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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	efficientnet-b7	.627
49	resnet-152_v1-tf	.627
50	focalnet_tiny_lrf_in1k	.627
51	AT_efficientnet-b2	.625
52	AdvProp_efficientnet-b2	.625
53	convnext_tiny_sup	.625
54	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.623
55	deit_small_imagenet_full_seed-0	.623
56	resnet50-SIN_IN	.619
57	resnext101_32x16d_wsl	.619
58	convnext_base_imagenet_full_seed-0	.619
59	fixres_resnext101_32x48d_wsl	.613
60	voneresnet-50-robust	.613
61	cvt_cvt-13-224-in1k_4_LucyV4	.610
62	convnext_small_imagenet_full_seed-0	.608
63	vit_base_patch16_clip_224:openai_ft_in1k	.608
64	resnet50_robust_l2_eps1	.608
65	efficientnet_b0	.606
66	AdvProp_efficientnet-b0	.604
67	AlexNet_SIN	.602
68	AlexNet_SIN_fov4	.602
69	AlexNet_SIN_fov12	.602
70	AlexNet_SIN_fov16	.602
71	resnet-101_v2-tf	.602
72	cvt_cvt-13-384-in1k_4_LucyV4	.596
73	resnet50_hvs_wzc	.596
74	BiT-S-R101x1	.596
75	resnet-50-robust	.596
76	imagenet_l2_3_0	.596
77	resnet50_robust_l2_eps3	.596
78	ViT_B_16_imagenet1k	.594
79	convnext_tiny_imagenet_full_seed-0	.594
80	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.590
81	tv_efficientnet-b1	.590
82	convnext_base:clip_laiona_augreg_ft_in1k_384	.590
83	resnet-50_v1-tf	.588
84	inception_v3-tf	.585
85	resnet-50-pytorch	.585
86	resnet50-sup	.585
87	cv_18_dagger_408_pretrained	.585
88	resnet50_tutorial	.585
89	resnet_50_v1	.585
90	resnet_101_v1	.583
91	blt_vs	.581
92	grcnn_109	.581
93	resnet-101_v1-tf	.579
94	BiT-S-R50x3	.577
95	BiT-S-R101x3	.575
96	resnet50_ewconplus_wzc	.575
97	ViT_B_32_imagenet1k	.573
98	cvt_cvt-21-224-in1k_4_LucyV4	.573
99	densenet-169	.573
100	densenet-169-keras	.571
101	antialias-resnet152	.571
102	inception_v4-tf	.569
103	antialiased-rnext101_32x8d	.569
104	resnet_50_v1_spiking	.567
105	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.567
106	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.565
107	xception-keras	.565
108	resnet50_st_wzc_2	.562
109	resnet50-vitoimagevidnet8	.560
110	densenet-201	.558
111	densenet_201_pytorch	.558
112	eBarlow_lmda_02_1000ep	.558
113	resnet_SIN_IN_FT_IN	.558
114	resnet50-SIN_IN_IN	.558
115	resnet-152_v2-tf	.556
116	densenet-201-keras	.556
117	imagenet_l2_10_0	.556
118	resnet50-moclr8deg	.556
119	BiT-S-R152x4	.554
120	inception_v1-tf	.554
121	resnet-152_v2_pytorch	.552
122	resnet_101_v2	.552
123	resnet50_l2_3_robust	.552
124	BiT-S-R50x1	.550
125	vonegrcnn_47e	.550
126	voneresnet-50	.550
127	resnet-50_v2-tf	.550
128	inception_v2-tf	.550
129	mobilenet_v2_1.0_96	.550
130	shufflenet_v2_x1_0	.550
131	grcnn	.550
132	vonegrcnn_47e	.550
133	resnet_152_v1	.550
134	vonegrcnn_62e_nobn	.548
135	vonegrcnn_62e_nobn	.548
136	Res2Net50_26w_4s	.546
137	cvt_cvt-21-384-in1k_4_LucyV4	.546
138	resnet50_st_wzc_seed3_2_323	.546
139	resnet50-VITO-8deg-cc	.542
140	inception_v3	.542
141	regnet_y_400mf	.540
142	resnet-34	.540
143	mobilenet_v2_0.5_128	.540
144	CORnet-S	.540
145	antialiased-r50	.540
146	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.537
147	nasnet_large-tf	.535
148	mobilenet_v2_1.3_224-tf	.535
149	vonegrcnn_52e_full	.533
150	resnet50_ewconplus_wzc_seed5_2__323	.533
151	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.531
152	resnet50_ewconplus_wzc_seed6_2_323	.531
153	resnet50	.529
154	resnet50_st_wzc_seed6_2_323	.529
155	inception_v4	.527
156	mobilenet_v2_1.0_160-tf	.527
157	mobilenet_v2_0.75_96	.527
158	effnetb1_272x240	.527
159	mobilenet_v2_1.4_224-tf	.525
160	resnet50_ewconplus_wzc_seed2_2__323	.525
161	vonegrcnn_52e_full	.523
162	voneresnet-50-non_stochastic	.523
163	efficientnet_b2_imagenet_full	.521
164	pnasnet_large-tf	.521
165	resnet-18	.521
166	mobilenet_v2_1.0_192-tf	.519
167	mobilenet_v2_1.0_224-tf	.519
168	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.519
169	resnet152_imagenet_full	.517
170	convnext_base_fb_in1k_gram_aligned_dinov3	.517
171	resnet_152_v2	.515
172	resnet50_st_wzc_seed7_2_323	.515
173	mobilenet_v2_0.5_192-tf	.515
174	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.515
175	resnet50_moco_v2	.512
176	nasnet_mobile	.512
177	inception_v1	.510
178	mobilenet_v2_1.0_128-tf	.508
179	mobilenet_v1_1.0_128	.506
180	mobilenet_v2_0.75_160-tf	.506
181	mobilenet_v1_0.75_224	.504
182	nasnet_large	.504
183	efficientnet_b0_imagenet_full	.502
184	densenet-121	.502
185	mobilenet_v2_0.5_96	.502
186	mobilenet_v1_1.0_160	.502
187	densenet-121-keras	.500
188	mobilenet_v1_0.75_160	.500
189	mobilenet_v2_1_3_224	.500
190	mobilenet_v1_0.75_128	.498
191	pnasnet_large_halfPrecision	.498
192	resnet101_imagenet_full	.498
193	pnasnet_large	.498
194	resnet50_eMMCR_Vanilla	.498
195	efficientnet_b1_imagenet_full	.496
196	mobilenet_v2_0.5_160	.496
197	resnet_50_v2	.494
198	mobilenet_v2_0.75_192-tf	.492
199	vgg-16-keras	.492
200	mobilenet_v2_1_0_160	.490
201	mobilenet_v2_0.35_192	.490
202	mobilenet_v1_0.5_128	.490
203	mobilenet_v2_0.75_128	.487
204	resnet50_linf_8_robust	.485
205	mobilenet_v2_0.75_224-tf	.483
206	vgg-19-keras	.483
207	mobilenet_v1_0.5_160	.483
208	mobilenet_v1_1.0_192	.481
209	mobilenet_v1_1.0_224	.477
210	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.475
211	mobilenet_v1_0.75_192	.475
212	mobilenet_v2_0.35_128	.475
213	ReAlnet06	.475
214	ReAlnet04	.473
215	resnet34_imagenet_full	.471
216	mobilenet_v2_0.5_224-tf	.471
217	mobilenet_v1_0.5_192	.469
218	alexnet_robust_correct	.469
219	ReAlnet08	.469
220	mobilenet_v2_1-4_224_pytorch	.469
221	mobilenet_v2_1_4_224	.469
222	ReAlnet05	.469
223	convnext_femto_ols:d1_in1k	.469
224	CLIP-RN50	.467
225	mobilenet_v2_0.35_160	.467
226	mobilenet_v2_0.35_96	.463
227	ReAlnet07	.463
228	mobilenet_v2_1_0_128	.463
229	mobilenet_v2_1_0_192	.463
230	xception	.463
231	mobilenet_v1_0.5_224	.460
232	ReAlnet03	.460
233	mobilenet_v2_0_5_192	.458
234	mobilenet_v2_0_75_160	.458
235	0.5x_resnet-18_LC_w_sh_1_iter	.456
236	ReAlnet10	.456
237	resnet50_imagenet_full	.452
238	mobilenet_v2_1_0_224	.452
239	mobilenet_v2_0_75_224	.450
240	ReAlnet01	.448
241	ReAlnet09	.446
242	resnet18_imagenet_full	.444
243	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.444
244	custom_model_cv_18_dagger_408	.442
245	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.438
246	alexnet	.438
247	alexnet_ks_torevert	.438
248	alexnet_fov4	.438
249	alexnet_fov12	.438
250	alexnet_fov16	.438
251	mobilenet_v1_0.25_224	.435
252	convnext_small_imagenet_100_seed-0	.435
253	ReAlnet02	.435
254	mobilenet_v2_0_75_192	.433
255	alexnet_l2_3_robust	.425
256	mobilenet_v2_0.35_224	.423
257	vgg_16	.423
258	mobilenet_v2_0_5_224	.421
259	alexnet_linf_8_robust	.417
260	pnasnet_large_pytorch	.417
261	alexnet_random_linf8_perturb	.412
262	nasnet_mobile-tf	.408
263	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.406
264	vgg_19	.406
265	alexnet_early_checkpoint	.404
266	resnet_50_v1_spiking_l4a	.404
267	mobilenet_v1_0.25_192	.400
268	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.396
269	mobilenet_v1_0.25_128	.396
270	squeezenet1_1	.392
271	mobilenet_v1_0.25_160	.390
272	deit_small_imagenet_100_seed-0	.373
273	squeezenet1_0	.358
274	resnet50_imagenet_100_seed-0	.327
275	resnet_50_v1_spiking_l4	.219
276	convnext_small_imagenet_10_seed-0	.117
277	resnet50_imagenet_10_seed-0	.106
278	deit_small_imagenet_10_seed-0	.094
279	resnet50-simclr-vissl	.087
280	deit_small_imagenet_1_seed-0	.081
281	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.069
282	resnet_50_v1_spiking_l3	.067
283	artResNet18_1	.067
284	resnet-50_untrained	.062
285	alexnet_training_seed_09	.062
286	alexnet_training_seed_05	.062
287	alexnet_training_seed_06	.062
288	alexnet_training_seed_02	.062
289	alexnet_training_seed_04	.062
290	alexnet_training_seed_08	.062
291	alexnet_training_seed_10	.062
292	alexnet_training_seed_01	.062
293	alexnet_training_seed_01_fov4	.062
294	alexnet_training_seed_01_fov12	.062
295	alexnet_training_seed_01_fov16	.062
296	resnet50-vicregl0p75	.062
297	alexnet_training_seed_03	.062
298	convnext_small_imagenet_1_seed-0	.060
299	resnet50_imagenet_1_seed-0	.060
300	alexnet_training_seed_07	.054
301	barlow-twins-resnet50	.052
302	resnet50-barlow	.048
303	mobilevit_small	.046
304	resnet50-vicregl0p9	.029
305	ViT-B/32
306	resnet-18-LC_w_sh_100_iter_conv_init_m
307	BiT-M-R50x1
308	resnet-18-LC_w_sh_1_iter_conv_init_m
309	BiT-M-R152x2
310	resnet-18-LC_w_sh_1_iter_m
311	resnet-18-LC_w_sh_10_iter_m
312	BiT-M-R50x3
313	resnet-18-LC_w_sh_100_iter_m
314	resnet-18-LC_w_sh_10_iter_conv_init_m
315	FrankRobWobv0
316	0.5x_resnet-18_LC
317	CORnetZ_CIFAR10
318	resnet18-local_aggregation
319	r3m_resnet18
320	resnet18-deepcluster
321	CORnet-Z
322	vggface
323	my-model
324	resnet18-instance_recognition
325	0.5x_resnet-18_LC_w_sh_10_iter
326	r3m_resnet50
327	r3m_resnet34
328	resnet-18-LC_m
329	resnet18-supervised
330	r3m_resnet50_nocrop
331	resnet-18-LC_1st_conv_m
332	resnet-18-LC_conv_init_m
333	resnet18-contrastive_multiview
334	0.5x_resnet-18
335	RN50
336	0.5x_resnet-18_LC_w_sh_100_iter
337	RN50
338	resnet18-colorization
339	resnet18-relative_position
340	resnet18-autoencoder
341	resnet18-simclr
342	ViT_L_32
343	ViT_B_32
344	resnet18-depth_prediction
345	ViT_B_16
346	r3m_resnet34_nocrop
347	resnet18-contrastive_predictive
348	resnet-18-LC_untrained
349	resnet-18_untrained
350	resnet-50x2_untrained
351	dcgan
352	unet_entire
353	CORnetZ_CIFAR10_bs32_20_04
354	v1-pyr-nodown
355	pixels
356	dorinet_cornet_z
357	omnivore_swinT
358	omnivore_swinS
359	openclip
360	vonealexnet_gaussian_noise_std4_fixed
361	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
362	yudixie_resnet50_category_class_0_240908
363	yudixie_resnet50_distance_reg_0_240908
364	resnet18_imagenet21kP
365	ConvLSTM
366	PredRNN
367	yolos_tiny
368	SimVP
369	TAU
370	MIM
371	hmax
372	briaai_rmbg_1_4
373	inception_v3_pytorch
374	ReAlnet01_cornet
375	yudixie_resnet18_distance_translation_0_240719
376	yudixie_resnet18_translation_rotation_0_240719
377	ReAlnet03_cornet
378	ReAlnet02_cornet
379	yudixie_resnet18_distance_reg_0_240719
380	yudixie_resnet18_rotation_reg_0_240719
381	yudixie_resnet18_imagenet1kpret_0_240719
382	compact_resnet50_V4
383	yudixie_resnet50_distance_translation_0_240908
384	yudixie_resnet50_rotation_reg_0_240908
385	vit_base_patch14_dinov2-lvd142m
386	yudixie_resnet50_distance_rotation_0_240908
387	ReAlnet01_cornet_fov4
388	resnet18_ecoset_full
389	resnet34_ecoset_full
390	compact_resnet50_IT
391	resnet50_ecoset_full
392	vit_base_patch14_reg4_dinov2-lvd142m
393	resnet101_ecoset_full
394	ReAlnet01_cornet_fov12
395	compact_vgg19_V4
396	ReAlnet04_cornet
397	resnet152_ecoset_full
398	yudixie_resnet50_translation_reg_0_240908
399	resnet-18_test_m
400	ReAlnet05_cornet
401	yudixie_resnet18_random_0_240719
402	aughost_fair_resnet18
403	yudixie_resnet18_cat_obj_class_all_latents_0_240719
404	yudixie_resnet18_distance_translation_rotation_0_240719
405	SeLaVi-AVE
406	yudixie_resnet18_object_class_0_240719
407	ReAlnet06_cornet
408	yudixie_resnet18_translation_reg_0_240719
409	compact_resnet50_robust_V4
410	ReAlnet01_cornet_fov16
411	r3d_18
412	yudixie_resnet50_translation_rotation_0_240908
413	ReAlnet01_ALL
414	ReAlnet07_cornet
415	mc3_18
416	ReAlnet02_ALL
417	VOneCORnet-S
418	omnivore_swinB
419	s3d
420	ReAlnet08_cornet
421	resnet50-vicreg
422	mvit_v1_b
423	compact_cornets_V4
424	ReAlnet09_cornet
425	ReAlnet04_ALL
426	compact_cornets_IT
427	SeLaVi-Kinetics400
428	ReAlnet05_ALL
429	vit_base_patch16_dinov3-lvd1689m
430	SeLaVi-VGG-Sound
431	ReAlnet06_ALL
432	SeLaVi-Kinetics-Sound
433	compact_vgg19_IT
434	compact_resnet50_robust_IT
435	ReAlnet10_cornet
436	ReAlnet07_ALL
437	yudixie_resnet50_random_0_240908
438	r2plus1d_18
439	mvit_v2_s
440	ReAlnet08_ALL
441	yudixie_resnet18_category_class_0_240719
442	yudixie_resnet18_distance_rotation_0_240719
443	ReAlnet09_ALL
444	yudixie_resnet50_imagenet1kpret_0_240312
445	yudixie_resnet50_cat_obj_class_all_latents_0_240908
446	yudixie_resnet50_object_class_0_240908
447	resnet50_primary_visual_cortex
448	ReAlnet10_ALL
449	yudixie_resnet50_distance_translation_rotation_0_240908
450	yudixie_resnet50_imagenet1kpret_0_240908
451	ReAlnet03_ALL
452	resnet18_fair
453	pr1_stl10_disentangled_alpha04
454	predictive_coding_pc
455	yudixie_wbh_object_class_scaling_0415_run_0
456	yudixie_wbh_object_class_scaling_0415_run_3
457	yudixie_wbh_object_class_scaling_0415_imagenet
458	yudixie_wbh_object_class_scaling_0415_run_2
459	yudixie_wbh_object_class_scaling_0415_run_1
460	deepeisnn_ei_cifar10_vgg19
461	deepeisnn_ei_cifar10_resnet18
462	deepeisnn_ei_imagenet_resnet18
463	dinov2_vitl14_reg_lc_featread

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