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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021phasescrambling-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.895
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.894
3	vit_large_patch14_clip_224:openai_ft_in1k	.892
4	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.883
5	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.880
6	deit_large_imagenet_full_seed-0	.880
7	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.875
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.873
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.872
10	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.856
11	deit_base_imagenet_full_seed-0	.850
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.844
13	vit_large_patch14_clip_336:laion2b_ft_in1k	.836
14	convnext_large:fb_in22k_ft_in1k	.836
15	convnext_xlarge:fb_in22k_ft_in1k	.833
16	focalnet_tiny_lrf_in1k	.822
17	convnext_base_imagenet_full_seed-0	.822
18	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.819
19	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.817
20	convnext_large_imagenet_full_seed-0	.817
21	convnext_small_imagenet_full_seed-0	.809
22	convnext_base:clip_laiona_augreg_ft_in1k_384	.805
23	vit_relpos_base_patch16_clsgap_224:sw_in1k	.803
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.800
25	cv_18_dagger_408_pretrained	.791
26	convnext_tiny_imagenet_full_seed-0	.789
27	convnext_tiny:in12k_ft_in1k	.786
28	effnetv2m_custom384	.784
29	vit_base_patch16_clip_224:openai_ft_in1k	.781
30	deit_small_imagenet_full_seed-0	.778
31	convnext_tiny_sup	.770
32	resnext101_32x32d_wsl	.769
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.767
34	fixres_resnext101_32x48d_wsl	.764
35	cvt_cvt-21-224-in1k_4_LucyV4	.761
36	cvt_cvt-21-384-in1k_4_LucyV4	.761
37	resnext101_32x16d_wsl	.753
38	resnext101_32x48d_wsl	.752
39	cvt_cvt-13-224-in1k_4_LucyV4	.750
40	resnext101_32x8d_wsl	.738
41	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.727
42	AdvProp_efficientnet-b7	.723
43	cvt_cvt-13-384-in1k_4_LucyV4	.717
44	convnext_femto_ols:d1_in1k	.713
45	resnet-152_v2_pytorch	.692
46	densenet-201-keras	.691
47	resnet50-vitoimagevidnet8	.691
48	densenet-201	.689
49	densenet_201_pytorch	.689
50	resnet_101_v2	.689
51	tv_efficientnet-b1	.684
52	resnet-101_v2-tf	.680
53	nasnet_large-tf	.678
54	AdvProp_efficientnet-b4	.678
55	AT_efficientnet-b4	.678
56	effnetb1_272x240	.675
57	nasnet_large	.672
58	AdvProp_efficientnet-b6	.670
59	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.666
60	resnet50-VITO-8deg-cc	.666
61	resnet50-SIN_IN	.664
62	BiT-S-R152x4	.661
63	BiT-S-R101x3	.661
64	resnet50-SIN	.661
65	grcnn_109	.661
66	AT_efficientnet-b2	.659
67	AdvProp_efficientnet-b2	.659
68	densenet-169	.658
69	voneresnet-50-robust	.656
70	resnet_101_v1	.656
71	resnet_152_v1	.656
72	BiT-S-R50x3	.655
73	inception_v1-tf	.653
74	densenet-169-keras	.653
75	efficientnet-b7	.653
76	resnet_50_v2	.653
77	Res2Net50_26w_4s	.652
78	BiT-S-R101x1	.650
79	inception_v2-tf	.650
80	BiT-S-R50x1	.648
81	AdvProp_efficientnet-b0	.648
82	antialias-resnet152	.648
83	densenet-121-keras	.645
84	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.645
85	pnasnet_large-tf	.644
86	resnet50_robust_l2_eps1	.644
87	densenet-121	.644
88	resnet-152_v2-tf	.641
89	inception_v4-tf	.639
90	pnasnet_large	.639
91	ViT_L_32_imagenet1k	.637
92	resnet_SIN_IN_FT_IN	.636
93	BiT-S-R152x2	.636
94	resnet50-SIN_IN_IN	.636
95	resnet-152_v1-tf	.633
96	antialiased-rnext101_32x8d	.633
97	grcnn	.633
98	resnet_152_v2	.633
99	xception-keras	.630
100	inception_v4	.628
101	resnet-50_v2-tf	.627
102	CLIP-RN50	.627
103	inception_v3_pytorch	.627
104	resnet-34	.625
105	inception_v3	.623
106	resnet101_imagenet_full	.622
107	resnet152_imagenet_full	.622
108	CORnet-S	.620
109	resnet-50-pytorch	.620
110	resnet50-sup	.620
111	resnet50_tutorial	.620
112	resnet_50_v1	.620
113	inception_v3-tf	.616
114	resnet-50_v1-tf	.614
115	antialiased-r50	.614
116	resnet50	.613
117	mobilenet_v2_1_3_224	.613
118	efficientnet_b2_imagenet_full	.609
119	mobilenet_v2_1.4_224-tf	.608
120	resnet-50-robust	.606
121	imagenet_l2_3_0	.606
122	resnet50_robust_l2_eps3	.606
123	resnet50_imagenet_full	.606
124	resnet-101_v1-tf	.605
125	vgg-19-keras	.602
126	vonegrcnn_47e	.598
127	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.597
128	mobilenet_v2_1.3_224-tf	.597
129	resnet_50_v1_spiking	.597
130	inception_v1	.597
131	efficientnet_b0	.595
132	voneresnet-50-non_stochastic	.595
133	custom_model_cv_18_dagger_408	.595
134	blt_vs	.595
135	efficientnet_b1_imagenet_full	.595
136	ViT_B_32_imagenet1k	.594
137	mobilenet_v2_1_0_192	.594
138	AlexNet_SIN	.594
139	resnet50_l2_3_robust	.592
140	shufflenet_v2_x1_0	.592
141	vonegrcnn_47e	.589
142	resnet50-moclr8deg	.589
143	mobilenet_v2_1.0_224-tf	.588
144	mobilenet_v2_1_0_160	.588
145	mobilenet_v2_1.0_192-tf	.584
146	voneresnet-50	.584
147	vgg_16	.584
148	vonegrcnn_62e_nobn	.583
149	vonegrcnn_62e_nobn	.583
150	vgg-16-keras	.580
151	resnet34_imagenet_full	.580
152	mobilenet_v2_0_75_224	.578
153	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.577
154	ReAlnet09	.577
155	ReAlnet10	.577
156	vonegrcnn_52e_full	.577
157	mobilenet_v2_0.75_192-tf	.575
158	vonegrcnn_52e_full	.573
159	resnet-18	.573
160	vgg_19	.573
161	ReAlnet06	.573
162	resnet50_moco_v2	.572
163	ReAlnet05	.572
164	mobilenet_v2_1.0_160-tf	.569
165	regnet_y_400mf	.567
166	ReAlnet03	.567
167	ReAlnet04	.567
168	nasnet_mobile	.566
169	resnet50_linf_8_robust	.564
170	mobilenet_v2_0_75_192	.564
171	mobilenet_v2_1_0_224	.562
172	ReAlnet01	.562
173	AdvProp_efficientnet-b8	.559
174	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.558
175	alexnet	.558
176	mobilenet_v2_0.75_224-tf	.556
177	efficientnet_b0_imagenet_full	.553
178	ReAlnet07	.553
179	ReAlnet08	.550
180	ReAlnet02	.550
181	resnet18_imagenet_full	.548
182	mobilenet_v1_0.75_224	.545
183	mobilenet_v1_1.0_224	.545
184	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.544
185	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.541
186	resnet50_eMMCR_Vanilla	.539
187	xception	.536
188	squeezenet1_1	.533
189	mobilenet_v1_1.0_192	.530
190	alexnet_random_linf8_perturb	.530
191	mobilenet_v2_0.75_160-tf	.525
192	alexnet_early_checkpoint	.523
193	imagenet_l2_10_0	.523
194	mobilenet_v2_1-4_224_pytorch	.522
195	mobilenet_v2_1_4_224	.522
196	alexnet-baseline	.520
197	alexnet	.520
198	alexnet	.520
199	alexnet	.520
200	alexnet	.520
201	alexnet	.520
202	alexnet	.520
203	alexnet	.520
204	alexnet_ks_torevert	.520
205	mobilenet_v2_1_0_128	.519
206	mobilenet_v2_0.75_128	.517
207	mobilenet_v2_0_5_192	.517
208	mobilenet_v1_1.0_160	.516
209	mobilenet_v2_0.5_192-tf	.511
210	mobilenet_v2_1.0_128-tf	.511
211	mobilenet_v1_0.75_192	.511
212	mobilenet_v2_0_75_160	.511
213	mobilenet_v2_0_5_224	.506
214	resnet_50_v1_spiking_l4a	.505
215	mobilenet_v2_0.5_224-tf	.503
216	mobilenet_v2_0.5_160	.503
217	mobilenet_v2_1.0_96	.498
218	convnext_small_imagenet_100_seed-0	.498
219	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.495
220	alexnet_l2_3_robust	.494
221	mobilenet_v1_1.0_128	.492
222	mobilenet_v2_0.5_128	.491
223	mobilenet_v1_0.75_160	.489
224	mobilenet_v1_0.5_224	.484
225	mobilenet_v2_0.35_224	.483
226	pnasnet_large_pytorch	.481
227	nasnet_mobile-tf	.470
228	alexnet_robust_correct	.470
229	mobilenet_v2_0.75_96	.463
230	mobilenet_v1_0.5_128	.463
231	mobilenet_v1_0.5_192	.461
232	mobilenet_v2_0.35_192	.461
233	squeezenet1_0	.458
234	mobilenet_v1_0.5_160	.458
235	mobilenet_v1_0.75_128	.445
236	mobilenet_v2_0.35_160	.445
237	alexnet_linf_8_robust	.436
238	mobilenet_v1_0.25_160	.430
239	mobilenet_v2_0.5_96	.427
240	mobilenet_v1_0.25_224	.420
241	mobilenet_v2_0.35_96	.417
242	mobilenet_v2_0.35_128	.412
243	0.5x_resnet-18_LC_w_sh_1_iter	.406
244	mobilenet_v1_0.25_128	.398
245	mobilenet_v1_0.25_192	.378
246	deit_small_imagenet_100_seed-0	.372
247	resnet50_imagenet_100_seed-0	.323
248	resnet_50_v1_spiking_l4	.286
249	convnext_small_imagenet_10_seed-0	.109
250	deit_small_imagenet_10_seed-0	.105
251	resnet50_imagenet_10_seed-0	.098
252	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.097
253	deit_small_imagenet_1_seed-0	.086
254	resnet50_imagenet_1_seed-0	.083
255	artResNet18_1	.080
256	resnet50-barlow	.073
257	alexnet_training_seed_06	.069
258	resnet_50_v1_spiking_l3	.067
259	resnet50-meshes-lt-100-original-scratch	.064
260	convnext_small_imagenet_1_seed-0	.064
261	barlow-twins-resnet50	.062
262	resnet50-simclr-vissl	.062
263	resnet-50_untrained	.062
264	alexnet_training_seed_05	.062
265	alexnet_training_seed_02	.062
266	alexnet_training_seed_04	.062
267	alexnet_training_seed_10	.062
268	alexnet_training_seed_08	.062
269	alexnet_training_seed_01	.062
270	alexnet_training_seed_03	.062
271	resnet50-meshes-lt-100-original-pretrained	.061
272	alexnet_training_seed_09	.061
273	alexnet_training_seed_07	.059
274	resnet50-vicregl0p9	.059
275	resnet50-vicregl0p75	.041
276	mobilevit_small	.037
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	resnet-18-LC_m
300	r3m_resnet34
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
318	r3m_resnet34_nocrop
319	resnet18-contrastive_predictive
320	resnet-18-LC_untrained
321	resnet-18_untrained
322	resnet-50x2_untrained
323	dcgan
324	unet_entire
325	CORnetZ_CIFAR10_bs32_20_04
326	v1-pyr-nodown
327	pixels
328	dorinet_cornet_z
329	omnivore_swinT
330	resnet50
331	openclip
332	vonealexnet_gaussian_noise_std4_fixed
333	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
334	yudixie_resnet50_object_class_0_240908
335	yudixie_resnet50_imagenet1kpret_0_240312
336	yudixie_resnet50_category_class_0_240908
337	resnet18_imagenet21kP
338	yudixie_resnet50_distance_reg_0_240908
339	ConvLSTM
340	PredRNN
341	SimVP
342	briaai_rmbg_1_4
343	TAU
344	yolos_tiny
345	MIM
346	hmax
347	ReAlnet01_cornet
348	yudixie_resnet18_distance_translation_0_240719
349	ReAlnet03_cornet
350	yudixie_resnet18_translation_reg_0_240719
351	yudixie_resnet18_rotation_reg_0_240719
352	yudixie_resnet18_imagenet1kpret_0_240719
353	ReAlnet02_cornet
354	yudixie_resnet50_distance_translation_0_240908
355	yudixie_resnet50_rotation_reg_0_240908
356	yudixie_resnet50_distance_rotation_0_240908
357	resnet18_ecoset_full
358	VOneCORnet-S
359	resnet34_ecoset_full
360	resnet50_ecoset_full
361	resnet101_ecoset_full
362	resnet152_ecoset_full
363	ReAlnet04_cornet
364	yudixie_resnet18_translation_rotation_0_240719
365	yudixie_resnet18_distance_rotation_0_240719
366	resnet-18_test_m
367	yudixie_resnet18_category_class_0_240719
368	yudixie_resnet18_random_0_240719
369	yudixie_resnet18_cat_obj_class_all_latents_0_240719
370	yudixie_resnet50_translation_reg_0_240908
371	ReAlnet05_cornet
372	yudixie_resnet18_distance_translation_rotation_0_240719
373	yudixie_resnet18_object_class_0_240719
374	ReAlnet06_cornet
375	SeLaVi-AVE
376	r3d_18
377	ReAlnet04_ALL
378	yudixie_resnet50_translation_rotation_0_240908
379	yudixie_resnet18_distance_reg_0_240719
380	ReAlnet07_cornet
381	mc3_18
382	ReAlnet02_ALL
383	s3d
384	omnivore_swinB
385	ReAlnet08_cornet
386	mvit_v1_b
387	resnet50-vicreg
388	ReAlnet01_ALL
389	ReAlnet03_ALL
390	ReAlnet09_cornet
391	ReAlnet05_ALL
392	omnivore_swinS
393	SeLaVi-Kinetics400
394	SeLaVi-VGG-Sound
395	ReAlnet06_ALL
396	SeLaVi-Kinetics-Sound
397	ReAlnet10_cornet
398	r2plus1d_18
399	yudixie_resnet50_random_0_240908
400	ReAlnet07_ALL
401	yudixie_resnet50_distance_translation_rotation_0_240908
402	mvit_v2_s
403	ReAlnet08_ALL
404	yudixie_resnet50_imagenet1kpret_0_240908
405	ReAlnet09_ALL
406	yudixie_resnet50_cat_obj_class_all_latents_0_240908
407	ReAlnet10_ALL
408	resnet50_primary_visual_cortex

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: Geirhos2021phasescrambling

Metric: top1