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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021eidolonIII-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	.758
2	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.733
3	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.731
4	AdvProp_efficientnet-b6	.717
5	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.715
6	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.715
7	AdvProp_efficientnet-b7	.715
8	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.715
9	vit_large_patch14_clip_224:openai_ft_in1k	.708
10	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.700
11	convnext_large:fb_in22k_ft_in1k	.696
12	resnet50-SIN	.692
13	convnext_xlarge:fb_in22k_ft_in1k	.688
14	deit_large_imagenet_full_seed-0	.683
15	AdvProp_efficientnet-b4	.683
16	AT_efficientnet-b4	.683
17	vit_large_patch14_clip_336:laion2b_ft_in1k	.679
18	deit_base_imagenet_full_seed-0	.665
19	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.665
20	AdvProp_efficientnet-b8	.662
21	effnetv2m_custom384	.662
22	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.658
23	ViT_L_32_imagenet1k	.654
24	vit_large_patch14_clip_224:laion2b_ft_in1k	.654
25	vit_relpos_base_patch16_clsgap_224:sw_in1k	.652
26	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.646
27	resnext101_32x32d_wsl	.646
28	resnext101_32x8d_wsl	.637
29	convnext_tiny:in12k_ft_in1k	.635
30	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.635
31	BiT-S-R152x2	.633
32	resnext101_32x48d_wsl	.633
33	convnext_large_imagenet_full_seed-0	.631
34	efficientnet-b7	.627
35	resnet-152_v1-tf	.627
36	focalnet_tiny_lrf_in1k	.627
37	AT_efficientnet-b2	.625
38	AdvProp_efficientnet-b2	.625
39	convnext_tiny_sup	.625
40	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.623
41	deit_small_imagenet_full_seed-0	.623
42	resnet50-SIN_IN	.619
43	resnext101_32x16d_wsl	.619
44	convnext_base_imagenet_full_seed-0	.619
45	fixres_resnext101_32x48d_wsl	.613
46	voneresnet-50-robust	.613
47	cvt_cvt-13-224-in1k_4_LucyV4	.610
48	convnext_small_imagenet_full_seed-0	.608
49	vit_base_patch16_clip_224:openai_ft_in1k	.608
50	resnet50_robust_l2_eps1	.608
51	efficientnet_b0	.606
52	AdvProp_efficientnet-b0	.604
53	AlexNet_SIN	.602
54	AlexNet_SIN_fov4	.602
55	AlexNet_SIN_fov12	.602
56	AlexNet_SIN_fov16	.602
57	resnet-101_v2-tf	.602
58	cvt_cvt-13-384-in1k_4_LucyV4	.596
59	BiT-S-R101x1	.596
60	resnet-50-robust	.596
61	imagenet_l2_3_0	.596
62	resnet50_robust_l2_eps3	.596
63	ViT_B_16_imagenet1k	.594
64	convnext_tiny_imagenet_full_seed-0	.594
65	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
66	tv_efficientnet-b1	.590
67	convnext_base:clip_laiona_augreg_ft_in1k_384	.590
68	resnet-50_v1-tf	.588
69	inception_v3-tf	.585
70	resnet-50-pytorch	.585
71	resnet50-sup	.585
72	cv_18_dagger_408_pretrained	.585
73	resnet50_tutorial	.585
74	resnet_50_v1	.585
75	resnet_101_v1	.583
76	blt_vs	.581
77	grcnn_109	.581
78	resnet-101_v1-tf	.579
79	BiT-S-R50x3	.577
80	BiT-S-R101x3	.575
81	resnet50_ewconplus_wzc	.575
82	ViT_B_32_imagenet1k	.573
83	cvt_cvt-21-224-in1k_4_LucyV4	.573
84	densenet-169	.573
85	densenet-169-keras	.571
86	antialias-resnet152	.571
87	inception_v4-tf	.569
88	antialiased-rnext101_32x8d	.569
89	resnet_50_v1_spiking	.567
90	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.565
91	xception-keras	.565
92	resnet50-vitoimagevidnet8	.560
93	densenet-201	.558
94	densenet_201_pytorch	.558
95	eBarlow_lmda_02_1000ep	.558
96	resnet_SIN_IN_FT_IN	.558
97	resnet50-SIN_IN_IN	.558
98	resnet-152_v2-tf	.556
99	densenet-201-keras	.556
100	imagenet_l2_10_0	.556
101	resnet50-moclr8deg	.556
102	BiT-S-R152x4	.554
103	inception_v1-tf	.554
104	resnet-152_v2_pytorch	.552
105	resnet_101_v2	.552
106	resnet50_l2_3_robust	.552
107	BiT-S-R50x1	.550
108	vonegrcnn_47e	.550
109	voneresnet-50	.550
110	resnet-50_v2-tf	.550
111	inception_v2-tf	.550
112	mobilenet_v2_1.0_96	.550
113	shufflenet_v2_x1_0	.550
114	grcnn	.550
115	vonegrcnn_47e	.550
116	resnet_152_v1	.550
117	vonegrcnn_62e_nobn	.548
118	vonegrcnn_62e_nobn	.548
119	Res2Net50_26w_4s	.546
120	cvt_cvt-21-384-in1k_4_LucyV4	.546
121	resnet50-VITO-8deg-cc	.542
122	inception_v3	.542
123	regnet_y_400mf	.540
124	resnet-34	.540
125	mobilenet_v2_0.5_128	.540
126	CORnet-S	.540
127	antialiased-r50	.540
128	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.537
129	nasnet_large-tf	.535
130	mobilenet_v2_1.3_224-tf	.535
131	vonegrcnn_52e_full	.533
132	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.531
133	resnet50	.529
134	inception_v4	.527
135	mobilenet_v2_1.0_160-tf	.527
136	mobilenet_v2_0.75_96	.527
137	effnetb1_272x240	.527
138	mobilenet_v2_1.4_224-tf	.525
139	vonegrcnn_52e_full	.523
140	voneresnet-50-non_stochastic	.523
141	efficientnet_b2_imagenet_full	.521
142	pnasnet_large-tf	.521
143	resnet-18	.521
144	mobilenet_v2_1.0_192-tf	.519
145	mobilenet_v2_1.0_224-tf	.519
146	resnet152_imagenet_full	.517
147	resnet_152_v2	.515
148	mobilenet_v2_0.5_192-tf	.515
149	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.515
150	resnet50_moco_v2	.512
151	nasnet_mobile	.512
152	inception_v1	.510
153	mobilenet_v2_1.0_128-tf	.508
154	mobilenet_v1_1.0_128	.506
155	mobilenet_v2_0.75_160-tf	.506
156	mobilenet_v1_0.75_224	.504
157	nasnet_large	.504
158	efficientnet_b0_imagenet_full	.502
159	densenet-121	.502
160	mobilenet_v2_0.5_96	.502
161	mobilenet_v1_1.0_160	.502
162	densenet-121-keras	.500
163	mobilenet_v1_0.75_160	.500
164	mobilenet_v2_1_3_224	.500
165	mobilenet_v1_0.75_128	.498
166	pnasnet_large_halfPrecision	.498
167	resnet101_imagenet_full	.498
168	pnasnet_large	.498
169	resnet50_eMMCR_Vanilla	.498
170	efficientnet_b1_imagenet_full	.496
171	mobilenet_v2_0.5_160	.496
172	resnet_50_v2	.494
173	mobilenet_v2_0.75_192-tf	.492
174	vgg-16-keras	.492
175	mobilenet_v2_1_0_160	.490
176	mobilenet_v2_0.35_192	.490
177	mobilenet_v1_0.5_128	.490
178	mobilenet_v2_0.75_128	.487
179	resnet50_linf_8_robust	.485
180	mobilenet_v2_0.75_224-tf	.483
181	vgg-19-keras	.483
182	mobilenet_v1_0.5_160	.483
183	mobilenet_v1_1.0_192	.481
184	mobilenet_v1_1.0_224	.477
185	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.475
186	mobilenet_v1_0.75_192	.475
187	mobilenet_v2_0.35_128	.475
188	ReAlnet06	.475
189	ReAlnet04	.473
190	resnet34_imagenet_full	.471
191	mobilenet_v2_0.5_224-tf	.471
192	mobilenet_v1_0.5_192	.469
193	alexnet_robust_correct	.469
194	ReAlnet08	.469
195	mobilenet_v2_1-4_224_pytorch	.469
196	mobilenet_v2_1_4_224	.469
197	ReAlnet05	.469
198	convnext_femto_ols:d1_in1k	.469
199	CLIP-RN50	.467
200	mobilenet_v2_0.35_160	.467
201	mobilenet_v2_0.35_96	.463
202	ReAlnet07	.463
203	mobilenet_v2_1_0_128	.463
204	mobilenet_v2_1_0_192	.463
205	xception	.463
206	mobilenet_v1_0.5_224	.460
207	ReAlnet03	.460
208	mobilenet_v2_0_5_192	.458
209	mobilenet_v2_0_75_160	.458
210	0.5x_resnet-18_LC_w_sh_1_iter	.456
211	ReAlnet10	.456
212	resnet50_imagenet_full	.452
213	mobilenet_v2_1_0_224	.452
214	mobilenet_v2_0_75_224	.450
215	ReAlnet01	.448
216	ReAlnet09	.446
217	resnet18_imagenet_full	.444
218	custom_model_cv_18_dagger_408	.442
219	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.438
220	alexnet	.438
221	alexnet_ks_torevert	.438
222	alexnet_fov4	.438
223	alexnet_fov12	.438
224	alexnet_fov16	.438
225	mobilenet_v1_0.25_224	.435
226	convnext_small_imagenet_100_seed-0	.435
227	ReAlnet02	.435
228	mobilenet_v2_0_75_192	.433
229	alexnet_l2_3_robust	.425
230	mobilenet_v2_0.35_224	.423
231	vgg_16	.423
232	mobilenet_v2_0_5_224	.421
233	alexnet_linf_8_robust	.417
234	pnasnet_large_pytorch	.417
235	alexnet_random_linf8_perturb	.412
236	nasnet_mobile-tf	.408
237	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.406
238	vgg_19	.406
239	alexnet_early_checkpoint	.404
240	resnet_50_v1_spiking_l4a	.404
241	mobilenet_v1_0.25_192	.400
242	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.396
243	mobilenet_v1_0.25_128	.396
244	squeezenet1_1	.392
245	mobilenet_v1_0.25_160	.390
246	deit_small_imagenet_100_seed-0	.373
247	squeezenet1_0	.358
248	resnet50_imagenet_100_seed-0	.327
249	resnet_50_v1_spiking_l4	.219
250	convnext_small_imagenet_10_seed-0	.117
251	resnet50_imagenet_10_seed-0	.106
252	deit_small_imagenet_10_seed-0	.094
253	resnet50-simclr-vissl	.087
254	deit_small_imagenet_1_seed-0	.081
255	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.069
256	resnet_50_v1_spiking_l3	.067
257	artResNet18_1	.067
258	resnet-50_untrained	.062
259	resnet50-meshes-lt-100-original-scratch	.062
260	alexnet_training_seed_09	.062
261	alexnet_training_seed_05	.062
262	alexnet_training_seed_06	.062
263	alexnet_training_seed_02	.062
264	alexnet_training_seed_04	.062
265	alexnet_training_seed_08	.062
266	alexnet_training_seed_10	.062
267	alexnet_training_seed_01	.062
268	alexnet_training_seed_01_fov4	.062
269	alexnet_training_seed_01_fov12	.062
270	alexnet_training_seed_01_fov16	.062
271	resnet50-vicregl0p75	.062
272	alexnet_training_seed_03	.062
273	resnet50-meshes-lt-100-original-pretrained	.060
274	convnext_small_imagenet_1_seed-0	.060
275	resnet50_imagenet_1_seed-0	.060
276	alexnet_training_seed_07	.054
277	barlow-twins-resnet50	.052
278	resnet50-barlow	.048
279	mobilevit_small	.046
280	resnet50-vicregl0p9	.029
281	ViT-B/32
282	resnet-18-LC_w_sh_100_iter_conv_init_m
283	BiT-M-R50x1
284	resnet-18-LC_w_sh_1_iter_conv_init_m
285	BiT-M-R152x2
286	resnet-18-LC_w_sh_1_iter_m
287	resnet-18-LC_w_sh_10_iter_m
288	BiT-M-R50x3
289	resnet-18-LC_w_sh_100_iter_m
290	resnet-18-LC_w_sh_10_iter_conv_init_m
291	FrankRobWobv0
292	0.5x_resnet-18_LC
293	CORnetZ_CIFAR10
294	resnet18-local_aggregation
295	r3m_resnet18
296	resnet18-deepcluster
297	CORnet-Z
298	vggface
299	my-model
300	resnet18-instance_recognition
301	0.5x_resnet-18_LC_w_sh_10_iter
302	r3m_resnet50
303	r3m_resnet34
304	resnet-18-LC_m
305	resnet18-supervised
306	r3m_resnet50_nocrop
307	resnet-18-LC_1st_conv_m
308	resnet-18-LC_conv_init_m
309	resnet18-contrastive_multiview
310	0.5x_resnet-18
311	RN50
312	0.5x_resnet-18_LC_w_sh_100_iter
313	RN50
314	resnet18-colorization
315	resnet18-relative_position
316	resnet18-autoencoder
317	resnet18-simclr
318	ViT_L_32
319	ViT_B_32
320	resnet18-depth_prediction
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	omnivore_swinS
335	resnet50
336	openclip
337	vonealexnet_gaussian_noise_std4_fixed
338	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
339	yudixie_resnet50_category_class_0_240908
340	yudixie_resnet50_distance_reg_0_240908
341	resnet18_imagenet21kP
342	ConvLSTM
343	PredRNN
344	yolos_tiny
345	SimVP
346	TAU
347	MIM
348	hmax
349	briaai_rmbg_1_4
350	inception_v3_pytorch
351	ReAlnet01_cornet
352	yudixie_resnet18_distance_translation_0_240719
353	yudixie_resnet18_translation_rotation_0_240719
354	ReAlnet03_cornet
355	ReAlnet02_cornet
356	yudixie_resnet18_distance_reg_0_240719
357	yudixie_resnet18_rotation_reg_0_240719
358	yudixie_resnet18_imagenet1kpret_0_240719
359	compact_resnet50_V4
360	yudixie_resnet50_distance_translation_0_240908
361	yudixie_resnet50_rotation_reg_0_240908
362	vit_base_patch14_dinov2-lvd142m
363	yudixie_resnet50_distance_rotation_0_240908
364	ReAlnet01_cornet_fov4
365	resnet18_ecoset_full
366	resnet34_ecoset_full
367	compact_resnet50_IT
368	resnet50_ecoset_full
369	vit_base_patch14_reg4_dinov2-lvd142m
370	resnet101_ecoset_full
371	ReAlnet01_cornet_fov12
372	compact_vgg19_V4
373	ReAlnet04_cornet
374	resnet152_ecoset_full
375	yudixie_resnet50_translation_reg_0_240908
376	resnet-18_test_m
377	ReAlnet05_cornet
378	yudixie_resnet18_random_0_240719
379	aughost_fair_resnet18
380	yudixie_resnet18_cat_obj_class_all_latents_0_240719
381	yudixie_resnet18_distance_translation_rotation_0_240719
382	SeLaVi-AVE
383	yudixie_resnet18_object_class_0_240719
384	ReAlnet06_cornet
385	yudixie_resnet18_translation_reg_0_240719
386	compact_resnet50_robust_V4
387	ReAlnet01_cornet_fov16
388	r3d_18
389	yudixie_resnet50_translation_rotation_0_240908
390	ReAlnet01_ALL
391	ReAlnet07_cornet
392	mc3_18
393	ReAlnet02_ALL
394	VOneCORnet-S
395	omnivore_swinB
396	s3d
397	ReAlnet08_cornet
398	resnet50-vicreg
399	mvit_v1_b
400	compact_cornets_V4
401	ReAlnet09_cornet
402	ReAlnet04_ALL
403	compact_cornets_IT
404	SeLaVi-Kinetics400
405	ReAlnet05_ALL
406	vit_base_patch16_dinov3-lvd1689m
407	SeLaVi-VGG-Sound
408	ReAlnet06_ALL
409	SeLaVi-Kinetics-Sound
410	compact_vgg19_IT
411	compact_resnet50_robust_IT
412	ReAlnet10_cornet
413	ReAlnet07_ALL
414	yudixie_resnet50_random_0_240908
415	r2plus1d_18
416	mvit_v2_s
417	ReAlnet08_ALL
418	yudixie_resnet18_category_class_0_240719
419	yudixie_resnet18_distance_rotation_0_240719
420	ReAlnet09_ALL
421	yudixie_resnet50_imagenet1kpret_0_240312
422	yudixie_resnet50_cat_obj_class_all_latents_0_240908
423	yudixie_resnet50_object_class_0_240908
424	resnet50_primary_visual_cortex
425	ReAlnet10_ALL
426	yudixie_resnet50_distance_translation_rotation_0_240908
427	yudixie_resnet50_imagenet1kpret_0_240908
428	ReAlnet03_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: Geirhos2021eidolonIII

Metric: top1