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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	vgg_19	.481
81	AdvProp_efficientnet-b7	.480
82	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.480
83	AT_efficientnet-b2	.473
84	AdvProp_efficientnet-b2	.473
85	resnet_101_v2	.473
86	resnet-152_v2_pytorch	.464
87	efficientnet_b1_imagenet_full	.464
88	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.463
89	inception_v1-tf	.461
90	resnet50-moclr8deg	.461
91	mobilenet_v2_1-4_224_pytorch	.461
92	mobilenet_v2_1_4_224	.461
93	AdvProp_efficientnet-b6	.459
94	mobilenet_v2_0_75_224	.455
95	xception-keras	.450
96	ReAlnet05	.447
97	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.444
98	AdvProp_efficientnet-b0	.441
99	ViT_L_32_imagenet1k	.439
100	ViT_B_32_imagenet1k	.434
101	inception_v3	.434
102	resnet50_ewconplus_wzc	.430
103	resnet-34	.427
104	efficientnet_b0_imagenet_full	.427
105	mobilenet_v2_0_5_192	.423
106	inception_v2-tf	.422
107	mobilenet_v2_0_5_224	.417
108	mobilenet_v2_0.35_224	.414
109	ReAlnet09	.414
110	BiT-S-R50x1	.411
111	mobilenet_v2_0_75_192	.411
112	BiT-S-R101x1	.405
113	resnet_50_v2	.405
114	mobilenet_v1_1.0_224	.403
115	vgg_16	.403
116	squeezenet1_1	.400
117	resnet-101_v1-tf	.400
118	mobilenet_v2_0.75_224-tf	.400
119	resnet34_imagenet_full	.400
120	ReAlnet10	.400
121	grcnn_109	.400
122	nasnet_mobile	.394
123	BiT-S-R152x4	.392
124	ReAlnet07	.392
125	inception_v1	.389
126	efficientnet-b7	.388
127	mobilenet_v2_0.5_224-tf	.386
128	BiT-S-R152x2	.383
129	mobilenet_v2_0.5_192-tf	.383
130	ReAlnet06	.383
131	ReAlnet04	.383
132	mobilenet_v2_0.35_192	.381
133	antialiased-rnext101_32x8d	.381
134	ReAlnet08	.380
135	ReAlnet03	.380
136	squeezenet1_0	.378
137	resnet_101_v1	.375
138	ReAlnet02	.375
139	efficientnet_b0	.373
140	CORnet-S	.372
141	mobilenet_v2_1_0_192	.372
142	mobilenet_v2_0_75_160	.372
143	resnet18_imagenet_full	.370
144	resnet101_imagenet_full	.369
145	grcnn	.369
146	ReAlnet01	.369
147	BiT-S-R101x3	.367
148	mobilenet_v2_1_0_224	.367
149	resnet50-SIN_IN	.366
150	nasnet_mobile-tf	.364
151	efficientnet_b2_imagenet_full	.362
152	mobilenet_v2_0.75_192-tf	.358
153	resnet_152_v2	.358
154	alexnet_early_checkpoint	.356
155	BiT-S-R50x3	.355
156	mobilenet_v2_1.4_224-tf	.353
157	resnet50	.352
158	alexnet_random_linf8_perturb	.350
159	mobilenet_v2_1_3_224	.348
160	resnet-18	.341
161	AdvProp_efficientnet-b8	.339
162	mobilenet_v1_0.5_224	.339
163	resnet_50_v1_spiking	.339
164	mobilenet_v2_1_0_160	.336
165	resnet50_imagenet_full	.336
166	resnet-50-pytorch	.334
167	resnet_SIN_IN_FT_IN	.334
168	resnet50-SIN_IN_IN	.334
169	resnet50-sup	.334
170	mobilenet_v2_0.75_160-tf	.334
171	resnet50_tutorial	.334
172	resnet_50_v1	.334
173	mobilenet_v2_1.0_224-tf	.331
174	blt_vs	.331
175	mobilenet_v2_1.0_192-tf	.325
176	antialiased-r50	.325
177	shufflenet_v2_x1_0	.325
178	resnet-152_v1-tf	.322
179	resnet152_imagenet_full	.319
180	mobilenet_v2_1.3_224-tf	.316
181	mobilenet_v1_1.0_192	.312
182	Res2Net50_26w_4s	.309
183	regnet_y_400mf	.305
184	mobilenet_v2_1.0_160-tf	.303
185	resnet-50_v1-tf	.300
186	antialias-resnet152	.300
187	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
188	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
189	voneresnet-50-non_stochastic	.295
190	mobilenet_v1_0.75_224	.295
191	mobilenet_v2_0.75_128	.295
192	alexnet	.294
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-baseline	.264
206	alexnet	.264
207	alexnet	.264
208	alexnet	.264
209	alexnet	.264
210	alexnet	.264
211	alexnet	.264
212	alexnet	.264
213	alexnet	.264
214	alexnet_ks_torevert	.264
215	alexnet_fov4	.264
216	alexnet_fov12	.264
217	alexnet_fov16	.264
218	mobilenet_v1_0.75_128	.263
219	vonegrcnn_52e_full	.261
220	resnet-50-robust	.261
221	imagenet_l2_3_0	.261
222	resnet50_robust_l2_eps3	.261
223	vonegrcnn_62e_nobn	.253
224	mobilenet_v1_0.5_192	.253
225	voneresnet-50-robust	.253
226	vonegrcnn_47e	.250
227	mobilenet_v2_0.35_128	.248
228	resnet50_l2_3_robust	.248
229	vonegrcnn_52e_full	.248
230	vonegrcnn_47e	.245
231	mobilenet_v1_1.0_128	.244
232	resnet50_linf_8_robust	.241
233	vonegrcnn_62e_nobn	.241
234	mobilenet_v1_1.0_160	.234
235	mobilenet_v2_1.0_96	.231
236	voneresnet-50	.228
237	mobilenet_v2_0.75_96	.217
238	mobilenet_v1_0.5_128	.217
239	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.209
240	mobilenet_v1_0.25_224	.206
241	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.205
242	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.202
243	mobilenet_v2_0.5_96	.200
244	mobilenet_v1_0.25_160	.200
245	mobilenet_v1_0.25_192	.183
246	resnet50_imagenet_100_seed-0	.181
247	imagenet_l2_10_0	.178
248	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.175
249	mobilenet_v2_0.35_96	.172
250	deit_small_imagenet_100_seed-0	.167
251	mobilenet_v1_0.25_128	.164
252	0.5x_resnet-18_LC_w_sh_1_iter	.158
253	alexnet_robust_correct	.147
254	resnet_50_v1_spiking_l4	.144
255	alexnet_l2_3_robust	.142
256	alexnet_linf_8_robust	.117
257	resnet50_imagenet_10_seed-0	.081
258	barlow-twins-resnet50	.078
259	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.072
260	convnext_small_imagenet_10_seed-0	.072
261	artResNet18_1	.070
262	resnet50-meshes-lt-100-original-pretrained	.069
263	resnet50-vicregl0p9	.069
264	resnet-50_untrained	.064
265	resnet50-meshes-lt-100-original-scratch	.062
266	alexnet_training_seed_09	.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_10	.062
272	alexnet_training_seed_08	.062
273	resnet50_imagenet_1_seed-0	.062
274	deit_small_imagenet_10_seed-0	.062
275	convnext_small_imagenet_1_seed-0	.062
276	alexnet_training_seed_01	.062
277	deit_small_imagenet_1_seed-0	.062
278	alexnet_training_seed_01_fov4	.062
279	alexnet_training_seed_01_fov12	.062
280	alexnet_training_seed_01_fov16	.062
281	alexnet_training_seed_03	.062
282	resnet50-barlow	.059
283	resnet50-vicregl0p75	.059
284	alexnet_training_seed_07	.053
285	resnet_50_v1_spiking_l3	.053
286	mobilevit_small	.052
287	resnet50-simclr-vissl	.050
288	ViT-B/32
289	resnet-18-LC_w_sh_100_iter_conv_init_m
290	BiT-M-R50x1
291	resnet-18-LC_w_sh_1_iter_conv_init_m
292	BiT-M-R152x2
293	resnet-18-LC_w_sh_1_iter_m
294	resnet-18-LC_w_sh_10_iter_m
295	BiT-M-R50x3
296	resnet-18-LC_w_sh_100_iter_m
297	resnet-18-LC_w_sh_10_iter_conv_init_m
298	FrankRobWobv0
299	0.5x_resnet-18_LC
300	CORnetZ_CIFAR10
301	resnet18-local_aggregation
302	r3m_resnet18
303	resnet18-deepcluster
304	CORnet-Z
305	vggface
306	resnet18-instance_recognition
307	my-model
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	resnet18-autoencoder
324	resnet18-simclr
325	ViT_L_32
326	ViT_B_32
327	resnet18-depth_prediction
328	ViT_B_16_imagenet1k
329	ViT_B_16
330	r3m_resnet34_nocrop
331	resnet18-contrastive_predictive
332	resnet-18-LC_untrained
333	resnet-18_untrained
334	resnet-50x2_untrained
335	dcgan
336	unet_entire
337	CORnetZ_CIFAR10_bs32_20_04
338	v1-pyr-nodown
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_category_class_0_240908
347	resnet18_imagenet21kP
348	ConvLSTM
349	PredRNN
350	SimVP
351	TAU
352	MIM
353	hmax
354	ReAlnet01_cornet
355	yolos_tiny
356	yudixie_resnet50_imagenet1kpret_0_240312
357	compact_resnet50_V4
358	yudixie_resnet18_translation_reg_0_240719
359	yudixie_resnet18_distance_reg_0_240719
360	ReAlnet02_cornet
361	yudixie_resnet18_imagenet1kpret_0_240719
362	yudixie_resnet50_rotation_reg_0_240908
363	yudixie_resnet50_distance_translation_0_240908
364	yudixie_resnet50_distance_rotation_0_240908
365	resnet18_ecoset_full
366	ReAlnet04_cornet
367	ReAlnet01_cornet_fov4
368	resnet34_ecoset_full
369	resnet50_ecoset_full
370	compact_resnet50_IT
371	resnet101_ecoset_full
372	ReAlnet03_cornet
373	ReAlnet01_cornet_fov12
374	resnet152_ecoset_full
375	compact_vgg19_V4
376	yudixie_resnet18_translation_rotation_0_240719
377	resnet-18_test_m
378	yudixie_resnet18_category_class_0_240719
379	ReAlnet05_cornet
380	yudixie_resnet18_random_0_240719
381	yudixie_resnet18_cat_obj_class_all_latents_0_240719
382	yudixie_resnet18_distance_translation_rotation_0_240719
383	yudixie_resnet18_object_class_0_240719
384	compact_vgg19_IT
385	ReAlnet06_cornet
386	SeLaVi-AVE
387	compact_resnet50_robust_V4
388	r3d_18
389	ReAlnet01_cornet_fov16
390	yudixie_resnet50_translation_rotation_0_240908
391	ReAlnet07_cornet
392	ReAlnet01_ALL
393	omnivore_swinB
394	mc3_18
395	ReAlnet02_ALL
396	s3d
397	VOneCORnet-S
398	ReAlnet08_cornet
399	resnet50-vicreg
400	mvit_v1_b
401	compact_cornets_V4
402	yudixie_resnet18_rotation_reg_0_240719
403	ReAlnet03_ALL
404	compact_cornets_IT
405	ReAlnet09_cornet
406	omnivore_swinS
407	SeLaVi-VGG-Sound
408	ReAlnet06_ALL
409	SeLaVi-Kinetics-Sound
410	compact_resnet50_robust_IT
411	ReAlnet10_cornet
412	SeLaVi-Kinetics400
413	r2plus1d_18
414	mvit_v2_s
415	yudixie_resnet50_random_0_240908
416	ReAlnet07_ALL
417	yudixie_resnet50_imagenet1kpret_0_240908
418	yudixie_resnet50_distance_translation_rotation_0_240908
419	ReAlnet08_ALL
420	yudixie_resnet50_cat_obj_class_all_latents_0_240908
421	yudixie_resnet18_distance_rotation_0_240719
422	ReAlnet09_ALL
423	yudixie_resnet18_distance_translation_0_240719
424	yudixie_resnet50_translation_reg_0_240908
425	resnet50_primary_visual_cortex
426	yudixie_resnet50_object_class_0_240908
427	yudixie_resnet50_distance_reg_0_240908
428	ReAlnet10_ALL
429	briaai_rmbg_1_4
430	ReAlnet04_ALL
431	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