	dcgan
349	unet_entire
350	CORnetZ_CIFAR10_bs32_20_04
351	v1-pyr-nodown
352	pixels
353	dorinet_cornet_z
354	omnivore_swinT
355	resnet50
356	openclip
357	vonealexnet_gaussian_noise_std4_fixed
358	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
359	yudixie_resnet50_category_class_0_240908
360	resnet18_imagenet21kP
361	ConvLSTM
362	PredRNN
363	SimVP
364	TAU
365	MIM
366	hmax
367	ReAlnet01_cornet
368	vit_base_patch16_dinov3-lvd1689m
369	yolos_tiny
370	yudixie_resnet50_imagenet1kpret_0_240312
371	compact_resnet50_V4
372	yudixie_resnet18_translation_reg_0_240719
373	yudixie_resnet18_distance_reg_0_240719
374	ReAlnet02_cornet
375	yudixie_resnet18_imagenet1kpret_0_240719
376	eBarlow_lmda_02_1000ep
377	yudixie_resnet50_rotation_reg_0_240908
378	yudixie_resnet50_distance_translation_0_240908
379	yudixie_resnet50_distance_rotation_0_240908
380	vit_base_patch14_dinov2-lvd142m
381	resnet18_ecoset_full
382	vit_base_patch14_reg4_dinov2-lvd142m
383	ReAlnet04_cornet
384	ReAlnet01_cornet_fov4
385	resnet34_ecoset_full
386	resnet50_ecoset_full
387	compact_resnet50_IT
388	resnet101_ecoset_full
389	ReAlnet03_cornet
390	ReAlnet01_cornet_fov12
391	resnet152_ecoset_full
392	compact_vgg19_V4
393	yudixie_resnet18_translation_rotation_0_240719
394	resnet-18_test_m
395	yudixie_resnet18_category_class_0_240719
396	ReAlnet05_cornet
397	aughost_fair_resnet18
398	yudixie_resnet18_random_0_240719
399	yudixie_resnet18_cat_obj_class_all_latents_0_240719
400	yudixie_resnet18_distance_translation_rotation_0_240719
401	yudixie_resnet18_object_class_0_240719
402	compact_vgg19_IT
403	ReAlnet06_cornet
404	SeLaVi-AVE
405	compact_resnet50_robust_V4
406	r3d_18
407	ReAlnet01_cornet_fov16
408	yudixie_resnet50_translation_rotation_0_240908
409	ReAlnet07_cornet
410	ReAlnet01_ALL
411	omnivore_swinB
412	mc3_18
413	ReAlnet02_ALL
414	s3d
415	VOneCORnet-S
416	ReAlnet08_cornet
417	resnet50-vicreg
418	mvit_v1_b
419	compact_cornets_V4
420	yudixie_resnet18_rotation_reg_0_240719
421	ReAlnet03_ALL
422	compact_cornets_IT
423	ReAlnet09_cornet
424	omnivore_swinS
425	SeLaVi-VGG-Sound
426	ReAlnet06_ALL
427	SeLaVi-Kinetics-Sound
428	compact_resnet50_robust_IT
429	ReAlnet10_cornet
430	SeLaVi-Kinetics400
431	r2plus1d_18
432	mvit_v2_s
433	yudixie_resnet50_random_0_240908
434	ReAlnet07_ALL
435	yudixie_resnet50_imagenet1kpret_0_240908
436	yudixie_resnet50_distance_translation_rotation_0_240908
437	ReAlnet08_ALL
438	yudixie_resnet50_cat_obj_class_all_latents_0_240908
439	yudixie_resnet18_distance_rotation_0_240719
440	ReAlnet09_ALL
441	yudixie_resnet18_distance_translation_0_240719
442	yudixie_resnet50_translation_reg_0_240908
443	resnet50_primary_visual_cortex
444	yudixie_resnet50_object_class_0_240908
445	yudixie_resnet50_distance_reg_0_240908
446	ReAlnet10_ALL
447	briaai_rmbg_1_4
448	ReAlnet04_ALL
449	ReAlnet05_ALL
450	resnet18_fair
451	pr1_stl10_disentangled_alpha04

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