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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	dinov2_vitg14_lc	.800
2	dinov2_vitg14_reg_lc	.794
3	dinov2_vitl14_lc	.777
4	dinov2_vitl14_reg_lc	.767
5	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.758
6	dinov2_vitl14_lc_res336	.754
7	eva02_large_in22k	.752
8	convnext_xxlarge_clip	.750
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.733
10	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.731
11	AdvProp_efficientnet-b6	.717
12	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.715
13	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.715
14	AdvProp_efficientnet-b7	.715
15	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.715
16	vit_huge_clip336_laion	.710
17	vit_large_patch14_clip_224:openai_ft_in1k	.708
18	deit3_base_patch16_224_fb_in22k_ft_in1k	.708
19	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.700
20	convnext_large:fb_in22k_ft_in1k	.696
21	resnet50-SIN	.692
22	vit_large_clip336_openai	.692
23	convnext_xlarge:fb_in22k_ft_in1k	.688
24	convnext_base_fb_in22k_ft_in1k	.688
25	deit_large_imagenet_full_seed-0	.683
26	AdvProp_efficientnet-b4	.683
27	AT_efficientnet-b4	.683
28	vit_large_patch14_clip_336:laion2b_ft_in1k	.679
29	convnext_large_in22k_supervised	.677
30	deit_base_imagenet_full_seed-0	.665
31	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.665
32	AdvProp_efficientnet-b8	.662
33	effnetv2m_custom384	.662
34	convnext_base_fb_in1k	.660
35	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.658
36	ViT_L_32_imagenet1k	.654
37	vit_large_patch14_clip_224:laion2b_ft_in1k	.654
38	convnext_large_clip_laion2b_finelayers	.654
39	vit_relpos_base_patch16_clsgap_224:sw_in1k	.652
40	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.646
41	resnext101_32x32d_wsl	.646
42	resnext101_32x8d_wsl	.637
43	convnext_tiny:in12k_ft_in1k	.635
44	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.635
45	BiT-S-R152x2	.633
46	resnext101_32x48d_wsl	.633
47	convnext_large_imagenet_full_seed-0	.631
48	regnety_032	.627
49	efficientnet-b7	.627
50	resnet-152_v1-tf	.627
51	focalnet_tiny_lrf_in1k	.627
52	AT_efficientnet-b2	.625
53	AdvProp_efficientnet-b2	.625
54	convnext_tiny_sup	.625
55	seresnext50_32x4d	.625
56	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.623
57	deit_small_imagenet_full_seed-0	.623
58	resnet50-SIN_IN	.619
59	resnext101_32x16d_wsl	.619
60	convnext_base_imagenet_full_seed-0	.619
61	fixres_resnext101_32x48d_wsl	.613
62	voneresnet-50-robust	.613
63	cvt_cvt-13-224-in1k_4_LucyV4	.610
64	convnext_small_imagenet_full_seed-0	.608
65	vit_base_patch16_clip_224:openai_ft_in1k	.608
66	resnet50_robust_l2_eps1	.608
67	efficientnet_b0	.606
68	AdvProp_efficientnet-b0	.604
69	AlexNet_SIN	.602
70	AlexNet_SIN_fov4	.602
71	AlexNet_SIN_fov12	.602
72	AlexNet_SIN_fov16	.602
73	resnet-101_v2-tf	.602
74	cvt_cvt-13-384-in1k_4_LucyV4	.596
75	resnet50_hvs_wzc	.596
76	BiT-S-R101x1	.596
77	resnet-50-robust	.596
78	imagenet_l2_3_0	.596
79	resnet50_robust_l2_eps3	.596
80	ViT_B_16_imagenet1k	.594
81	convnext_tiny_imagenet_full_seed-0	.594
82	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
83	tv_efficientnet-b1	.590
84	convnext_base:clip_laiona_augreg_ft_in1k_384	.590
85	resnet-50_v1-tf	.588
86	inception_v3-tf	.585
87	resnet-50-pytorch	.585
88	resnet50-sup	.585
89	cv_18_dagger_408_pretrained	.585
90	resnet50_tutorial	.585
91	resnet_50_v1	.585
92	resnet_101_v1	.583
93	blt_vs	.581
94	grcnn_109	.581
95	resnet-101_v1-tf	.579
96	BiT-S-R50x3	.577
97	BiT-S-R101x3	.575
98	resnet50_ewconplus_wzc	.575
99	ViT_B_32_imagenet1k	.573
100	cvt_cvt-21-224-in1k_4_LucyV4	.573
101	densenet-169	.573
102	densenet-169-keras	.571
103	antialias-resnet152	.571
104	inception_v4-tf	.569
105	antialiased-rnext101_32x8d	.569
106	resnet_50_v1_spiking	.567
107	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.567
108	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.565
109	xception-keras	.565
110	resnet50_st_wzc_2	.562
111	resnet50-vitoimagevidnet8	.560
112	densenet-201	.558
113	densenet_201_pytorch	.558
114	eBarlow_lmda_02_1000ep	.558
115	resnet_SIN_IN_FT_IN	.558
116	resnet50-SIN_IN_IN	.558
117	resnet-152_v2-tf	.556
118	densenet-201-keras	.556
119	imagenet_l2_10_0	.556
120	resnet50-moclr8deg	.556
121	BiT-S-R152x4	.554
122	inception_v1-tf	.554
123	resnet-152_v2_pytorch	.552
124	resnet_101_v2	.552
125	resnet50_l2_3_robust	.552
126	BiT-S-R50x1	.550
127	vonegrcnn_47e	.550
128	voneresnet-50	.550
129	resnet-50_v2-tf	.550
130	inception_v2-tf	.550
131	mobilenet_v2_1.0_96	.550
132	shufflenet_v2_x1_0	.550
133	grcnn	.550
134	vonegrcnn_47e	.550
135	resnet_152_v1	.550
136	vonegrcnn_62e_nobn	.548
137	vonegrcnn_62e_nobn	.548
138	Res2Net50_26w_4s	.546
139	cvt_cvt-21-384-in1k_4_LucyV4	.546
140	resnet50_st_wzc_seed3_2_323	.546
141	resnet50-VITO-8deg-cc	.542
142	inception_v3	.542
143	regnet_y_400mf	.540
144	resnet-34	.540
145	mobilenet_v2_0.5_128	.540
146	CORnet-S	.540
147	antialiased-r50	.540
148	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.537
149	nasnet_large-tf	.535
150	mobilenet_v2_1.3_224-tf	.535
151	vonegrcnn_52e_full	.533
152	resnet50_ewconplus_wzc_seed5_2__323	.533
153	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.531
154	resnet50_ewconplus_wzc_seed6_2_323	.531
155	resnet50	.529
156	resnet50_st_wzc_seed6_2_323	.529
157	inception_v4	.527
158	mobilenet_v2_1.0_160-tf	.527
159	mobilenet_v2_0.75_96	.527
160	effnetb1_272x240	.527
161	mobilenet_v2_1.4_224-tf	.525
162	resnet50_ewconplus_wzc_seed2_2__323	.525
163	vonegrcnn_52e_full	.523
164	voneresnet-50-non_stochastic	.523
165	efficientnet_b2_imagenet_full	.521
166	pnasnet_large-tf	.521
167	resnet-18	.521
168	mobilenet_v2_1.0_192-tf	.519
169	mobilenet_v2_1.0_224-tf	.519
170	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.519
171	resnet152_imagenet_full	.517
172	convnext_base_fb_in1k_gram_aligned_dinov3	.517
173	resnet_152_v2	.515
174	resnet50_st_wzc_seed7_2_323	.515
175	mobilenet_v2_0.5_192-tf	.515
176	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.515
177	resnet50_moco_v2	.512
178	nasnet_mobile	.512
179	inception_v1	.510
180	mobilenet_v2_1.0_128-tf	.508
181	mobilenet_v1_1.0_128	.506
182	mobilenet_v2_0.75_160-tf	.506
183	mobilenet_v1_0.75_224	.504
184	nasnet_large	.504
185	efficientnet_b0_imagenet_full	.502
186	densenet-121	.502
187	mobilenet_v2_0.5_96	.502
188	mobilenet_v1_1.0_160	.502
189	densenet-121-keras	.500
190	mobilenet_v1_0.75_160	.500
191	mobilenet_v2_1_3_224	.500
192	mobilenet_v1_0.75_128	.498
193	pnasnet_large_halfPrecision	.498
194	resnet101_imagenet_full	.498
195	pnasnet_large	.498
196	resnet50_eMMCR_Vanilla	.498
197	efficientnet_b1_imagenet_full	.496
198	mobilenet_v2_0.5_160	.496
199	resnet_50_v2	.494
200	mobilenet_v2_0.75_192-tf	.492
201	vgg-16-keras	.492
202	mobilenet_v2_1_0_160	.490
203	mobilenet_v2_0.35_192	.490
204	mobilenet_v1_0.5_128	.490
205	mobilenet_v2_0.75_128	.487
206	resnet50_linf_8_robust	.485
207	mobilenet_v2_0.75_224-tf	.483
208	vgg-19-keras	.483
209	mobilenet_v1_0.5_160	.483
210	mobilenet_v1_1.0_192	.481
211	mobilenet_v1_1.0_224	.477
212	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.475
213	mobilenet_v1_0.75_192	.475
214	mobilenet_v2_0.35_128	.475
215	ReAlnet06	.475
216	ReAlnet04	.473
217	resnet34_imagenet_full	.471
218	mobilenet_v2_0.5_224-tf	.471
219	mobilenet_v1_0.5_192	.469
220	alexnet_robust_correct	.469
221	ReAlnet08	.469
222	mobilenet_v2_1-4_224_pytorch	.469
223	mobilenet_v2_1_4_224	.469
224	ReAlnet05	.469
225	convnext_femto_ols:d1_in1k	.469
226	CLIP-RN50	.467
227	mobilenet_v2_0.35_160	.467
228	mobilenet_v2_0.35_96	.463
229	ReAlnet07	.463
230	mobilenet_v2_1_0_128	.463
231	mobilenet_v2_1_0_192	.463
232	xception	.463
233	mobilenet_v1_0.5_224	.460
234	ReAlnet03	.460
235	mobilenet_v2_0_5_192	.458
236	mobilenet_v2_0_75_160	.458
237	0.5x_resnet-18_LC_w_sh_1_iter	.456
238	ReAlnet10	.456
239	resnet50_imagenet_full	.452
240	mobilenet_v2_1_0_224	.452
241	mobilenet_v2_0_75_224	.450
242	ReAlnet01	.448
243	ReAlnet09	.446
244	resnet18_imagenet_full	.444
245	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.444
246	custom_model_cv_18_dagger_408	.442
247	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.438
248	alexnet	.438
249	alexnet_ks_torevert	.438
250	alexnet_fov4	.438
251	alexnet_fov12	.438
252	alexnet_fov16	.438
253	mobilenet_v1_0.25_224	.435
254	convnext_small_imagenet_100_seed-0	.435
255	ReAlnet02	.435
256	mobilenet_v2_0_75_192	.433
257	alexnet_l2_3_robust	.425
258	mobilenet_v2_0.35_224	.423
259	vgg_16	.423
260	mobilenet_v2_0_5_224	.421
261	alexnet_linf_8_robust	.417
262	pnasnet_large_pytorch	.417
263	alexnet_random_linf8_perturb	.412
264	nasnet_mobile-tf	.408
265	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.406
266	vgg_19	.406
267	alexnet_early_checkpoint	.404
268	resnet_50_v1_spiking_l4a	.404
269	mobilenet_v1_0.25_192	.400
270	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.396
271	mobilenet_v1_0.25_128	.396
272	squeezenet1_1	.392
273	mobilenet_v1_0.25_160	.390
274	deit_small_imagenet_100_seed-0	.373
275	squeezenet1_0	.358
276	resnet50_imagenet_100_seed-0	.327
277	resnet_50_v1_spiking_l4	.219
278	convnext_small_imagenet_10_seed-0	.117
279	resnet50_imagenet_10_seed-0	.106
280	deit_small_imagenet_10_seed-0	.094
281	resnet50-simclr-vissl	.087
282	deit_small_imagenet_1_seed-0	.081
283	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.069
284	resnet_50_v1_spiking_l3	.067
285	artResNet18_1	.067
286	resnet-50_untrained	.062
287	alexnet_training_seed_09	.062
288	alexnet_training_seed_05	.062
289	alexnet_training_seed_06	.062
290	alexnet_training_seed_02	.062
291	alexnet_training_seed_04	.062
292	alexnet_training_seed_08	.062
293	alexnet_training_seed_10	.062
294	alexnet_training_seed_01	.062
295	alexnet_training_seed_01_fov4	.062
296	alexnet_training_seed_01_fov12	.062
297	alexnet_training_seed_01_fov16	.062
298	resnet50-vicregl0p75	.062
299	alexnet_training_seed_03	.062
300	convnext_small_imagenet_1_seed-0	.060
301	resnet50_imagenet_1_seed-0	.060
302	alexnet_training_seed_07	.054
303	barlow-twins-resnet50	.052
304	resnet50-barlow	.048
305	mobilevit_small	.046
306	resnet50-vicregl0p9	.029
307	ViT-B/32
308	resnet-18-LC_w_sh_100_iter_conv_init_m
309	BiT-M-R50x1
310	resnet-18-LC_w_sh_1_iter_conv_init_m
311	BiT-M-R152x2
312	resnet-18-LC_w_sh_1_iter_m
313	resnet-18-LC_w_sh_10_iter_m
314	BiT-M-R50x3
315	resnet-18-LC_w_sh_100_iter_m
316	resnet-18-LC_w_sh_10_iter_conv_init_m
317	FrankRobWobv0
318	0.5x_resnet-18_LC
319	CORnetZ_CIFAR10
320	resnet18-local_aggregation
321	r3m_resnet18
322	resnet18-deepcluster
323	CORnet-Z
324	vggface
325	my-model
326	resnet18-instance_recognition
327	0.5x_resnet-18_LC_w_sh_10_iter
328	r3m_resnet50
329	r3m_resnet34
330	resnet-18-LC_m
331	resnet18-supervised
332	r3m_resnet50_nocrop
333	resnet-18-LC_1st_conv_m
334	resnet-18-LC_conv_init_m
335	resnet18-contrastive_multiview
336	0.5x_resnet-18
337	RN50
338	0.5x_resnet-18_LC_w_sh_100_iter
339	RN50
340	resnet18-colorization
341	resnet18-relative_position
342	resnet18-autoencoder
343	resnet18-simclr
344	ViT_L_32
345	ViT_B_32
346	resnet18-depth_prediction
347	ViT_B_16
348	r3m_resnet34_nocrop
349	resnet18-contrastive_predictive
350	resnet-18-LC_untrained
351	resnet-18_untrained
352	resnet-50x2_untrained
353	dcgan
354	unet_entire
355	CORnetZ_CIFAR10_bs32_20_04
356	v1-pyr-nodown
357	pixels
358	dorinet_cornet_z
359	omnivore_swinT
360	omnivore_swinS
361	openclip
362	vonealexnet_gaussian_noise_std4_fixed
363	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
364	yudixie_resnet50_category_class_0_240908
365	yudixie_resnet50_distance_reg_0_240908
366	resnet18_imagenet21kP
367	ConvLSTM
368	PredRNN
369	yolos_tiny
370	SimVP
371	TAU
372	MIM
373	hmax
374	briaai_rmbg_1_4
375	inception_v3_pytorch
376	ReAlnet01_cornet
377	yudixie_resnet18_distance_translation_0_240719
378	yudixie_resnet18_translation_rotation_0_240719
379	ReAlnet03_cornet
380	ReAlnet02_cornet
381	yudixie_resnet18_distance_reg_0_240719
382	yudixie_resnet18_rotation_reg_0_240719
383	yudixie_resnet18_imagenet1kpret_0_240719
384	compact_resnet50_V4
385	yudixie_resnet50_distance_translation_0_240908
386	yudixie_resnet50_rotation_reg_0_240908
387	vit_base_patch14_dinov2-lvd142m
388	yudixie_resnet50_distance_rotation_0_240908
389	ReAlnet01_cornet_fov4
390	resnet18_ecoset_full
391	resnet34_ecoset_full
392	compact_resnet50_IT
393	resnet50_ecoset_full
394	vit_base_patch14_reg4_dinov2-lvd142m
395	resnet101_ecoset_full
396	ReAlnet01_cornet_fov12
397	compact_vgg19_V4
398	ReAlnet04_cornet
399	resnet152_ecoset_full
400	yudixie_resnet50_translation_reg_0_240908
401	resnet-18_test_m
402	ReAlnet05_cornet
403	yudixie_resnet18_random_0_240719
404	yudixie_resnet18_cat_obj_class_all_latents_0_240719
405	yudixie_resnet18_distance_translation_rotation_0_240719
406	SeLaVi-AVE
407	yudixie_resnet18_object_class_0_240719
408	ReAlnet06_cornet
409	yudixie_resnet18_translation_reg_0_240719
410	compact_resnet50_robust_V4
411	ReAlnet01_cornet_fov16
412	r3d_18
413	yudixie_resnet50_translation_rotation_0_240908
414	ReAlnet01_ALL
415	ReAlnet07_cornet
416	mc3_18
417	ReAlnet02_ALL
418	VOneCORnet-S
419	omnivore_swinB
420	s3d
421	ReAlnet08_cornet
422	resnet50-vicreg
423	mvit_v1_b
424	compact_cornets_V4
425	ReAlnet09_cornet
426	ReAlnet04_ALL
427	compact_cornets_IT
428	SeLaVi-Kinetics400
429	ReAlnet05_ALL
430	vit_base_patch16_dinov3-lvd1689m
431	SeLaVi-VGG-Sound
432	ReAlnet06_ALL
433	SeLaVi-Kinetics-Sound
434	compact_vgg19_IT
435	compact_resnet50_robust_IT
436	ReAlnet10_cornet
437	ReAlnet07_ALL
438	yudixie_resnet50_random_0_240908
439	r2plus1d_18
440	mvit_v2_s
441	ReAlnet08_ALL
442	yudixie_resnet18_category_class_0_240719
443	yudixie_resnet18_distance_rotation_0_240719
444	ReAlnet09_ALL
445	yudixie_resnet50_imagenet1kpret_0_240312
446	yudixie_resnet50_cat_obj_class_all_latents_0_240908
447	yudixie_resnet50_object_class_0_240908
448	resnet50_primary_visual_cortex
449	ReAlnet10_ALL
450	yudixie_resnet50_distance_translation_rotation_0_240908
451	yudixie_resnet50_imagenet1kpret_0_240908
452	ReAlnet03_ALL
453	resnet18_fair
454	pr1_stl10_disentangled_alpha04
455	predictive_coding_pc
456	yudixie_wbh_object_class_scaling_0415_run_0
457	yudixie_wbh_object_class_scaling_0415_run_3
458	yudixie_wbh_object_class_scaling_0415_imagenet
459	yudixie_wbh_object_class_scaling_0415_run_2
460	yudixie_wbh_object_class_scaling_0415_run_1
461	deepeisnn_ei_cifar10_vgg19
462	deepeisnn_ei_cifar10_resnet18
463	deepeisnn_ei_imagenet_resnet18
464	dinov2_vitl14_reg_lc_featread
465	gabor_filter_entropy_4x4
466	gabor_filter_entropy_4x4_v2
467	gabor_filter_entropy_4x4_v2_forcedCosine
468	gabor_filter_mean_abs_4x4_v2

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