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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021falsecolour-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.998
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.996
3	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.996
4	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.996
5	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.996
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.996
7	vit_large_patch14_clip_224:openai_ft_in1k	.995
8	vit_large_patch14_clip_336:laion2b_ft_in1k	.995
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.995
10	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.993
11	convnext_base:clip_laiona_augreg_ft_in1k_384	.993
12	nasnet_large-tf	.991
13	convnext_xlarge:fb_in22k_ft_in1k	.991
14	convnext_large_imagenet_full_seed-0	.991
15	convnext_large:fb_in22k_ft_in1k	.991
16	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.989
17	nasnet_large	.989
18	fixres_resnext101_32x48d_wsl	.989
19	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.989
20	resnext101_32x32d_wsl	.988
21	resnext101_32x48d_wsl	.988
22	convnext_tiny_imagenet_full_seed-0	.988
23	vit_base_patch16_clip_224:openai_ft_in1k	.988
24	AdvProp_efficientnet-b7	.986
25	resnext101_32x8d_wsl	.986
26	effnetv2m_custom384	.986
27	convnext_small_imagenet_full_seed-0	.986
28	deit_large_imagenet_full_seed-0	.986
29	deit_base_imagenet_full_seed-0	.984
30	convnext_base_imagenet_full_seed-0	.984
31	cvt_cvt-13-384-in1k_4_LucyV4	.984
32	pnasnet_large_halfPrecision	.984
33	pnasnet_large	.984
34	resnext101_32x16d_wsl	.984
35	cvt_cvt-13-224-in1k_4_LucyV4	.982
36	inception_v4-tf	.982
37	xception-keras	.980
38	vit_relpos_base_patch16_clsgap_224:sw_in1k	.977
39	AdvProp_efficientnet-b6	.977
40	pnasnet_large-tf	.975
41	efficientnet-b7	.975
42	BiT-S-R101x3	.975
43	resnet-152_v2-tf	.975
44	convnext_tiny:in12k_ft_in1k	.975
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.975
46	inception_v3_pytorch	.975
47	cvt_cvt-21-384-in1k_4_LucyV4	.975
48	resnet_101_v2	.975
49	resnet_101_v1	.975
50	AdvProp_efficientnet-b4	.973
51	AT_efficientnet-b4	.973
52	convnext_tiny_sup	.973
53	focalnet_tiny_lrf_in1k	.973
54	cvt_cvt-21-224-in1k_4_LucyV4	.973
55	inception_v3	.973
56	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.973
57	resnet-152_v2_pytorch	.973
58	inception_v4	.973
59	deit_small_imagenet_full_seed-0	.971
60	inception_v3-tf	.971
61	effnetb1_272x240	.971
62	resnet-50_v2-tf	.970
63	AT_efficientnet-b2	.968
64	AdvProp_efficientnet-b2	.968
65	AdvProp_efficientnet-b0	.968
66	tv_efficientnet-b1	.966
67	Res2Net50_26w_4s	.964
68	grcnn_109	.964
69	resnet_152_v2	.964
70	BiT-S-R152x2	.964
71	antialiased-rnext101_32x8d	.964
72	antialias-resnet152	.964
73	BiT-S-R152x4	.963
74	resnet-101_v2-tf	.963
75	cv_18_dagger_408_pretrained	.963
76	resnet152_imagenet_full	.963
77	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.963
78	densenet-121-keras	.961
79	resnet101_imagenet_full	.961
80	densenet-121	.961
81	efficientnet_b0	.959
82	densenet-201	.957
83	densenet_201_pytorch	.957
84	resnet-101_v1-tf	.957
85	densenet-201-keras	.957
86	resnet50-moclr8deg	.957
87	resnet-152_v1-tf	.955
88	resnet50-VITO-8deg-cc	.955
89	resnet50_ewconplus_wzc	.955
90	resnet_50_v2	.955
91	densenet-169-keras	.954
92	grcnn	.954
93	resnet50-vitoimagevidnet8	.954
94	resnet_152_v1	.954
95	densenet-169	.954
96	BiT-S-R50x3	.952
97	antialiased-r50	.952
98	inception_v2-tf	.950
99	resnet50	.950
100	resnet50_imagenet_full	.950
101	resnet_SIN_IN_FT_IN	.948
102	resnet50-SIN_IN_IN	.948
103	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.946
104	convnext_femto_ols:d1_in1k	.946
105	resnet-50_v1-tf	.946
106	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.946
107	BiT-S-R50x1	.945
108	resnet-50-pytorch	.943
109	mobilenet_v2_1.4_224-tf	.943
110	mobilenet_v2_1.0_224-tf	.943
111	resnet50-sup	.943
112	resnet50_moco_v2	.943
113	resnet50_tutorial	.943
114	resnet_50_v1	.943
115	voneresnet-50-non_stochastic	.941
116	xception	.941
117	vonegrcnn_62e_nobn	.941
118	vonegrcnn_47e	.941
119	efficientnet_b1_imagenet_full	.939
120	eBarlow_lmda_02_1000ep	.939
121	vonegrcnn_52e_full	.939
122	BiT-S-R101x1	.939
123	mobilenet_v2_1.3_224-tf	.939
124	vonegrcnn_62e_nobn	.938
125	nasnet_mobile	.938
126	mobilenet_v2_1.0_192-tf	.936
127	mobilenet_v2_1.0_160-tf	.936
128	mobilenet_v2_1_0_224	.936
129	resnet_50_v1_spiking	.934
130	vonegrcnn_52e_full	.934
131	resnet-34	.934
132	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.932
133	vonegrcnn_47e	.932
134	ViT_L_32_imagenet1k	.930
135	resnet34_imagenet_full	.930
136	AdvProp_efficientnet-b8	.929
137	mobilenet_v2_1_0_192	.929
138	resnet50_eMMCR_Vanilla	.929
139	mobilenet_v2_1_3_224	.927
140	resnet50-SIN_IN	.927
141	vgg-19-keras	.927
142	inception_v1-tf	.925
143	efficientnet_b2_imagenet_full	.925
144	vgg-16-keras	.923
145	ViT_B_16_imagenet1k	.923
146	voneresnet-50	.920
147	custom_model_cv_18_dagger_408	.920
148	mobilenet_v2_0.75_224-tf	.918
149	regnet_y_400mf	.918
150	ViT_B_32_imagenet1k	.916
151	nasnet_mobile-tf	.914
152	CORnet-S	.912
153	mobilenet_v2_1_0_160	.912
154	blt_vs	.912
155	inception_v1	.912
156	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.911
157	ReAlnet06	.909
158	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.909
159	mobilenet_v2_0_75_224	.907
160	mobilenet_v2_0.75_160-tf	.907
161	mobilenet_v2_0.75_192-tf	.905
162	efficientnet_b0_imagenet_full	.905
163	mobilenet_v2_0_75_160	.905
164	pnasnet_large_pytorch	.905
165	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.904
166	mobilenet_v1_1.0_224	.904
167	mobilenet_v2_0_75_192	.902
168	resnet-18	.900
169	voneresnet-50-robust	.900
170	mobilenet_v2_0_5_224	.900
171	resnet18_imagenet_full	.898
172	mobilenet_v2_1_0_128	.896
173	mobilenet_v2_0.5_224-tf	.896
174	CLIP-RN50	.896
175	resnet50_robust_l2_eps1	.896
176	mobilenet_v2_1.0_128-tf	.895
177	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.893
178	ReAlnet09	.893
179	ReAlnet10	.893
180	vgg_19	.891
181	vgg_16	.889
182	ReAlnet03	.889
183	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.889
184	ReAlnet05	.887
185	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.886
186	ReAlnet04	.886
187	ReAlnet01	.884
188	mobilenet_v1_0.75_192	.884
189	mobilenet_v1_1.0_160	.884
190	ReAlnet08	.882
191	mobilenet_v1_0.75_224	.880
192	shufflenet_v2_x1_0	.880
193	ReAlnet07	.880
194	mobilenet_v2_1.0_96	.877
195	mobilenet_v1_1.0_192	.873
196	ReAlnet02	.873
197	mobilenet_v2_0.5_160	.866
198	mobilenet_v2_0.5_192-tf	.866
199	mobilenet_v1_1.0_128	.866
200	mobilenet_v2_1-4_224_pytorch	.866
201	mobilenet_v2_1_4_224	.866
202	mobilenet_v2_0.75_128	.861
203	mobilenet_v2_0.35_224	.857
204	mobilenet_v2_0_5_192	.855
205	mobilenet_v2_0.75_96	.848
206	mobilenet_v1_0.5_224	.848
207	mobilenet_v1_0.75_128	.848
208	mobilenet_v1_0.75_160	.848
209	mobilenet_v2_0.35_192	.848
210	mobilenet_v2_0.35_160	.848
211	mobilenet_v2_0.5_128	.848
212	resnet-50-robust	.839
213	imagenet_l2_3_0	.839
214	resnet50_robust_l2_eps3	.839
215	resnet50-SIN	.834
216	0.5x_resnet-18_LC_w_sh_1_iter	.834
217	resnet_50_v1_spiking_l4a	.825
218	resnet50_l2_3_robust	.823
219	mobilenet_v1_0.5_160	.821
220	mobilenet_v1_0.5_192	.820
221	convnext_small_imagenet_100_seed-0	.805
222	mobilenet_v2_0.35_128	.798
223	mobilenet_v2_0.5_96	.796
224	squeezenet1_0	.791
225	squeezenet1_1	.784
226	mobilenet_v1_0.5_128	.780
227	alexnet_random_linf8_perturb	.779
228	alexnet_ks_torevert	.777
229	alexnet_fov4	.777
230	alexnet_fov12	.777
231	alexnet_fov16	.777
232	alexnet	.777
233	resnet50_linf_8_robust	.771
234	mobilenet_v2_0.35_96	.768
235	alexnet_early_checkpoint	.757
236	mobilenet_v1_0.25_224	.750
237	mobilenet_v1_0.25_192	.725
238	imagenet_l2_10_0	.718
239	AlexNet_SIN	.704
240	AlexNet_SIN_fov4	.704
241	AlexNet_SIN_fov12	.704
242	AlexNet_SIN_fov16	.704
243	mobilenet_v1_0.25_160	.700
244	mobilenet_v1_0.25_128	.679
245	alexnet_l2_3_robust	.673
246	alexnet_robust_correct	.632
247	resnet50_imagenet_100_seed-0	.625
248	deit_small_imagenet_100_seed-0	.600
249	alexnet_linf_8_robust	.570
250	resnet_50_v1_spiking_l4	.496
251	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.204
252	resnet50_imagenet_10_seed-0	.152
253	convnext_small_imagenet_10_seed-0	.096
254	resnet50_imagenet_1_seed-0	.082
255	deit_small_imagenet_10_seed-0	.075
256	deit_small_imagenet_1_seed-0	.075
257	alexnet_training_seed_06	.068
258	alexnet_training_seed_09	.066
259	resnet-50_untrained	.062
260	resnet50-meshes-lt-100-original-pretrained	.062
261	resnet50-meshes-lt-100-original-scratch	.062
262	alexnet_training_seed_05	.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	resnet_50_v1_spiking_l3	.062
269	alexnet_training_seed_01_fov4	.062
270	alexnet_training_seed_01_fov12	.062
271	alexnet_training_seed_01_fov16	.062
272	resnet50-vicregl0p75	.062
273	alexnet_training_seed_03	.062
274	alexnet_training_seed_07	.061
275	artResNet18_1	.061
276	convnext_small_imagenet_1_seed-0	.057
277	resnet50-barlow	.057
278	barlow-twins-resnet50	.054
279	mobilevit_small	.050
280	resnet50-simclr-vissl	.034
281	resnet50-vicregl0p9	.018
282	ViT-B/32
283	resnet-18-LC_w_sh_100_iter_conv_init_m
284	BiT-M-R50x1
285	resnet-18-LC_w_sh_1_iter_conv_init_m
286	BiT-M-R152x2
287	resnet-18-LC_w_sh_1_iter_m
288	resnet-18-LC_w_sh_10_iter_m
289	BiT-M-R50x3
290	resnet-18_test_m
291	resnet-18-LC_w_sh_100_iter_m
292	resnet-18-LC_w_sh_10_iter_conv_init_m
293	FrankRobWobv0
294	0.5x_resnet-18_LC
295	CORnetZ_CIFAR10
296	resnet18-local_aggregation
297	r3m_resnet18
298	resnet18-deepcluster
299	CORnet-Z
300	vggface
301	resnet18-instance_recognition
302	my-model
303	0.5x_resnet-18_LC_w_sh_10_iter
304	r3m_resnet50
305	r3m_resnet34
306	resnet-18-LC_m
307	resnet18-supervised
308	r3m_resnet50_nocrop
309	resnet-18-LC_1st_conv_m
310	resnet-18-LC_conv_init_m
311	resnet18-contrastive_multiview
312	0.5x_resnet-18
313	RN50
314	0.5x_resnet-18_LC_w_sh_100_iter
315	RN50
316	resnet18-colorization
317	resnet18-relative_position
318	resnet18-autoencoder
319	resnet18-simclr
320	ViT_L_32
321	ViT_B_32
322	resnet18-depth_prediction
323	ViT_B_16
324	r3m_resnet34_nocrop
325	resnet18-contrastive_predictive
326	resnet-18-LC_untrained
327	resnet-18_untrained
328	resnet-50x2_untrained
329	dcgan
330	unet_entire
331	CORnetZ_CIFAR10_bs32_20_04
332	v1-pyr-nodown
333	pixels
334	dorinet_cornet_z
335	omnivore_swinT
336	resnet50
337	openclip
338	vonealexnet_gaussian_noise_std4_fixed
339	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
340	yudixie_resnet50_imagenet1kpret_0_240312
341	yudixie_resnet50_category_class_0_240908
342	yudixie_resnet50_distance_reg_0_240908
343	ConvLSTM
344	PredRNN
345	SimVP
346	TAU
347	MIM
348	hmax
349	ReAlnet01_cornet
350	yudixie_resnet18_distance_translation_0_240719
351	compact_resnet50_V4
352	yolos_tiny
353	yudixie_resnet18_distance_reg_0_240719
354	yudixie_resnet18_rotation_reg_0_240719
355	yudixie_resnet18_imagenet1kpret_0_240719
356	ReAlnet03_cornet
357	yudixie_resnet50_distance_rotation_0_240908
358	yudixie_resnet50_rotation_reg_0_240908
359	vit_base_patch14_dinov2-lvd142m
360	yudixie_resnet50_distance_translation_0_240908
361	ReAlnet01_cornet_fov4
362	resnet18_ecoset_full
363	resnet34_ecoset_full
364	VOneCORnet-S
365	compact_resnet50_IT
366	ReAlnet04_cornet
367	resnet50_ecoset_full
368	resnet101_ecoset_full
369	aughost_fair_resnet18
370	resnet152_ecoset_full
371	compact_vgg19_V4
372	vit_base_patch14_reg4_dinov2-lvd142m
373	yudixie_resnet18_translation_rotation_0_240719
374	yudixie_resnet18_distance_rotation_0_240719
375	ReAlnet01_cornet_fov12
376	ReAlnet05_cornet
377	yudixie_resnet18_category_class_0_240719
378	yudixie_resnet18_random_0_240719
379	yudixie_resnet50_translation_reg_0_240908
380	yudixie_resnet18_cat_obj_class_all_latents_0_240719
381	compact_vgg19_IT
382	yudixie_resnet18_translation_reg_0_240719
383	yudixie_resnet18_distance_translation_rotation_0_240719
384	ReAlnet06_cornet
385	yudixie_resnet18_object_class_0_240719
386	SeLaVi-AVE
387	compact_resnet50_robust_V4
388	ReAlnet01_cornet_fov16
389	r3d_18
390	yudixie_resnet50_translation_rotation_0_240908
391	ReAlnet07_cornet
392	omnivore_swinS
393	mc3_18
394	ReAlnet02_ALL
395	s3d
396	ReAlnet08_cornet
397	omnivore_swinB
398	resnet50-vicreg
399	mvit_v1_b
400	ReAlnet01_ALL
401	compact_cornets_V4
402	ReAlnet03_ALL
403	ReAlnet09_cornet
404	resnet18_imagenet21kP
405	ReAlnet04_ALL
406	compact_cornets_IT
407	vit_base_patch16_dinov3-lvd1689m
408	SeLaVi-Kinetics400
409	SeLaVi-VGG-Sound
410	ReAlnet06_ALL
411	SeLaVi-Kinetics-Sound
412	compact_resnet50_robust_IT
413	yudixie_resnet50_random_0_240908
414	yudixie_resnet50_distance_translation_rotation_0_240908
415	yudixie_resnet50_imagenet1kpret_0_240908
416	ReAlnet07_ALL
417	r2plus1d_18
418	mvit_v2_s
419	ReAlnet08_ALL
420	yudixie_resnet50_object_class_0_240908
421	yudixie_resnet50_cat_obj_class_all_latents_0_240908
422	ReAlnet09_ALL
423	ReAlnet02_cornet
424	resnet50_primary_visual_cortex
425	ReAlnet10_ALL
426	ReAlnet10_cornet
427	briaai_rmbg_1_4
428	ReAlnet05_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: Geirhos2021falsecolour

Metric: top1