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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	alexnet	.306
101	resnet50_l2_3_robust	.306
102	inception_v4	.306
103	mobilenet_v2_1.0_160-tf	.300
104	mobilenet_v2_1.0_224-tf	.300
105	mobilenet_v2_0_5_192	.300
106	mobilenet_v2_0_75_160	.300
107	mobilenet_v2_1_0_192	.300
108	convnext_small_imagenet_100_seed-0	.300
109	mobilenet_v2_0.5_160	.294
110	alexnet-baseline	.294
111	alexnet	.294
112	alexnet	.294
113	alexnet	.294
114	mobilenet_v1_1.0_192	.294
115	alexnet	.294
116	alexnet	.294
117	alexnet	.294
118	mobilenet_v1_0.5_160	.294
119	alexnet	.294
120	alexnet	.294
121	alexnet_ks_torevert	.294
122	alexnet_fov4	.294
123	alexnet_fov12	.294
124	alexnet_fov16	.294
125	resnet101_imagenet_full	.294
126	cvt_cvt-13-224-in1k_4_LucyV4	.294
127	mobilenet_v2_1_0_224	.294
128	mobilenet_v2_1.0_128-tf	.287
129	mobilenet_v2_0.75_160-tf	.287
130	resnet_50_v2	.287
131	resnet-50-robust	.281
132	resnet-101_v2-tf	.281
133	resnet-152_v2-tf	.281
134	mobilenet_v2_0.35_128	.281
135	alexnet_l2_3_robust	.281
136	imagenet_l2_3_0	.281
137	resnet50_robust_l2_eps3	.281
138	resnet50-vitoimagevidnet8	.281
139	mobilenet_v1_0.5_224	.275
140	mobilenet_v1_1.0_160	.275
141	alexnet_early_checkpoint	.275
142	ReAlnet07	.275
143	inception_v3	.275
144	mobilenet_v2_0.75_96	.269
145	mobilenet_v1_1.0_128	.269
146	mobilenet_v1_1.0_224	.269
147	mobilenet_v2_1_0_160	.269
148	inception_v3_pytorch	.269
149	deit_large_imagenet_full_seed-0	.269
150	efficientnet_b2_imagenet_full	.269
151	nasnet_large-tf	.263
152	inception_v3-tf	.263
153	BiT-S-R50x3	.263
154	mobilenet_v1_0.75_224	.263
155	mobilenet_v1_0.25_224	.263
156	voneresnet-50-robust	.263
157	alexnet_random_linf8_perturb	.263
158	resnet34_imagenet_full	.263
159	resnet50_imagenet_full	.263
160	BiT-S-R152x2	.256
161	mobilenet_v1_0.25_192	.256
162	grcnn	.256
163	CORnet-S	.250
164	inception_v1-tf	.250
165	mobilenet_v2_1.0_96	.250
166	mobilenet_v1_0.75_128	.250
167	mobilenet_v1_0.75_160	.250
168	mobilenet_v1_0.25_128	.250
169	resnet-152_v2_pytorch	.250
170	pnasnet_large_pytorch	.250
171	nasnet_large	.250
172	densenet-121-keras	.244
173	BiT-S-R50x1	.244
174	resnet-152_v1-tf	.244
175	resnet-50_v2-tf	.244
176	vgg-19-keras	.244
177	xception-keras	.244
178	cv_18_dagger_408_pretrained	.244
179	custom_model_cv_18_dagger_408	.244
180	cvt_cvt-21-384-in1k_4_LucyV4	.244
181	densenet-121	.244
182	vgg-16-keras	.237
183	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.237
184	resnet_101_v2	.237
185	resnet_101_v1	.237
186	ReAlnet03	.237
187	pnasnet_large-tf	.231
188	resnet50_moco_v2	.231
189	resnet50-VITO-8deg-cc	.231
190	nasnet_mobile-tf	.225
191	ReAlnet04	.225
192	deit_base_imagenet_full_seed-0	.225
193	nasnet_mobile	.225
194	mobilenet_v2_0.5_96	.219
195	resnet50-SIN_IN	.219
196	BiT-S-R152x4	.219
197	alexnet_robust_correct	.219
198	resnet50-moclr8deg	.219
199	ReAlnet08	.219
200	ReAlnet09	.219
201	deit_small_imagenet_full_seed-0	.219
202	inception_v1	.219
203	resnet50	.212
204	antialias-resnet152	.212
205	pnasnet_large	.212
206	ReAlnet02	.212
207	resnet50_robust_l2_eps1	.206
208	resnet_152_v1	.206
209	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.200
210	ViT_L_32_imagenet1k	.200
211	resnet-18	.200
212	ReAlnet10	.200
213	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.194
214	alexnet_linf_8_robust	.194
215	regnet_y_400mf	.194
216	Res2Net50_26w_4s	.194
217	resnet50_eMMCR_Vanilla	.194
218	resnet_SIN_IN_FT_IN	.188
219	vonegrcnn_47e	.188
220	resnet50-SIN_IN_IN	.188
221	inception_v2-tf	.188
222	vgg_19	.188
223	xception	.188
224	vonegrcnn_62e_nobn	.181
225	ReAlnet06	.181
226	ReAlnet01	.181
227	vonegrcnn_47e	.181
228	resnet-50_v1-tf	.175
229	vonegrcnn_52e_full	.169
230	resnet-101_v1-tf	.169
231	voneresnet-50	.169
232	vonegrcnn_62e_nobn	.169
233	vonegrcnn_52e_full	.169
234	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
235	0.5x_resnet-18_LC_w_sh_1_iter	.163
236	resnet-34	.163
237	deit_small_imagenet_100_seed-0	.156
238	ReAlnet05	.156
239	squeezenet1_0	.150
240	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.150
241	resnet_152_v2	.150
242	squeezenet1_1	.144
243	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.144
244	vgg_16	.144
245	resnet50-vicregl0p75	.144
246	resnet-50-pytorch	.138
247	resnet50-sup	.138
248	antialiased-r50	.138
249	resnet_50_v1_spiking	.138
250	resnet50_tutorial	.138
251	resnet_50_v1	.138
252	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.131
253	voneresnet-50-non_stochastic	.125
254	ViT_B_16_imagenet1k	.125
255	resnet50_imagenet_10_seed-0	.119
256	ViT_B_32_imagenet1k	.106
257	resnet_50_v1_spiking_l4	.106
258	resnet_50_v1_spiking_l4a	.106
259	shufflenet_v2_x1_0	.100
260	alexnet_training_seed_09	.094
261	resnet50-simclr-vissl	.087
262	barlow-twins-resnet50	.081
263	resnet50_imagenet_100_seed-0	.081
264	resnet50-meshes-lt-100-original-scratch	.069
265	convnext_small_imagenet_10_seed-0	.069
266	alexnet_training_seed_04	.069
267	alexnet_training_seed_07	.069
268	alexnet_training_seed_01	.069
269	alexnet_training_seed_01_fov4	.069
270	alexnet_training_seed_01_fov12	.069
271	alexnet_training_seed_01_fov16	.069
272	resnet-50_untrained	.062
273	resnet50-meshes-lt-100-original-pretrained	.062
274	mobilevit_small	.062
275	alexnet_training_seed_02	.062
276	alexnet_training_seed_08	.062
277	alexnet_training_seed_10	.062
278	deit_small_imagenet_10_seed-0	.062
279	convnext_small_imagenet_1_seed-0	.062
280	resnet50_imagenet_1_seed-0	.062
281	deit_small_imagenet_1_seed-0	.062
282	alexnet_training_seed_03	.062
283	alexnet_training_seed_05	.056
284	alexnet_training_seed_06	.056
285	resnet_50_v1_spiking_l3	.050
286	artResNet18_1	.044
287	resnet50-barlow	.037
288	resnet50-vicregl0p9	.037
289	ViT-B/32
290	resnet-18-LC_w_sh_100_iter_conv_init_m
291	BiT-M-R50x1
292	resnet-18-LC_w_sh_1_iter_conv_init_m
293	BiT-M-R152x2
294	resnet-18-LC_w_sh_1_iter_m
295	resnet-18-LC_w_sh_10_iter_m
296	BiT-M-R50x3
297	resnet-18-LC_w_sh_100_iter_m
298	resnet-18-LC_w_sh_10_iter_conv_init_m
299	FrankRobWobv0
300	0.5x_resnet-18_LC
301	CORnetZ_CIFAR10
302	resnet18-local_aggregation
303	r3m_resnet18
304	resnet18-deepcluster
305	CORnet-Z
306	vggface
307	my-model
308	resnet18-instance_recognition
309	0.5x_resnet-18_LC_w_sh_10_iter
310	r3m_resnet50
311	r3m_resnet34
312	resnet-18-LC_m
313	resnet18-supervised
314	r3m_resnet50_nocrop
315	resnet-18-LC_1st_conv_m
316	resnet-18-LC_conv_init_m
317	resnet18-contrastive_multiview
318	0.5x_resnet-18
319	RN50
320	0.5x_resnet-18_LC_w_sh_100_iter
321	RN50
322	resnet18-colorization
323	resnet18-relative_position
324	resnet18-autoencoder
325	resnet18-simclr
326	ViT_L_32
327	ViT_B_32
328	resnet18-depth_prediction
329	ViT_B_16
330	r3m_resnet34_nocrop
331	resnet18-contrastive_predictive
332	resnet-18-LC_untrained
333	resnet-18_untrained
334	resnet-50x2_untrained
335	dcgan
336	unet_entire
337	CORnetZ_CIFAR10_bs32_20_04
338	v1-pyr-nodown
339	pixels
340	dorinet_cornet_z
341	omnivore_swinT
342	omnivore_swinS
343	resnet50
344	openclip
345	vonealexnet_gaussian_noise_std4_fixed
346	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
347	yudixie_resnet50_category_class_0_240908
348	yudixie_resnet50_distance_reg_0_240908
349	ConvLSTM
350	PredRNN
351	SimVP
352	TAU
353	briaai_rmbg_1_4
354	MIM
355	yolos_tiny
356	ReAlnet01_cornet
357	compact_resnet50_V4
358	ReAlnet02_cornet
359	yudixie_resnet18_rotation_reg_0_240719
360	yudixie_resnet50_rotation_reg_0_240908
361	yudixie_resnet50_distance_rotation_0_240908
362	compact_resnet50_IT
363	yudixie_resnet50_distance_translation_0_240908
364	ReAlnet01_cornet_fov4
365	resnet18_ecoset_full
366	VOneCORnet-S
367	ReAlnet04_cornet
368	resnet101_ecoset_full
369	ReAlnet01_cornet_fov12
370	compact_vgg19_V4
371	resnet152_ecoset_full
372	ReAlnet03_cornet
373	yudixie_resnet18_distance_translation_0_240719
374	resnet-18_test_m
375	yudixie_resnet18_distance_rotation_0_240719
376	ReAlnet05_cornet
377	yudixie_resnet18_category_class_0_240719
378	yudixie_resnet50_translation_reg_0_240908
379	compact_resnet50_robust_V4
380	yudixie_resnet18_translation_reg_0_240719
381	yudixie_resnet18_distance_translation_rotation_0_240719
382	compact_vgg19_IT
383	SeLaVi-AVE
384	hmax
385	ReAlnet06_cornet
386	ReAlnet01_cornet_fov16
387	yudixie_resnet50_translation_rotation_0_240908
388	ReAlnet01_ALL
389	r3d_18
390	yudixie_resnet18_distance_reg_0_240719
391	ReAlnet02_ALL
392	ReAlnet07_cornet
393	mc3_18
394	s3d
395	omnivore_swinB
396	resnet50-vicreg
397	ReAlnet08_cornet
398	mvit_v1_b
399	compact_cornets_V4
400	ReAlnet03_ALL
401	ReAlnet04_ALL
402	resnet18_imagenet21kP
403	ReAlnet09_cornet
404	compact_cornets_IT
405	SeLaVi-Kinetics400
406	ReAlnet05_ALL
407	SeLaVi-VGG-Sound
408	SeLaVi-Kinetics-Sound
409	ReAlnet06_ALL
410	compact_resnet50_robust_IT
411	ReAlnet10_cornet
412	yudixie_resnet50_imagenet1kpret_0_240908
413	yudixie_resnet50_distance_translation_rotation_0_240908
414	ReAlnet07_ALL
415	r2plus1d_18
416	mvit_v2_s
417	resnet34_ecoset_full
418	resnet50_ecoset_full
419	ReAlnet08_ALL
420	yudixie_resnet18_object_class_0_240719
421	yudixie_resnet50_cat_obj_class_all_latents_0_240908
422	yudixie_resnet18_cat_obj_class_all_latents_0_240719
423	yudixie_resnet18_random_0_240719
424	ReAlnet09_ALL
425	yudixie_resnet18_translation_rotation_0_240719
426	yudixie_resnet50_imagenet1kpret_0_240312
427	yudixie_resnet18_imagenet1kpret_0_240719
428	resnet50_primary_visual_cortex
429	yudixie_resnet50_object_class_0_240908
430	ReAlnet10_ALL
431	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