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