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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021edge-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	.963
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.944
3	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.925
4	vit_large_patch14_clip_224:laion2b_ft_in1k	.925
5	vit_large_patch14_clip_336:laion2b_ft_in1k	.906
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.900
7	vit_large_patch14_clip_224:openai_ft_in1k	.894
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.894
9	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.894
10	resnext101_32x32d_wsl	.881
11	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.881
12	convnext_base:clip_laiona_augreg_ft_in1k_384	.863
13	convnext_large:fb_in22k_ft_in1k	.844
14	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.825
15	convnext_xlarge:fb_in22k_ft_in1k	.812
16	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.800
17	resnext101_32x48d_wsl	.775
18	resnext101_32x8d_wsl	.769
19	resnext101_32x16d_wsl	.762
20	vit_base_patch16_clip_224:openai_ft_in1k	.756
21	fixres_resnext101_32x48d_wsl	.731
22	effnetv2m_custom384	.719
23	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.700
24	convnext_tiny:in12k_ft_in1k	.694
25	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.669
26	AdvProp_efficientnet-b7	.662
27	AdvProp_efficientnet-b4	.600
28	AdvProp_efficientnet-b6	.600
29	AT_efficientnet-b4	.600
30	AlexNet_SIN	.581
31	AlexNet_SIN_fov4	.581
32	AlexNet_SIN_fov12	.581
33	AlexNet_SIN_fov16	.581
34	AdvProp_efficientnet-b8	.575
35	effnetb1_272x240	.569
36	tv_efficientnet-b1	.506
37	grcnn_109	.506
38	CLIP-RN50	.481
39	resnet50-SIN	.469
40	AT_efficientnet-b2	.469
41	AdvProp_efficientnet-b2	.469
42	vit_relpos_base_patch16_clsgap_224:sw_in1k	.438
43	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.438
44	convnext_large_imagenet_full_seed-0	.412
45	convnext_base_imagenet_full_seed-0	.400
46	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.400
47	convnext_small_imagenet_full_seed-0	.394
48	AdvProp_efficientnet-b0	.388
49	focalnet_tiny_lrf_in1k	.388
50	densenet-201-keras	.381
51	densenet-201	.381
52	densenet_201_pytorch	.381
53	mobilenet_v2_0.5_224-tf	.369
54	mobilenet_v2_0.75_192-tf	.369
55	efficientnet_b0	.362
56	efficientnet-b7	.362
57	mobilenet_v2_1.3_224-tf	.362
58	mobilenet_v2_0.75_224-tf	.362
59	mobilenet_v2_1_0_128	.362
60	cvt_cvt-21-224-in1k_4_LucyV4	.356
61	mobilenet_v2_0.5_128	.350
62	blt_vs	.350
63	mobilenet_v2_0_5_224	.350
64	mobilenet_v2_1_3_224	.350
65	mobilenet_v2_0.35_160	.344
66	imagenet_l2_10_0	.344
67	mobilenet_v2_0_75_224	.344
68	mobilenet_v2_0_75_192	.344
69	convnext_tiny_imagenet_full_seed-0	.344
70	inception_v4-tf	.338
71	mobilenet_v1_0.25_160	.338
72	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.338
73	mobilenet_v2_0.35_224	.331
74	mobilenet_v2_1.4_224-tf	.331
75	resnet152_imagenet_full	.331
76	convnext_femto_ols:d1_in1k	.331
77	BiT-S-R101x3	.325
78	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.325
79	densenet-169-keras	.325
80	mobilenet_v1_0.75_192	.325
81	resnet50_linf_8_robust	.325
82	efficientnet_b0_imagenet_full	.325
83	mobilenet_v2_1-4_224_pytorch	.325
84	resnet18_imagenet_full	.325
85	mobilenet_v2_1_4_224	.325
86	densenet-169	.325
87	mobilenet_v2_0.75_128	.319
88	mobilenet_v2_0.35_192	.319
89	convnext_tiny_sup	.319
90	antialiased-rnext101_32x8d	.319
91	BiT-S-R101x1	.312
92	mobilenet_v2_0.5_192-tf	.312
93	mobilenet_v1_0.5_128	.312
94	resnet50_ewconplus_wzc	.312
95	efficientnet_b1_imagenet_full	.312
96	cvt_cvt-13-384-in1k_4_LucyV4	.312
97	mobilenet_v2_1.0_192-tf	.306
98	mobilenet_v1_0.5_192	.306
99	mobilenet_v2_0.35_96	.306
100	resnet50_l2_3_robust	.306
101	inception_v4	.306
102	mobilenet_v2_1.0_160-tf	.300
103	mobilenet_v2_1.0_224-tf	.300
104	mobilenet_v2_0_5_192	.300
105	mobilenet_v2_0_75_160	.300
106	mobilenet_v2_1_0_192	.300
107	convnext_small_imagenet_100_seed-0	.300
108	mobilenet_v2_0.5_160	.294
109	alexnet	.294
110	mobilenet_v1_1.0_192	.294
111	mobilenet_v1_0.5_160	.294
112	alexnet_ks_torevert	.294
113	alexnet_fov4	.294
114	alexnet_fov12	.294
115	alexnet_fov16	.294
116	resnet101_imagenet_full	.294
117	cvt_cvt-13-224-in1k_4_LucyV4	.294
118	mobilenet_v2_1_0_224	.294
119	mobilenet_v2_1.0_128-tf	.287
120	mobilenet_v2_0.75_160-tf	.287
121	resnet_50_v2	.287
122	resnet-50-robust	.281
123	resnet-101_v2-tf	.281
124	resnet-152_v2-tf	.281
125	mobilenet_v2_0.35_128	.281
126	alexnet_l2_3_robust	.281
127	imagenet_l2_3_0	.281
128	resnet50_robust_l2_eps3	.281
129	resnet50-vitoimagevidnet8	.281
130	mobilenet_v1_0.5_224	.275
131	mobilenet_v1_1.0_160	.275
132	alexnet_early_checkpoint	.275
133	ReAlnet07	.275
134	inception_v3	.275
135	mobilenet_v2_0.75_96	.269
136	mobilenet_v1_1.0_128	.269
137	mobilenet_v1_1.0_224	.269
138	mobilenet_v2_1_0_160	.269
139	inception_v3_pytorch	.269
140	deit_large_imagenet_full_seed-0	.269
141	efficientnet_b2_imagenet_full	.269
142	nasnet_large-tf	.263
143	inception_v3-tf	.263
144	BiT-S-R50x3	.263
145	mobilenet_v1_0.75_224	.263
146	mobilenet_v1_0.25_224	.263
147	voneresnet-50-robust	.263
148	alexnet_random_linf8_perturb	.263
149	resnet34_imagenet_full	.263
150	resnet50_imagenet_full	.263
151	BiT-S-R152x2	.256
152	mobilenet_v1_0.25_192	.256
153	grcnn	.256
154	CORnet-S	.250
155	inception_v1-tf	.250
156	mobilenet_v2_1.0_96	.250
157	mobilenet_v1_0.75_128	.250
158	mobilenet_v1_0.75_160	.250
159	mobilenet_v1_0.25_128	.250
160	resnet-152_v2_pytorch	.250
161	pnasnet_large_pytorch	.250
162	nasnet_large	.250
163	densenet-121-keras	.244
164	BiT-S-R50x1	.244
165	resnet-152_v1-tf	.244
166	resnet-50_v2-tf	.244
167	vgg-19-keras	.244
168	xception-keras	.244
169	cv_18_dagger_408_pretrained	.244
170	custom_model_cv_18_dagger_408	.244
171	cvt_cvt-21-384-in1k_4_LucyV4	.244
172	densenet-121	.244
173	vgg-16-keras	.237
174	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.237
175	resnet_101_v2	.237
176	resnet_101_v1	.237
177	ReAlnet03	.237
178	pnasnet_large-tf	.231
179	resnet50_moco_v2	.231
180	resnet50-VITO-8deg-cc	.231
181	nasnet_mobile-tf	.225
182	ReAlnet04	.225
183	deit_base_imagenet_full_seed-0	.225
184	nasnet_mobile	.225
185	mobilenet_v2_0.5_96	.219
186	resnet50-SIN_IN	.219
187	BiT-S-R152x4	.219
188	alexnet_robust_correct	.219
189	resnet50-moclr8deg	.219
190	ReAlnet08	.219
191	ReAlnet09	.219
192	deit_small_imagenet_full_seed-0	.219
193	inception_v1	.219
194	resnet50	.212
195	antialias-resnet152	.212
196	pnasnet_large_halfPrecision	.212
197	pnasnet_large	.212
198	ReAlnet02	.212
199	resnet50_robust_l2_eps1	.206
200	resnet_152_v1	.206
201	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.200
202	ViT_L_32_imagenet1k	.200
203	resnet-18	.200
204	ReAlnet10	.200
205	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.194
206	alexnet_linf_8_robust	.194
207	regnet_y_400mf	.194
208	Res2Net50_26w_4s	.194
209	resnet50_eMMCR_Vanilla	.194
210	resnet_SIN_IN_FT_IN	.188
211	vonegrcnn_47e	.188
212	resnet50-SIN_IN_IN	.188
213	inception_v2-tf	.188
214	vgg_19	.188
215	xception	.188
216	vonegrcnn_62e_nobn	.181
217	ReAlnet06	.181
218	ReAlnet01	.181
219	vonegrcnn_47e	.181
220	resnet-50_v1-tf	.175
221	vonegrcnn_52e_full	.169
222	resnet-101_v1-tf	.169
223	voneresnet-50	.169
224	vonegrcnn_62e_nobn	.169
225	vonegrcnn_52e_full	.169
226	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
227	0.5x_resnet-18_LC_w_sh_1_iter	.163
228	resnet-34	.163
229	deit_small_imagenet_100_seed-0	.156
230	ReAlnet05	.156
231	squeezenet1_0	.150
232	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.150
233	resnet_152_v2	.150
234	squeezenet1_1	.144
235	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.144
236	vgg_16	.144
237	resnet50-vicregl0p75	.144
238	resnet-50-pytorch	.138
239	resnet50-sup	.138
240	antialiased-r50	.138
241	resnet_50_v1_spiking	.138
242	resnet50_tutorial	.138
243	resnet_50_v1	.138
244	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.131
245	voneresnet-50-non_stochastic	.125
246	ViT_B_16_imagenet1k	.125
247	resnet50_imagenet_10_seed-0	.119
248	ViT_B_32_imagenet1k	.106
249	resnet_50_v1_spiking_l4	.106
250	resnet_50_v1_spiking_l4a	.106
251	shufflenet_v2_x1_0	.100
252	alexnet_training_seed_09	.094
253	resnet50-simclr-vissl	.087
254	barlow-twins-resnet50	.081
255	resnet50_imagenet_100_seed-0	.081
256	resnet50-meshes-lt-100-original-scratch	.069
257	convnext_small_imagenet_10_seed-0	.069
258	alexnet_training_seed_04	.069
259	alexnet_training_seed_07	.069
260	alexnet_training_seed_01	.069
261	alexnet_training_seed_01_fov4	.069
262	alexnet_training_seed_01_fov12	.069
263	alexnet_training_seed_01_fov16	.069
264	resnet-50_untrained	.062
265	resnet50-meshes-lt-100-original-pretrained	.062
266	mobilevit_small	.062
267	alexnet_training_seed_02	.062
268	alexnet_training_seed_08	.062
269	alexnet_training_seed_10	.062
270	deit_small_imagenet_10_seed-0	.062
271	convnext_small_imagenet_1_seed-0	.062
272	resnet50_imagenet_1_seed-0	.062
273	deit_small_imagenet_1_seed-0	.062
274	alexnet_training_seed_03	.062
275	alexnet_training_seed_05	.056
276	alexnet_training_seed_06	.056
277	resnet_50_v1_spiking_l3	.050
278	artResNet18_1	.044
279	resnet50-barlow	.037
280	resnet50-vicregl0p9	.037
281	ViT-B/32
282	resnet-18-LC_w_sh_100_iter_conv_init_m
283	BiT-M-R50x1
284	resnet-18-LC_w_sh_1_iter_conv_init_m
285	BiT-M-R152x2
286	resnet-18-LC_w_sh_1_iter_m
287	resnet-18-LC_w_sh_10_iter_m
288	BiT-M-R50x3
289	resnet-18-LC_w_sh_100_iter_m
290	resnet-18-LC_w_sh_10_iter_conv_init_m
291	FrankRobWobv0
292	0.5x_resnet-18_LC
293	CORnetZ_CIFAR10
294	resnet18-local_aggregation
295	r3m_resnet18
296	resnet18-deepcluster
297	CORnet-Z
298	vggface
299	my-model
300	resnet18-instance_recognition
301	0.5x_resnet-18_LC_w_sh_10_iter
302	r3m_resnet50
303	r3m_resnet34
304	resnet-18-LC_m
305	resnet18-supervised
306	r3m_resnet50_nocrop
307	resnet-18-LC_1st_conv_m
308	resnet-18-LC_conv_init_m
309	resnet18-contrastive_multiview
310	0.5x_resnet-18
311	RN50
312	0.5x_resnet-18_LC_w_sh_100_iter
313	RN50
314	resnet18-colorization
315	resnet18-relative_position
316	resnet18-autoencoder
317	resnet18-simclr
318	ViT_L_32
319	ViT_B_32
320	resnet18-depth_prediction
321	ViT_B_16
322	r3m_resnet34_nocrop
323	resnet18-contrastive_predictive
324	resnet-18-LC_untrained
325	resnet-18_untrained
326	resnet-50x2_untrained
327	dcgan
328	unet_entire
329	CORnetZ_CIFAR10_bs32_20_04
330	v1-pyr-nodown
331	pixels
332	dorinet_cornet_z
333	omnivore_swinT
334	omnivore_swinS
335	resnet50
336	openclip
337	vonealexnet_gaussian_noise_std4_fixed
338	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
339	yudixie_resnet50_category_class_0_240908
340	yudixie_resnet50_distance_reg_0_240908
341	ConvLSTM
342	PredRNN
343	SimVP
344	TAU
345	briaai_rmbg_1_4
346	MIM
347	yolos_tiny
348	ReAlnet01_cornet
349	compact_resnet50_V4
350	ReAlnet02_cornet
351	yudixie_resnet18_rotation_reg_0_240719
352	yudixie_resnet50_rotation_reg_0_240908
353	eBarlow_lmda_02_1000ep
354	vit_base_patch14_dinov2-lvd142m
355	vit_base_patch14_reg4_dinov2-lvd142m
356	yudixie_resnet50_distance_rotation_0_240908
357	compact_resnet50_IT
358	yudixie_resnet50_distance_translation_0_240908
359	ReAlnet01_cornet_fov4
360	resnet18_ecoset_full
361	VOneCORnet-S
362	ReAlnet04_cornet
363	resnet101_ecoset_full
364	ReAlnet01_cornet_fov12
365	compact_vgg19_V4
366	vit_base_patch16_dinov3-lvd1689m
367	resnet152_ecoset_full
368	ReAlnet03_cornet
369	yudixie_resnet18_distance_translation_0_240719
370	aughost_fair_resnet18
371	resnet-18_test_m
372	yudixie_resnet18_distance_rotation_0_240719
373	ReAlnet05_cornet
374	yudixie_resnet18_category_class_0_240719
375	yudixie_resnet50_translation_reg_0_240908
376	compact_resnet50_robust_V4
377	yudixie_resnet18_translation_reg_0_240719
378	yudixie_resnet18_distance_translation_rotation_0_240719
379	compact_vgg19_IT
380	SeLaVi-AVE
381	hmax
382	ReAlnet06_cornet
383	ReAlnet01_cornet_fov16
384	yudixie_resnet50_translation_rotation_0_240908
385	ReAlnet01_ALL
386	r3d_18
387	yudixie_resnet18_distance_reg_0_240719
388	ReAlnet02_ALL
389	ReAlnet07_cornet
390	mc3_18
391	s3d
392	omnivore_swinB
393	resnet50-vicreg
394	ReAlnet08_cornet
395	mvit_v1_b
396	compact_cornets_V4
397	ReAlnet03_ALL
398	ReAlnet04_ALL
399	resnet18_imagenet21kP
400	ReAlnet09_cornet
401	compact_cornets_IT
402	SeLaVi-Kinetics400
403	ReAlnet05_ALL
404	SeLaVi-VGG-Sound
405	SeLaVi-Kinetics-Sound
406	ReAlnet06_ALL
407	compact_resnet50_robust_IT
408	ReAlnet10_cornet
409	yudixie_resnet50_imagenet1kpret_0_240908
410	yudixie_resnet50_distance_translation_rotation_0_240908
411	ReAlnet07_ALL
412	r2plus1d_18
413	mvit_v2_s
414	resnet34_ecoset_full
415	resnet50_ecoset_full
416	ReAlnet08_ALL
417	yudixie_resnet18_object_class_0_240719
418	yudixie_resnet50_cat_obj_class_all_latents_0_240908
419	yudixie_resnet18_cat_obj_class_all_latents_0_240719
420	yudixie_resnet18_random_0_240719
421	ReAlnet09_ALL
422	yudixie_resnet18_translation_rotation_0_240719
423	yudixie_resnet50_imagenet1kpret_0_240312
424	yudixie_resnet18_imagenet1kpret_0_240719
425	resnet50_primary_visual_cortex
426	yudixie_resnet50_object_class_0_240908
427	ReAlnet10_ALL
428	yudixie_resnet50_random_0_240908

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: Geirhos2021edge

Metric: top1