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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonI-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	voneresnet-50-robust	.654
2	resnet50_robust_l2_eps1	.637
3	deit_large_imagenet_full_seed-0	.630
4	resnet50-SIN	.627
5	resnet-50-robust	.625
6	AdvProp_efficientnet-b6	.625
7	imagenet_l2_3_0	.625
8	imagenet_l2_10_0	.625
9	resnet50_robust_l2_eps3	.625
10	AdvProp_efficientnet-b7	.618
11	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.606
12	vit_large_patch14_clip_224:openai_ft_in1k	.606
13	AdvProp_efficientnet-b4	.603
14	AT_efficientnet-b4	.603
15	resnet50_l2_3_robust	.603
16	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.603
17	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.601
18	AdvProp_efficientnet-b8	.595
19	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.590
20	blt_vs	.588
21	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.585
22	deit_base_imagenet_full_seed-0	.584
23	mobilenet_v2_1.0_96	.583
24	convnext_large:fb_in22k_ft_in1k	.583
25	ViT_L_32_imagenet1k	.581
26	convnext_xlarge:fb_in22k_ft_in1k	.579
27	vit_large_patch14_clip_224:laion2b_ft_in1k	.578
28	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.576
29	AlexNet_SIN	.575
30	AlexNet_SIN_fov4	.575
31	AlexNet_SIN_fov12	.575
32	AlexNet_SIN_fov16	.575
33	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.571
34	vit_large_patch14_clip_336:laion2b_ft_in1k	.566
35	voneresnet-50	.565
36	convnext_tiny_sup	.562
37	resnext101_32x48d_wsl	.560
38	mobilenet_v2_0.75_96	.559
39	mobilenet_v2_0.5_128	.559
40	ViT_B_16_imagenet1k	.557
41	resnet50_linf_8_robust	.557
42	resnet50_ewconplus_wzc	.556
43	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.556
44	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.555
45	mobilenet_v1_0.75_128	.551
46	mobilenet_v2_0.5_96	.550
47	deit_small_imagenet_full_seed-0	.550
48	mobilenet_v2_0.75_128	.547
49	vit_relpos_base_patch16_clsgap_224:sw_in1k	.547
50	resnext101_32x8d_wsl	.546
51	resnet50-SIN_IN	.546
52	alexnet_robust_correct	.546
53	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.545
54	resnet-50_v1-tf	.544
55	vonegrcnn_62e_nobn	.542
56	grcnn_109	.542
57	mobilenet_v2_0.75_160-tf	.541
58	resnet_101_v1	.541
59	vonegrcnn_47e	.539
60	resnext101_32x16d_wsl	.537
61	focalnet_tiny_lrf_in1k	.537
62	vonegrcnn_47e	.537
63	mobilenet_v2_0.5_160	.536
64	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.536
65	mobilenet_v1_0.5_128	.535
66	ViT_B_32_imagenet1k	.535
67	antialiased-rnext101_32x8d	.535
68	vonegrcnn_52e_full	.535
69	resnet-50-pytorch	.534
70	resnet50-sup	.534
71	resnet50_tutorial	.534
72	resnet_50_v1	.534
73	vonegrcnn_62e_nobn	.534
74	mobilenet_v2_1.0_128-tf	.532
75	resnext101_32x32d_wsl	.532
76	convnext_tiny:in12k_ft_in1k	.530
77	CORnet-S	.529
78	mobilenet_v2_0.75_192-tf	.529
79	resnet-152_v1-tf	.529
80	densenet-201-keras	.527
81	fixres_resnext101_32x48d_wsl	.527
82	alexnet_l2_3_robust	.527
83	densenet-201	.527
84	vonegrcnn_52e_full	.526
85	densenet_201_pytorch	.526
86	eBarlow_lmda_02_1000ep	.526
87	resnet_152_v1	.526
88	efficientnet-b7	.524
89	AT_efficientnet-b2	.522
90	AdvProp_efficientnet-b2	.522
91	convnext_large_imagenet_full_seed-0	.522
92	inception_v2-tf	.521
93	antialias-resnet152	.521
94	inception_v1-tf	.520
95	mobilenet_v2_1.0_192-tf	.520
96	mobilenet_v1_1.0_128	.520
97	voneresnet-50-non_stochastic	.519
98	mobilenet_v2_0.35_192	.519
99	mobilenet_v2_1_0_128	.519
100	vit_base_patch16_clip_224:openai_ft_in1k	.519
101	convnext_base:clip_laiona_augreg_ft_in1k_384	.519
102	mobilenet_v2_0.5_192-tf	.517
103	resnet_50_v1_spiking	.517
104	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.516
105	mobilenet_v1_0.5_192	.516
106	AdvProp_efficientnet-b0	.516
107	resnet-34	.516
108	tv_efficientnet-b1	.516
109	resnet_SIN_IN_FT_IN	.515
110	resnet-101_v2-tf	.515
111	resnet50-SIN_IN_IN	.515
112	densenet-169-keras	.515
113	effnetv2m_custom384	.515
114	mobilenet_v2_0.35_160	.515
115	custom_model_cv_18_dagger_408	.515
116	densenet-169	.515
117	resnet-101_v1-tf	.514
118	shufflenet_v2_x1_0	.514
119	resnet50-vitoimagevidnet8	.514
120	mobilenet_v1_0.5_160	.512
121	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.512
122	densenet-121	.511
123	densenet-121-keras	.510
124	mobilenet_v1_0.75_160	.510
125	mobilenet_v2_0.35_96	.510
126	mobilenet_v1_1.0_160	.510
127	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.509
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.507
129	mobilenet_v1_1.0_192	.507
130	resnet-152_v2-tf	.506
131	resnet50-moclr8deg	.505
132	mobilenet_v2_0_75_160	.505
133	grcnn	.505
134	resnet-50_v2-tf	.504
135	mobilenet_v1_0.75_192	.502
136	resnet101_imagenet_full	.502
137	mobilenet_v2_0.35_128	.501
138	ReAlnet06	.501
139	0.5x_resnet-18_LC_w_sh_1_iter	.500
140	convnext_base_imagenet_full_seed-0	.500
141	efficientnet_b0	.499
142	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.499
143	resnet152_imagenet_full	.499
144	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.497
145	alexnet_linf_8_robust	.497
146	Res2Net50_26w_4s	.497
147	resnet50_eMMCR_Vanilla	.497
148	antialiased-r50	.496
149	resnet-152_v2_pytorch	.496
150	inception_v3-tf	.495
151	mobilenet_v1_0.25_192	.495
152	mobilenet_v2_0_75_192	.494
153	inception_v3_pytorch	.494
154	cvt_cvt-13-224-in1k_4_LucyV4	.494
155	cv_18_dagger_408_pretrained	.492
156	mobilenet_v2_1.0_160-tf	.491
157	resnet-18	.490
158	efficientnet_b2_imagenet_full	.490
159	efficientnet_b1_imagenet_full	.490
160	mobilenet_v2_0.5_224-tf	.489
161	convnext_tiny_imagenet_full_seed-0	.489
162	resnet50_moco_v2	.487
163	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.486
164	efficientnet_b0_imagenet_full	.486
165	resnet50	.485
166	convnext_small_imagenet_full_seed-0	.485
167	mobilenet_v2_1.4_224-tf	.484
168	vgg-19-keras	.482
169	inception_v1	.482
170	nasnet_large-tf	.481
171	mobilenet_v2_0_5_192	.481
172	cvt_cvt-13-384-in1k_4_LucyV4	.481
173	mobilenet_v1_0.5_224	.479
174	cvt_cvt-21-224-in1k_4_LucyV4	.477
175	mobilenet_v2_1_0_192	.477
176	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.477
177	BiT-S-R101x1	.476
178	BiT-S-R101x3	.476
179	mobilenet_v1_0.25_224	.476
180	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.475
181	mobilenet_v2_1.3_224-tf	.475
182	alexnet	.474
183	mobilenet_v1_1.0_224	.474
184	alexnet_ks_torevert	.474
185	regnet_y_400mf	.474
186	alexnet_fov4	.474
187	alexnet_fov12	.474
188	alexnet_fov16	.474
189	mobilenet_v2_0.75_224-tf	.472
190	xception-keras	.471
191	resnet50-VITO-8deg-cc	.471
192	mobilenet_v2_0_5_224	.471
193	nasnet_mobile	.471
194	mobilenet_v1_0.25_160	.470
195	mobilenet_v2_1_0_160	.470
196	mobilenet_v2_0_75_224	.469
197	cvt_cvt-21-384-in1k_4_LucyV4	.469
198	ReAlnet05	.469
199	BiT-S-R152x2	.468
200	ReAlnet08	.468
201	mobilenet_v2_1_3_224	.466
202	inception_v4-tf	.464
203	resnet50_imagenet_full	.464
204	ReAlnet09	.464
205	ReAlnet10	.464
206	pnasnet_large-tf	.463
207	mobilenet_v1_0.25_128	.463
208	resnet_101_v2	.461
209	ReAlnet03	.461
210	ReAlnet07	.460
211	ReAlnet04	.460
212	vgg-16-keras	.459
213	ReAlnet01	.458
214	mobilenet_v2_1.0_224-tf	.455
215	effnetb1_272x240	.454
216	nasnet_large	.454
217	pnasnet_large_halfPrecision	.453
218	resnet18_imagenet_full	.453
219	pnasnet_large	.453
220	BiT-S-R50x1	.451
221	resnet34_imagenet_full	.450
222	mobilenet_v2_1-4_224_pytorch	.449
223	mobilenet_v2_1_4_224	.449
224	mobilenet_v2_1_0_224	.448
225	ReAlnet02	.448
226	inception_v3	.446
227	mobilenet_v2_0.35_224	.445
228	BiT-S-R152x4	.444
229	resnet_50_v1_spiking_l4a	.444
230	xception	.443
231	convnext_femto_ols:d1_in1k	.439
232	mobilenet_v1_0.75_224	.438
233	inception_v4	.434
234	alexnet_random_linf8_perturb	.431
235	BiT-S-R50x3	.430
236	deit_small_imagenet_100_seed-0	.430
237	vgg_16	.426
238	squeezenet1_1	.425
239	resnet_50_v2	.425
240	resnet_152_v2	.424
241	alexnet_early_checkpoint	.422
242	vgg_19	.419
243	pnasnet_large_pytorch	.412
244	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.409
245	convnext_small_imagenet_100_seed-0	.404
246	nasnet_mobile-tf	.393
247	CLIP-RN50	.376
248	squeezenet1_0	.359
249	resnet50_imagenet_100_seed-0	.357
250	resnet_50_v1_spiking_l4	.280
251	resnet50_imagenet_10_seed-0	.133
252	convnext_small_imagenet_10_seed-0	.106
253	deit_small_imagenet_10_seed-0	.101
254	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.085
255	resnet50_imagenet_1_seed-0	.076
256	deit_small_imagenet_1_seed-0	.075
257	resnet-50_untrained	.066
258	resnet50-meshes-lt-100-original-scratch	.062
259	alexnet_training_seed_05	.062
260	alexnet_training_seed_02	.062
261	alexnet_training_seed_04	.062
262	alexnet_training_seed_08	.062
263	alexnet_training_seed_10	.062
264	alexnet_training_seed_01	.062
265	alexnet_training_seed_01_fov4	.062
266	alexnet_training_seed_01_fov12	.062
267	alexnet_training_seed_01_fov16	.062
268	alexnet_training_seed_03	.062
269	alexnet_training_seed_09	.062
270	alexnet_training_seed_06	.059
271	alexnet_training_seed_07	.059
272	mobilevit_small	.056
273	resnet50-vicregl0p75	.054
274	resnet50-simclr-vissl	.052
275	resnet50-barlow	.051
276	resnet50-meshes-lt-100-original-pretrained	.051
277	artResNet18_1	.051
278	resnet_50_v1_spiking_l3	.050
279	resnet50-vicregl0p9	.050
280	barlow-twins-resnet50	.048
281	convnext_small_imagenet_1_seed-0	.044
282	ViT-B/32
283	resnet-18-LC_w_sh_100_iter_conv_init_m
284	BiT-M-R50x1
285	resnet-18-LC_w_sh_1_iter_conv_init_m
286	BiT-M-R152x2
287	resnet-18-LC_w_sh_1_iter_m
288	resnet-18-LC_w_sh_10_iter_m
289	BiT-M-R50x3
290	resnet-18-LC_w_sh_100_iter_m
291	resnet-18-LC_w_sh_10_iter_conv_init_m
292	FrankRobWobv0
293	0.5x_resnet-18_LC
294	CORnetZ_CIFAR10
295	resnet18-local_aggregation
296	r3m_resnet18
297	resnet18-deepcluster
298	CORnet-Z
299	vggface
300	my-model
301	resnet18-instance_recognition
302	0.5x_resnet-18_LC_w_sh_10_iter
303	r3m_resnet50
304	r3m_resnet34
305	resnet-18-LC_m
306	resnet18-supervised
307	r3m_resnet50_nocrop
308	resnet-18-LC_1st_conv_m
309	resnet-18-LC_conv_init_m
310	resnet18-contrastive_multiview
311	0.5x_resnet-18
312	RN50
313	0.5x_resnet-18_LC_w_sh_100_iter
314	RN50
315	resnet18-colorization
316	resnet18-relative_position
317	resnet18-autoencoder
318	resnet18-simclr
319	ViT_L_32
320	ViT_B_32
321	resnet18-depth_prediction
322	ViT_B_16
323	r3m_resnet34_nocrop
324	yudixie_resnet50_object_class_0_240908
325	resnet18-contrastive_predictive
326	resnet-18-LC_untrained
327	resnet-18_untrained
328	resnet-50x2_untrained
329	dcgan
330	unet_entire
331	CORnetZ_CIFAR10_bs32_20_04
332	v1-pyr-nodown
333	pixels
334	dorinet_cornet_z
335	omnivore_swinT
336	omnivore_swinS
337	resnet50
338	openclip
339	vonealexnet_gaussian_noise_std4_fixed
340	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
341	ReAlnet10_ALL
342	yudixie_resnet50_imagenet1kpret_0_240312
343	yudixie_resnet50_category_class_0_240908
344	yudixie_resnet50_distance_reg_0_240908
345	resnet18_imagenet21kP
346	ConvLSTM
347	PredRNN
348	SimVP
349	TAU
350	MIM
351	hmax
352	briaai_rmbg_1_4
353	ReAlnet01_cornet
354	yolos_tiny
355	compact_resnet50_V4
356	yudixie_resnet18_rotation_reg_0_240719
357	ReAlnet02_cornet
358	yudixie_resnet50_distance_rotation_0_240908
359	vit_base_patch14_reg4_dinov2-lvd142m
360	yudixie_resnet50_distance_translation_0_240908
361	yudixie_resnet50_rotation_reg_0_240908
362	ReAlnet03_cornet
363	resnet18_ecoset_full
364	compact_resnet50_IT
365	ReAlnet01_cornet_fov4
366	resnet34_ecoset_full
367	resnet50_ecoset_full
368	vit_base_patch16_dinov3-lvd1689m
369	ReAlnet01_cornet_fov12
370	resnet101_ecoset_full
371	ReAlnet04_cornet
372	resnet152_ecoset_full
373	yudixie_resnet18_distance_translation_0_240719
374	aughost_fair_resnet18
375	yudixie_resnet18_translation_rotation_0_240719
376	compact_vgg19_V4
377	yudixie_resnet18_distance_rotation_0_240719
378	resnet-18_test_m
379	yudixie_resnet50_translation_reg_0_240908
380	yudixie_resnet18_random_0_240719
381	ReAlnet05_cornet
382	compact_resnet50_robust_V4
383	compact_vgg19_IT
384	yudixie_resnet18_distance_translation_rotation_0_240719
385	SeLaVi-AVE
386	yudixie_resnet18_object_class_0_240719
387	vit_base_patch14_dinov2-lvd142m
388	yudixie_resnet18_translation_reg_0_240719
389	ReAlnet06_cornet
390	ReAlnet01_cornet_fov16
391	r3d_18
392	ReAlnet01_ALL
393	yudixie_resnet18_distance_reg_0_240719
394	ReAlnet07_cornet
395	ReAlnet02_ALL
396	mc3_18
397	s3d
398	resnet50-vicreg
399	mvit_v1_b
400	omnivore_swinB
401	VOneCORnet-S
402	compact_cornets_V4
403	ReAlnet03_ALL
404	compact_cornets_IT
405	ReAlnet09_cornet
406	SeLaVi-Kinetics400
407	ReAlnet05_ALL
408	SeLaVi-VGG-Sound
409	ReAlnet06_ALL
410	SeLaVi-Kinetics-Sound
411	compact_resnet50_robust_IT
412	ReAlnet07_ALL
413	r2plus1d_18
414	ReAlnet10_cornet
415	mvit_v2_s
416	ReAlnet08_ALL
417	yudixie_resnet50_random_0_240908
418	yudixie_resnet18_category_class_0_240719
419	yudixie_resnet18_cat_obj_class_all_latents_0_240719
420	yudixie_resnet50_imagenet1kpret_0_240908
421	ReAlnet09_ALL
422	yudixie_resnet18_imagenet1kpret_0_240719
423	yudixie_resnet50_translation_rotation_0_240908
424	resnet50_primary_visual_cortex
425	ReAlnet08_cornet
426	yudixie_resnet50_distance_translation_rotation_0_240908
427	yudixie_resnet50_cat_obj_class_all_latents_0_240908
428	ReAlnet04_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: Geirhos2021eidolonI

Metric: top1