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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021colour-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:laion2b_ft_in1k	.998
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.998
3	vit_large_patch14_clip_336:laion2b_ft_in1k	.998
4	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.998
5	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.998
6	resnext101_32x32d_wsl	.997
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.997
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.997
9	convnext_base:clip_laiona_augreg_ft_in1k_384	.997
10	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.997
11	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.995
12	vit_large_patch14_clip_224:openai_ft_in1k	.995
13	convnext_xlarge:fb_in22k_ft_in1k	.995
14	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.995
15	convnext_large_imagenet_full_seed-0	.995
16	fixres_resnext101_32x48d_wsl	.994
17	resnext101_32x48d_wsl	.994
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.994
19	vit_base_patch16_clip_224:openai_ft_in1k	.994
20	vit_relpos_base_patch16_clsgap_224:sw_in1k	.994
21	convnext_large:fb_in22k_ft_in1k	.994
22	AdvProp_efficientnet-b7	.992
23	deit_large_imagenet_full_seed-0	.992
24	deit_base_imagenet_full_seed-0	.992
25	nasnet_large-tf	.991
26	effnetv2m_custom384	.991
27	resnext101_32x16d_wsl	.991
28	focalnet_tiny_lrf_in1k	.991
29	convnext_base_imagenet_full_seed-0	.991
30	convnext_tiny:in12k_ft_in1k	.991
31	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.991
32	convnext_tiny_imagenet_full_seed-0	.991
33	nasnet_large	.991
34	pnasnet_large-tf	.989
35	resnext101_32x8d_wsl	.989
36	AdvProp_efficientnet-b6	.989
37	convnext_tiny_sup	.989
38	antialiased-rnext101_32x8d	.989
39	convnext_small_imagenet_full_seed-0	.989
40	AT_efficientnet-b2	.988
41	AdvProp_efficientnet-b2	.988
42	cvt_cvt-13-224-in1k_4_LucyV4	.988
43	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.988
44	inception_v3-tf	.986
45	inception_v4-tf	.986
46	cvt_cvt-21-224-in1k_4_LucyV4	.986
47	pnasnet_large	.986
48	BiT-S-R152x4	.984
49	AdvProp_efficientnet-b0	.984
50	antialias-resnet152	.984
51	BiT-S-R50x3	.983
52	BiT-S-R101x3	.983
53	cv_18_dagger_408_pretrained	.983
54	cvt_cvt-13-384-in1k_4_LucyV4	.983
55	resnet-152_v2_pytorch	.983
56	cvt_cvt-21-384-in1k_4_LucyV4	.983
57	AdvProp_efficientnet-b4	.981
58	resnet-152_v2-tf	.981
59	AT_efficientnet-b4	.981
60	ViT_B_16_imagenet1k	.981
61	antialiased-r50	.981
62	deit_small_imagenet_full_seed-0	.981
63	inception_v4	.981
64	resnet_101_v2	.981
65	resnet-101_v2-tf	.980
66	resnet_152_v1	.980
67	resnet50-vitoimagevidnet8	.980
68	resnet_SIN_IN_FT_IN	.978
69	BiT-S-R101x1	.978
70	resnet50-SIN_IN_IN	.978
71	densenet-201-keras	.978
72	inception_v2-tf	.978
73	Res2Net50_26w_4s	.978
74	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.978
75	densenet-201	.978
76	densenet_201_pytorch	.978
77	resnet152_imagenet_full	.978
78	grcnn	.978
79	efficientnet-b7	.977
80	resnet50-SIN_IN	.977
81	resnet-50_v1-tf	.977
82	effnetb1_272x240	.977
83	resnet50-moclr8deg	.977
84	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.977
85	resnet_101_v1	.977
86	BiT-S-R152x2	.975
87	BiT-S-R50x1	.975
88	efficientnet_b0	.975
89	resnet-101_v1-tf	.975
90	densenet-169-keras	.975
91	xception-keras	.975
92	resnet50_moco_v2	.975
93	resnet101_imagenet_full	.975
94	resnet_152_v2	.975
95	densenet-169	.975
96	ViT_L_32_imagenet1k	.973
97	resnet-152_v1-tf	.973
98	inception_v3	.973
99	voneresnet-50-non_stochastic	.972
100	resnet-50_v2-tf	.972
101	CORnet-S	.972
102	resnet50_imagenet_full	.972
103	tv_efficientnet-b1	.972
104	grcnn_109	.972
105	convnext_femto_ols:d1_in1k	.972
106	vonegrcnn_47e	.970
107	densenet-121-keras	.969
108	vonegrcnn_62e_nobn	.969
109	resnet-50-pytorch	.969
110	mobilenet_v2_1.4_224-tf	.969
111	mobilenet_v2_1.3_224-tf	.969
112	vonegrcnn_47e	.969
113	vgg-16-keras	.969
114	vgg-19-keras	.969
115	resnet50-sup	.969
116	resnet50_tutorial	.969
117	resnet_50_v1	.969
118	densenet-121	.969
119	mobilenet_v2_1.0_192-tf	.967
120	efficientnet_b2_imagenet_full	.967
121	mobilenet_v2_1.0_224-tf	.966
122	voneresnet-50	.966
123	vgg_19	.966
124	resnet50-VITO-8deg-cc	.966
125	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.966
126	resnet_50_v2	.966
127	mobilenet_v2_1.0_160-tf	.964
128	resnet50	.964
129	mobilenet_v2_1_3_224	.964
130	efficientnet_b1_imagenet_full	.963
131	nasnet_mobile	.963
132	vonegrcnn_52e_full	.963
133	resnet-34	.961
134	regnet_y_400mf	.961
135	vgg_16	.961
136	vonegrcnn_62e_nobn	.961
137	vonegrcnn_52e_full	.959
138	ViT_B_32_imagenet1k	.959
139	resnet50_robust_l2_eps1	.959
140	xception	.959
141	mobilenet_v1_1.0_224	.958
142	custom_model_cv_18_dagger_408	.956
143	resnet_50_v1_spiking	.956
144	inception_v1-tf	.955
145	resnet-18	.955
146	resnet34_imagenet_full	.955
147	resnet18_imagenet_full	.955
148	mobilenet_v1_0.75_224	.952
149	mobilenet_v1_1.0_192	.952
150	mobilenet_v1_1.0_160	.952
151	blt_vs	.952
152	ReAlnet06	.948
153	resnet50_eMMCR_Vanilla	.948
154	efficientnet_b0_imagenet_full	.947
155	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.945
156	AdvProp_efficientnet-b8	.944
157	mobilenet_v1_0.75_192	.944
158	ReAlnet09	.944
159	ReAlnet03	.944
160	mobilenet_v2_0.75_192-tf	.941
161	mobilenet_v2_1.0_128-tf	.939
162	mobilenet_v2_0.75_224-tf	.939
163	mobilenet_v1_0.5_224	.939
164	voneresnet-50-robust	.939
165	mobilenet_v2_1_0_192	.939
166	ReAlnet02	.939
167	mobilenet_v1_0.75_160	.938
168	ReAlnet07	.938
169	shufflenet_v2_x1_0	.936
170	mobilenet_v2_1_0_224	.936
171	mobilenet_v2_0.75_160-tf	.934
172	ReAlnet01	.934
173	nasnet_mobile-tf	.931
174	ReAlnet04	.931
175	mobilenet_v2_1_0_160	.931
176	inception_v1	.930
177	ReAlnet05	.930
178	ReAlnet08	.927
179	ReAlnet10	.925
180	mobilenet_v2_0.5_224-tf	.923
181	mobilenet_v1_1.0_128	.923
182	mobilenet_v2_0_75_224	.923
183	mobilenet_v2_0_75_192	.923
184	resnet50-SIN	.922
185	mobilenet_v2_0_75_160	.920
186	mobilenet_v2_0.75_128	.919
187	resnet_50_v1_spiking_l4a	.917
188	mobilenet_v2_0.5_192-tf	.912
189	0.5x_resnet-18_LC_w_sh_1_iter	.912
190	mobilenet_v1_0.75_128	.911
191	mobilenet_v2_1_0_128	.911
192	mobilenet_v1_0.5_192	.909
193	CLIP-RN50	.908
194	mobilenet_v2_1.0_96	.906
195	mobilenet_v2_0.5_160	.905
196	mobilenet_v1_0.5_160	.903
197	mobilenet_v2_0_5_224	.903
198	mobilenet_v2_0.75_96	.902
199	resnet-50-robust	.902
200	imagenet_l2_3_0	.902
201	resnet50_robust_l2_eps3	.902
202	mobilenet_v2_1-4_224_pytorch	.902
203	mobilenet_v2_1_4_224	.902
204	mobilenet_v2_0.35_192	.900
205	mobilenet_v2_0_5_192	.900
206	resnet50_l2_3_robust	.889
207	mobilenet_v2_0.35_224	.884
208	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.884
209	mobilenet_v2_0.5_128	.884
210	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.883
211	mobilenet_v2_0.35_160	.872
212	squeezenet1_1	.870
213	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
214	alexnet-baseline	.864
215	alexnet	.864
216	alexnet	.864
217	alexnet	.864
218	alexnet	.864
219	alexnet	.864
220	alexnet	.864
221	alexnet_random_linf8_perturb	.864
222	alexnet	.864
223	alexnet_ks_torevert	.864
224	mobilenet_v1_0.5_128	.863
225	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.859
226	convnext_small_imagenet_100_seed-0	.859
227	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.850
228	alexnet	.847
229	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.844
230	squeezenet1_0	.842
231	mobilenet_v1_0.25_224	.833
232	alexnet_early_checkpoint	.830
233	mobilenet_v2_0.5_96	.823
234	mobilenet_v1_0.25_192	.819
235	mobilenet_v2_0.35_128	.819
236	resnet50_linf_8_robust	.812
237	mobilenet_v1_0.25_160	.808
238	mobilenet_v2_0.35_96	.797
239	AlexNet_SIN	.797
240	imagenet_l2_10_0	.781
241	mobilenet_v1_0.25_128	.777
242	alexnet_l2_3_robust	.767
243	alexnet_robust_correct	.756
244	deit_small_imagenet_100_seed-0	.744
245	resnet50_imagenet_100_seed-0	.731
246	alexnet_linf_8_robust	.625
247	resnet_50_v1_spiking_l4	.584
248	resnet50_imagenet_10_seed-0	.200
249	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.170
250	convnext_small_imagenet_10_seed-0	.127
251	deit_small_imagenet_10_seed-0	.114
252	deit_small_imagenet_1_seed-0	.089
253	resnet50-barlow	.080
254	resnet50-vicregl0p75	.077
255	alexnet_training_seed_06	.075
256	alexnet_training_seed_09	.067
257	resnet-50_untrained	.062
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	convnext_small_imagenet_1_seed-0	.062
264	alexnet_training_seed_10	.062
265	alexnet_training_seed_01	.062
266	resnet_50_v1_spiking_l3	.062
267	alexnet_training_seed_03	.062
268	resnet50-simclr-vissl	.061
269	resnet50_imagenet_1_seed-0	.061
270	barlow-twins-resnet50	.058
271	resnet50-vicregl0p9	.058
272	resnet50-meshes-lt-100-original-pretrained	.056
273	alexnet_training_seed_07	.056
274	mobilevit_small	.045
275	artResNet18_1	.033
276	ViT-B/32
277	resnet-18-LC_w_sh_100_iter_conv_init_m
278	BiT-M-R50x1
279	resnet-18-LC_w_sh_1_iter_conv_init_m
280	resnet-18-LC_w_sh_1_iter_m
281	BiT-M-R152x2
282	resnet-18-LC_w_sh_10_iter_m
283	BiT-M-R50x3
284	resnet-18-LC_w_sh_100_iter_m
285	resnet-18-LC_w_sh_10_iter_conv_init_m
286	FrankRobWobv0
287	0.5x_resnet-18_LC
288	CORnetZ_CIFAR10
289	resnet18-local_aggregation
290	r3m_resnet18
291	CORnet-Z
292	resnet18-deepcluster
293	vggface
294	my-model
295	resnet18-instance_recognition
296	0.5x_resnet-18_LC_w_sh_10_iter
297	r3m_resnet50
298	r3m_resnet34
299	resnet-18-LC_m
300	r3m_resnet50_nocrop
301	resnet18-supervised
302	resnet-18-LC_1st_conv_m
303	resnet-18-LC_conv_init_m
304	resnet18-contrastive_multiview
305	0.5x_resnet-18
306	RN50
307	0.5x_resnet-18_LC_w_sh_100_iter
308	RN50
309	resnet18-colorization
310	resnet18-relative_position
311	resnet18-autoencoder
312	resnet18-simclr
313	ViT_L_32
314	ViT_B_32
315	resnet18-depth_prediction
316	ViT_B_16
317	r3m_resnet34_nocrop
318	resnet18-contrastive_predictive
319	resnet-18-LC_untrained
320	resnet-18_untrained
321	resnet-50x2_untrained
322	dcgan
323	unet_entire
324	CORnetZ_CIFAR10_bs32_20_04
325	v1-pyr-nodown
326	pixels
327	dorinet_cornet_z
328	omnivore_swinT
329	resnet50
330	openclip
331	vonealexnet_gaussian_noise_std4_fixed
332	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
333	yudixie_resnet50_category_class_0_240908
334	yudixie_resnet50_distance_reg_0_240908
335	ConvLSTM
336	PredRNN
337	SimVP
338	TAU
339	MIM
340	yolos_tiny
341	pnasnet_large_pytorch
342	inception_v3_pytorch
343	yudixie_resnet18_distance_translation_0_240719
344	briaai_rmbg_1_4
345	ReAlnet01_cornet
346	yudixie_resnet18_imagenet1kpret_0_240719
347	yudixie_resnet50_distance_rotation_0_240908
348	yudixie_resnet50_rotation_reg_0_240908
349	yudixie_resnet50_translation_reg_0_240908
350	yudixie_resnet50_distance_translation_0_240908
351	ReAlnet02_cornet
352	resnet34_ecoset_full
353	resnet50_ecoset_full
354	resnet101_ecoset_full
355	yudixie_resnet50_translation_rotation_0_240908
356	ReAlnet04_cornet
357	resnet152_ecoset_full
358	ReAlnet03_cornet
359	resnet-18_test_m
360	yudixie_resnet18_category_class_0_240719
361	ReAlnet05_cornet
362	yudixie_resnet18_random_0_240719
363	yudixie_resnet18_cat_obj_class_all_latents_0_240719
364	yudixie_resnet18_distance_translation_rotation_0_240719
365	SeLaVi-AVE
366	yudixie_resnet18_object_class_0_240719
367	yudixie_resnet18_rotation_reg_0_240719
368	ReAlnet06_cornet
369	yudixie_resnet18_translation_reg_0_240719
370	yudixie_resnet18_distance_reg_0_240719
371	hmax
372	r3d_18
373	ReAlnet01_ALL
374	ReAlnet07_cornet
375	omnivore_swinB
376	mc3_18
377	ReAlnet02_ALL
378	s3d
379	VOneCORnet-S
380	resnet50-vicreg
381	ReAlnet08_cornet
382	mvit_v1_b
383	ReAlnet03_ALL
384	resnet18_imagenet21kP
385	ReAlnet04_ALL
386	omnivore_swinS
387	SeLaVi-Kinetics400
388	ReAlnet05_ALL
389	SeLaVi-VGG-Sound
390	SeLaVi-Kinetics-Sound
391	ReAlnet06_ALL
392	yudixie_resnet50_random_0_240908
393	ReAlnet10_cornet
394	r2plus1d_18
395	ReAlnet07_ALL
396	yudixie_resnet50_distance_translation_rotation_0_240908
397	mvit_v2_s
398	yudixie_resnet50_imagenet1kpret_0_240908
399	resnet18_ecoset_full
400	ReAlnet08_ALL
401	yudixie_resnet18_distance_rotation_0_240719
402	ReAlnet09_ALL
403	yudixie_resnet18_translation_rotation_0_240719
404	yudixie_resnet50_imagenet1kpret_0_240312
405	yudixie_resnet50_object_class_0_240908
406	yudixie_resnet50_cat_obj_class_all_latents_0_240908
407	resnet50_primary_visual_cortex
408	ReAlnet09_cornet
409	ReAlnet10_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: Geirhos2021colour

Metric: top1