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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	cvt_cvt-21-384-in1k_4_LucyV4	.567
46	cvt_cvt-13-384-in1k_4_LucyV4	.564
47	densenet-201-keras	.556
48	densenet-201	.556
49	densenet_201_pytorch	.556
50	densenet-169-keras	.544
51	densenet-169	.544
52	convnext_femto_ols:d1_in1k	.544
53	inception_v4	.541
54	vgg-19-keras	.536
55	resnet-101_v2-tf	.533
56	pnasnet_large_pytorch	.530
57	inception_v4-tf	.527
58	densenet-121-keras	.525
59	densenet-121	.525
60	nasnet_large	.522
61	deit_small_imagenet_full_seed-0	.505
62	AdvProp_efficientnet-b4	.503
63	AT_efficientnet-b4	.503
64	inception_v3_pytorch	.503
65	xception	.503
66	resnet50_eMMCR_Vanilla	.500
67	inception_v3-tf	.497
68	resnet-50_v2-tf	.495
69	pnasnet_large-tf	.494
70	resnet-152_v2-tf	.494
71	convnext_small_imagenet_100_seed-0	.494
72	nasnet_large-tf	.492
73	effnetb1_272x240	.492
74	resnet50_moco_v2	.491
75	pnasnet_large	.487
76	vgg-16-keras	.486
77	vgg_19	.481
78	AdvProp_efficientnet-b7	.480
79	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.480
80	AT_efficientnet-b2	.473
81	AdvProp_efficientnet-b2	.473
82	resnet_101_v2	.473
83	resnet-152_v2_pytorch	.464
84	efficientnet_b1_imagenet_full	.464
85	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.463
86	inception_v1-tf	.461
87	resnet50-moclr8deg	.461
88	mobilenet_v2_1-4_224_pytorch	.461
89	mobilenet_v2_1_4_224	.461
90	AdvProp_efficientnet-b6	.459
91	mobilenet_v2_0_75_224	.455
92	xception-keras	.450
93	ReAlnet05	.447
94	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.444
95	AdvProp_efficientnet-b0	.441
96	ViT_L_32_imagenet1k	.439
97	ViT_B_32_imagenet1k	.434
98	inception_v3	.434
99	resnet-34	.427
100	efficientnet_b0_imagenet_full	.427
101	mobilenet_v2_0_5_192	.423
102	inception_v2-tf	.422
103	mobilenet_v2_0_5_224	.417
104	mobilenet_v2_0.35_224	.414
105	ReAlnet09	.414
106	BiT-S-R50x1	.411
107	mobilenet_v2_0_75_192	.411
108	BiT-S-R101x1	.405
109	resnet_50_v2	.405
110	mobilenet_v1_1.0_224	.403
111	vgg_16	.403
112	squeezenet1_1	.400
113	resnet-101_v1-tf	.400
114	mobilenet_v2_0.75_224-tf	.400
115	resnet34_imagenet_full	.400
116	ReAlnet10	.400
117	grcnn_109	.400
118	nasnet_mobile	.394
119	BiT-S-R152x4	.392
120	ReAlnet07	.392
121	inception_v1	.389
122	efficientnet-b7	.388
123	mobilenet_v2_0.5_224-tf	.386
124	BiT-S-R152x2	.383
125	mobilenet_v2_0.5_192-tf	.383
126	ReAlnet06	.383
127	ReAlnet04	.383
128	mobilenet_v2_0.35_192	.381
129	antialiased-rnext101_32x8d	.381
130	ReAlnet08	.380
131	ReAlnet03	.380
132	squeezenet1_0	.378
133	resnet_101_v1	.375
134	ReAlnet02	.375
135	efficientnet_b0	.373
136	CORnet-S	.372
137	mobilenet_v2_1_0_192	.372
138	mobilenet_v2_0_75_160	.372
139	resnet18_imagenet_full	.370
140	resnet101_imagenet_full	.369
141	grcnn	.369
142	ReAlnet01	.369
143	BiT-S-R101x3	.367
144	mobilenet_v2_1_0_224	.367
145	resnet50-SIN_IN	.366
146	nasnet_mobile-tf	.364
147	efficientnet_b2_imagenet_full	.362
148	mobilenet_v2_0.75_192-tf	.358
149	resnet_152_v2	.358
150	alexnet_early_checkpoint	.356
151	BiT-S-R50x3	.355
152	mobilenet_v2_1.4_224-tf	.353
153	resnet50	.352
154	alexnet_random_linf8_perturb	.350
155	mobilenet_v2_1_3_224	.348
156	resnet-18	.341
157	AdvProp_efficientnet-b8	.339
158	mobilenet_v1_0.5_224	.339
159	resnet_50_v1_spiking	.339
160	mobilenet_v2_1_0_160	.336
161	resnet50_imagenet_full	.336
162	resnet-50-pytorch	.334
163	resnet_SIN_IN_FT_IN	.334
164	resnet50-SIN_IN_IN	.334
165	resnet50-sup	.334
166	mobilenet_v2_0.75_160-tf	.334
167	resnet50_tutorial	.334
168	resnet_50_v1	.334
169	mobilenet_v2_1.0_224-tf	.331
170	blt_vs	.331
171	mobilenet_v2_1.0_192-tf	.325
172	antialiased-r50	.325
173	shufflenet_v2_x1_0	.325
174	resnet-152_v1-tf	.322
175	resnet152_imagenet_full	.319
176	mobilenet_v2_1.3_224-tf	.316
177	mobilenet_v1_1.0_192	.312
178	Res2Net50_26w_4s	.309
179	regnet_y_400mf	.305
180	mobilenet_v2_1.0_160-tf	.303
181	resnet-50_v1-tf	.300
182	antialias-resnet152	.300
183	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.298
184	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.298
185	voneresnet-50-non_stochastic	.295
186	mobilenet_v1_0.75_224	.295
187	mobilenet_v2_0.75_128	.295
188	alexnet	.294
189	resnet_152_v1	.294
190	mobilenet_v1_0.75_192	.291
191	custom_model_cv_18_dagger_408	.289
192	mobilenet_v2_0.5_160	.287
193	mobilenet_v2_0.35_160	.280
194	mobilenet_v2_0.5_128	.278
195	mobilenet_v2_1.0_128-tf	.275
196	mobilenet_v1_0.5_160	.272
197	resnet50_robust_l2_eps1	.269
198	resnet_50_v1_spiking_l4a	.269
199	mobilenet_v1_0.75_160	.267
200	mobilenet_v2_1_0_128	.267
201	alexnet-baseline	.264
202	alexnet	.264
203	alexnet	.264
204	alexnet	.264
205	alexnet	.264
206	alexnet	.264
207	alexnet	.264
208	alexnet	.264
209	alexnet_ks_torevert	.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_03	.062
271	resnet50-barlow	.059
272	resnet50-vicregl0p75	.059
273	alexnet_training_seed_07	.053
274	resnet_50_v1_spiking_l3	.053
275	mobilevit_small	.052
276	resnet50-simclr-vissl	.050
277	ViT-B/32
278	resnet-18-LC_w_sh_100_iter_conv_init_m
279	BiT-M-R50x1
280	resnet-18-LC_w_sh_1_iter_conv_init_m
281	BiT-M-R152x2
282	resnet-18-LC_w_sh_1_iter_m
283	resnet-18-LC_w_sh_10_iter_m
284	BiT-M-R50x3
285	resnet-18-LC_w_sh_100_iter_m
286	resnet-18-LC_w_sh_10_iter_conv_init_m
287	FrankRobWobv0
288	0.5x_resnet-18_LC
289	CORnetZ_CIFAR10
290	resnet18-local_aggregation
291	r3m_resnet18
292	resnet18-deepcluster
293	CORnet-Z
294	vggface
295	resnet18-instance_recognition
296	my-model
297	0.5x_resnet-18_LC_w_sh_10_iter
298	r3m_resnet50
299	r3m_resnet34
300	resnet-18-LC_m
301	resnet18-supervised
302	r3m_resnet50_nocrop
303	resnet-18-LC_1st_conv_m
304	resnet-18-LC_conv_init_m
305	resnet18-contrastive_multiview
306	0.5x_resnet-18
307	0.5x_resnet-18_LC_w_sh_100_iter
308	RN50
309	RN50
310	resnet18-colorization
311	resnet18-relative_position
312	resnet18-autoencoder
313	resnet18-simclr
314	ViT_L_32
315	ViT_B_32
316	resnet18-depth_prediction
317	ViT_B_16_imagenet1k
318	ViT_B_16
319	r3m_resnet34_nocrop
320	resnet18-contrastive_predictive
321	resnet-18-LC_untrained
322	resnet-18_untrained
323	resnet-50x2_untrained
324	dcgan
325	unet_entire
326	CORnetZ_CIFAR10_bs32_20_04
327	v1-pyr-nodown
328	pixels
329	dorinet_cornet_z
330	omnivore_swinT
331	resnet50
332	openclip
333	vonealexnet_gaussian_noise_std4_fixed
334	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
335	yudixie_resnet50_category_class_0_240908
336	resnet18_imagenet21kP
337	ConvLSTM
338	PredRNN
339	SimVP
340	TAU
341	MIM
342	hmax
343	ReAlnet01_cornet
344	yolos_tiny
345	yudixie_resnet50_imagenet1kpret_0_240312
346	yudixie_resnet18_translation_reg_0_240719
347	yudixie_resnet18_distance_reg_0_240719
348	ReAlnet02_cornet
349	yudixie_resnet18_imagenet1kpret_0_240719
350	yudixie_resnet50_rotation_reg_0_240908
351	yudixie_resnet50_distance_translation_0_240908
352	yudixie_resnet50_distance_rotation_0_240908
353	resnet18_ecoset_full
354	ReAlnet04_cornet
355	resnet34_ecoset_full
356	resnet50_ecoset_full
357	resnet101_ecoset_full
358	ReAlnet03_cornet
359	resnet152_ecoset_full
360	yudixie_resnet18_translation_rotation_0_240719
361	resnet-18_test_m
362	yudixie_resnet18_category_class_0_240719
363	ReAlnet05_cornet
364	yudixie_resnet18_random_0_240719
365	yudixie_resnet18_cat_obj_class_all_latents_0_240719
366	yudixie_resnet18_distance_translation_rotation_0_240719
367	yudixie_resnet18_object_class_0_240719
368	ReAlnet06_cornet
369	SeLaVi-AVE
370	r3d_18
371	yudixie_resnet50_translation_rotation_0_240908
372	ReAlnet07_cornet
373	ReAlnet01_ALL
374	omnivore_swinB
375	mc3_18
376	ReAlnet02_ALL
377	s3d
378	VOneCORnet-S
379	ReAlnet08_cornet
380	resnet50-vicreg
381	mvit_v1_b
382	yudixie_resnet18_rotation_reg_0_240719
383	ReAlnet03_ALL
384	ReAlnet09_cornet
385	omnivore_swinS
386	SeLaVi-VGG-Sound
387	ReAlnet06_ALL
388	SeLaVi-Kinetics-Sound
389	ReAlnet10_cornet
390	SeLaVi-Kinetics400
391	r2plus1d_18
392	mvit_v2_s
393	yudixie_resnet50_random_0_240908
394	ReAlnet07_ALL
395	yudixie_resnet50_imagenet1kpret_0_240908
396	yudixie_resnet50_distance_translation_rotation_0_240908
397	ReAlnet08_ALL
398	yudixie_resnet50_cat_obj_class_all_latents_0_240908
399	yudixie_resnet18_distance_rotation_0_240719
400	ReAlnet09_ALL
401	yudixie_resnet18_distance_translation_0_240719
402	yudixie_resnet50_translation_reg_0_240908
403	resnet50_primary_visual_cortex
404	yudixie_resnet50_object_class_0_240908
405	yudixie_resnet50_distance_reg_0_240908
406	ReAlnet10_ALL
407	briaai_rmbg_1_4
408	ReAlnet04_ALL
409	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