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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021sketch-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.974
2	vit_large_patch14_clip_224:openai_ft_in1k	.958
3	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.951
4	vit_large_patch14_clip_336:laion2b_ft_in1k	.946
5	vit_large_patch14_clip_224:laion2b_ft_in1k	.946
6	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.945
7	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.940
8	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.935
9	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.927
10	convnext_base:clip_laiona_augreg_ft_in1k_384	.925
11	resnext101_32x32d_wsl	.911
12	resnext101_32x16d_wsl	.909
13	resnext101_32x48d_wsl	.905
14	fixres_resnext101_32x48d_wsl	.902
15	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.902
16	vit_base_patch16_clip_224:openai_ft_in1k	.897
17	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.884
18	convnext_xlarge:fb_in22k_ft_in1k	.879
19	convnext_large:fb_in22k_ft_in1k	.873
20	resnext101_32x8d_wsl	.871
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.869
22	effnetv2m_custom384	.850
23	CLIP-RN50	.824
24	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.806
25	AdvProp_efficientnet-b7	.795
26	AdvProp_efficientnet-b6	.787
27	effnetb1_272x240	.779
28	convnext_tiny:in12k_ft_in1k	.779
29	convnext_base_imagenet_full_seed-0	.767
30	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.766
31	resnet50-SIN	.765
32	convnext_large_imagenet_full_seed-0	.760
33	convnext_small_imagenet_full_seed-0	.756
34	AdvProp_efficientnet-b8	.740
35	AdvProp_efficientnet-b4	.736
36	AT_efficientnet-b4	.736
37	efficientnet-b7	.731
38	resnet50-SIN_IN	.730
39	deit_large_imagenet_full_seed-0	.728
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.721
41	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.719
42	AT_efficientnet-b2	.713
43	AdvProp_efficientnet-b2	.713
44	focalnet_tiny_lrf_in1k	.706
45	antialias-resnet152	.695
46	cvt_cvt-13-224-in1k_4_LucyV4	.689
47	AdvProp_efficientnet-b0	.686
48	resnet-152_v2-tf	.682
49	pnasnet_large-tf	.681
50	nasnet_large-tf	.681
51	inception_v4-tf	.680
52	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.680
53	convnext_tiny_imagenet_full_seed-0	.680
54	densenet-201-keras	.677
55	densenet-201	.677
56	densenet_201_pytorch	.677
57	deit_base_imagenet_full_seed-0	.676
58	cvt_cvt-13-384-in1k_4_LucyV4	.675
59	nasnet_large	.672
60	pnasnet_large	.672
61	antialiased-rnext101_32x8d	.670
62	resnet_152_v1	.667
63	xception-keras	.661
64	convnext_tiny_sup	.660
65	inception_v4	.660
66	tv_efficientnet-b1	.657
67	densenet-169-keras	.656
68	deit_small_imagenet_full_seed-0	.656
69	densenet-169	.656
70	resnet_50_v2	.656
71	voneresnet-50-robust	.655
72	resnet_SIN_IN_FT_IN	.654
73	resnet50-SIN_IN_IN	.654
74	cvt_cvt-21-224-in1k_4_LucyV4	.654
75	ReAlnet02	.654
76	resnet-101_v2-tf	.652
77	cv_18_dagger_408_pretrained	.652
78	resnet-50_v2-tf	.651
79	resnet50_ewconplus_wzc	.650
80	resnet_101_v2	.650
81	resnet50-vitoimagevidnet8	.649
82	voneresnet-50-non_stochastic	.645
83	resnet50-VITO-8deg-cc	.645
84	inception_v3	.645
85	ReAlnet04	.644
86	AlexNet_SIN	.642
87	AlexNet_SIN_fov4	.642
88	AlexNet_SIN_fov12	.642
89	AlexNet_SIN_fov16	.642
90	inception_v2-tf	.641
91	grcnn_109	.641
92	convnext_femto_ols:d1_in1k	.637
93	efficientnet_b0	.636
94	resnet50_robust_l2_eps1	.634
95	ReAlnet07	.634
96	ReAlnet09	.634
97	inception_v3-tf	.632
98	ReAlnet10	.631
99	inception_v3_pytorch	.631
100	resnet152_imagenet_full	.631
101	resnet_101_v1	.631
102	densenet-121-keras	.630
103	blt_vs	.630
104	densenet-121	.630
105	ReAlnet01	.627
106	inception_v1-tf	.626
107	resnet-152_v1-tf	.626
108	ReAlnet08	.626
109	resnet50	.625
110	ReAlnet03	.625
111	Res2Net50_26w_4s	.621
112	ReAlnet05	.620
113	mobilenet_v2_1.0_128-tf	.618
114	BiT-S-R101x3	.616
115	resnet-34	.615
116	ReAlnet06	.615
117	BiT-S-R152x4	.614
118	resnet-101_v1-tf	.614
119	resnet50_imagenet_full	.614
120	grcnn	.613
121	antialiased-r50	.611
122	vgg-19-keras	.610
123	resnet_152_v2	.610
124	custom_model_cv_18_dagger_408	.609
125	resnet101_imagenet_full	.609
126	resnet-152_v2_pytorch	.608
127	efficientnet_b2_imagenet_full	.606
128	efficientnet_b1_imagenet_full	.606
129	mobilenet_v2_1.0_160-tf	.604
130	resnet-50-robust	.604
131	imagenet_l2_3_0	.604
132	resnet50_robust_l2_eps3	.604
133	cvt_cvt-21-384-in1k_4_LucyV4	.604
134	vonegrcnn_52e_full	.603
135	BiT-S-R152x2	.601
136	resnet50_l2_3_robust	.601
137	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.601
138	vonegrcnn_62e_nobn	.601
139	CORnet-S	.600
140	vonegrcnn_47e	.600
141	mobilenet_v2_1.4_224-tf	.599
142	resnet-50_v1-tf	.599
143	vonegrcnn_52e_full	.598
144	resnet-50-pytorch	.596
145	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.596
146	resnet50-sup	.596
147	resnet50_tutorial	.596
148	resnet_50_v1	.596
149	vonegrcnn_47e	.596
150	vonegrcnn_62e_nobn	.595
151	mobilenet_v2_1.0_224-tf	.595
152	resnet50_linf_8_robust	.595
153	mobilenet_v2_1_0_128	.595
154	resnet_50_v1_spiking	.594
155	nasnet_mobile	.594
156	resnet34_imagenet_full	.593
157	voneresnet-50	.591
158	regnet_y_400mf	.590
159	resnet18_imagenet_full	.588
160	mobilenet_v2_1_0_192	.585
161	BiT-S-R101x1	.583
162	vgg-16-keras	.583
163	mobilenet_v2_1_0_160	.583
164	mobilenet_v2_1_0_224	.583
165	mobilenet_v2_1.0_192-tf	.580
166	resnet-18	.580
167	inception_v1	.574
168	mobilenet_v2_0.75_224-tf	.573
169	mobilenet_v2_0_75_224	.573
170	mobilenet_v2_1.3_224-tf	.571
171	BiT-S-R50x3	.562
172	BiT-S-R50x1	.562
173	efficientnet_b0_imagenet_full	.562
174	vgg_19	.561
175	mobilenet_v2_0.75_160-tf	.560
176	mobilenet_v2_0.75_128	.560
177	mobilenet_v1_1.0_160	.559
178	mobilenet_v2_0.75_192-tf	.557
179	mobilenet_v2_1_3_224	.557
180	mobilenet_v1_1.0_192	.549
181	mobilenet_v2_0.5_192-tf	.547
182	mobilenet_v2_0_75_160	.547
183	shufflenet_v2_x1_0	.546
184	mobilenet_v2_1-4_224_pytorch	.546
185	mobilenet_v2_1_4_224	.546
186	mobilenet_v2_0_5_192	.545
187	nasnet_mobile-tf	.542
188	vgg_16	.542
189	mobilenet_v1_1.0_224	.540
190	mobilenet_v2_0_75_192	.540
191	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.535
192	mobilenet_v1_0.75_224	.532
193	mobilenet_v1_0.75_160	.531
194	resnet_50_v1_spiking_l4a	.531
195	resnet50_moco_v2	.529
196	ViT_L_32_imagenet1k	.526
197	resnet50-moclr8deg	.525
198	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.524
199	mobilenet_v1_0.75_192	.522
200	mobilenet_v2_0.5_128	.522
201	mobilenet_v2_0_5_224	.521
202	mobilenet_v2_0.5_224-tf	.520
203	mobilenet_v2_1.0_96	.520
204	convnext_small_imagenet_100_seed-0	.519
205	mobilenet_v1_0.5_224	.517
206	mobilenet_v2_0.75_96	.514
207	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.514
208	mobilenet_v2_0.5_160	.511
209	mobilenet_v1_0.75_128	.510
210	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.509
211	mobilenet_v1_1.0_128	.505
212	xception	.505
213	pnasnet_large_pytorch	.502
214	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.499
215	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.499
216	resnet50_eMMCR_Vanilla	.487
217	mobilenet_v2_0.35_192	.485
218	mobilenet_v2_0.35_160	.485
219	mobilenet_v1_0.5_192	.480
220	imagenet_l2_10_0	.479
221	mobilenet_v1_0.5_160	.472
222	alexnet_random_linf8_perturb	.466
223	mobilenet_v2_0.35_128	.461
224	ViT_B_32_imagenet1k	.461
225	mobilenet_v2_0.35_224	.456
226	squeezenet1_1	.456
227	alexnet	.456
228	0.5x_resnet-18_LC_w_sh_1_iter	.455
229	mobilenet_v1_0.5_128	.455
230	alexnet-baseline	.450
231	alexnet	.450
232	alexnet	.450
233	alexnet	.450
234	alexnet	.450
235	alexnet	.450
236	alexnet	.450
237	alexnet	.450
238	alexnet	.450
239	alexnet_ks_torevert	.450
240	alexnet_fov4	.450
241	alexnet_fov12	.450
242	alexnet_fov16	.450
243	mobilenet_v1_0.25_224	.449
244	squeezenet1_0	.444
245	mobilenet_v2_0.5_96	.438
246	alexnet_early_checkpoint	.431
247	mobilenet_v2_0.35_96	.419
248	mobilenet_v1_0.25_192	.410
249	mobilenet_v1_0.25_160	.380
250	alexnet_l2_3_robust	.379
251	mobilenet_v1_0.25_128	.364
252	alexnet_robust_correct	.351
253	resnet_50_v1_spiking_l4	.340
254	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.334
255	alexnet_linf_8_robust	.323
256	resnet50_imagenet_100_seed-0	.319
257	deit_small_imagenet_100_seed-0	.281
258	resnet50_imagenet_10_seed-0	.156
259	convnext_small_imagenet_10_seed-0	.085
260	artResNet18_1	.083
261	deit_small_imagenet_10_seed-0	.080
262	resnet-50_untrained	.076
263	resnet50_imagenet_1_seed-0	.076
264	resnet50-vicregl0p9	.075
265	resnet50-simclr-vissl	.072
266	resnet50-meshes-lt-100-original-pretrained	.066
267	resnet50-vicregl0p75	.065
268	alexnet_training_seed_07	.064
269	deit_small_imagenet_1_seed-0	.064
270	resnet50-meshes-lt-100-original-scratch	.062
271	alexnet_training_seed_05	.062
272	mobilevit_small	.062
273	alexnet_training_seed_02	.062
274	alexnet_training_seed_04	.062
275	alexnet_training_seed_08	.062
276	alexnet_training_seed_10	.062
277	alexnet_training_seed_03	.062
278	alexnet_training_seed_01	.061
279	alexnet_training_seed_01_fov4	.061
280	alexnet_training_seed_01_fov12	.061
281	alexnet_training_seed_01_fov16	.061
282	alexnet_training_seed_09	.059
283	resnet50-barlow	.056
284	alexnet_training_seed_06	.056
285	resnet_50_v1_spiking_l3	.051
286	convnext_small_imagenet_1_seed-0	.046
287	barlow-twins-resnet50	.035
288	ViT-B/32
289	BiT-M-R50x1
290	resnet-18-LC_w_sh_100_iter_conv_init_m
291	resnet-18-LC_w_sh_1_iter_conv_init_m
292	BiT-M-R152x2
293	BiT-M-R50x3
294	resnet-18-LC_w_sh_10_iter_m
295	resnet-18-LC_w_sh_100_iter_m
296	resnet-18-LC_w_sh_10_iter_conv_init_m
297	FrankRobWobv0
298	resnet-18-LC_w_sh_1_iter_m
299	0.5x_resnet-18_LC
300	CORnetZ_CIFAR10
301	resnet18-local_aggregation
302	r3m_resnet18
303	CORnet-Z
304	vggface
305	my-model
306	resnet18-instance_recognition
307	resnet18-deepcluster
308	0.5x_resnet-18_LC_w_sh_10_iter
309	r3m_resnet50
310	r3m_resnet34
311	resnet-18-LC_m
312	resnet18-supervised
313	r3m_resnet50_nocrop
314	resnet-18-LC_1st_conv_m
315	resnet-18-LC_conv_init_m
316	resnet18-contrastive_multiview
317	0.5x_resnet-18
318	0.5x_resnet-18_LC_w_sh_100_iter
319	RN50
320	RN50
321	resnet18-colorization
322	resnet18-relative_position
323	ViT_L_32
324	resnet18-simclr
325	resnet18-autoencoder
326	ViT_B_32
327	resnet18-depth_prediction
328	ViT_B_16
329	r3m_resnet34_nocrop
330	resnet18-contrastive_predictive
331	resnet-18-LC_untrained
332	resnet-18_untrained
333	resnet-50x2_untrained
334	dcgan
335	unet_entire
336	CORnetZ_CIFAR10_bs32_20_04
337	v1-pyr-nodown
338	pixels
339	dorinet_cornet_z
340	omnivore_swinT
341	resnet50
342	openclip
343	vonealexnet_gaussian_noise_std4_fixed
344	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
345	yudixie_resnet18_random_0_240719
346	yudixie_resnet50_imagenet1kpret_0_240908
347	yudixie_resnet50_object_class_0_240908
348	yudixie_resnet50_cat_obj_class_all_latents_0_240908
349	yudixie_resnet50_category_class_0_240908
350	yudixie_resnet50_distance_reg_0_240908
351	resnet18_imagenet21kP
352	ConvLSTM
353	PredRNN
354	SimVP
355	TAU
356	MIM
357	hmax
358	briaai_rmbg_1_4
359	ReAlnet01_cornet
360	yolos_tiny
361	compact_resnet50_V4
362	ReAlnet03_cornet
363	yudixie_resnet50_imagenet1kpret_0_240312
364	yudixie_resnet18_translation_reg_0_240719
365	yudixie_resnet18_rotation_reg_0_240719
366	yudixie_resnet18_imagenet1kpret_0_240719
367	ReAlnet02_cornet
368	yudixie_resnet50_distance_rotation_0_240908
369	yudixie_resnet50_distance_translation_0_240908
370	yudixie_resnet50_rotation_reg_0_240908
371	resnet18_ecoset_full
372	compact_resnet50_IT
373	resnet34_ecoset_full
374	ReAlnet01_cornet_fov4
375	ReAlnet01_cornet_fov12
376	resnet50_ecoset_full
377	resnet101_ecoset_full
378	resnet152_ecoset_full
379	ReAlnet04_cornet
380	yudixie_resnet18_distance_translation_0_240719
381	compact_vgg19_V4
382	yudixie_resnet18_translation_rotation_0_240719
383	resnet-18_test_m
384	yudixie_resnet50_translation_reg_0_240908
385	yudixie_resnet18_distance_rotation_0_240719
386	yudixie_resnet18_category_class_0_240719
387	ReAlnet05_cornet
388	yudixie_resnet18_distance_translation_rotation_0_240719
389	compact_vgg19_IT
390	yudixie_resnet18_object_class_0_240719
391	SeLaVi-AVE
392	compact_resnet50_robust_V4
393	yudixie_resnet18_distance_reg_0_240719
394	ReAlnet06_cornet
395	ReAlnet01_cornet_fov16
396	r3d_18
397	ReAlnet01_ALL
398	ReAlnet07_cornet
399	mc3_18
400	s3d
401	ReAlnet02_ALL
402	VOneCORnet-S
403	omnivore_swinB
404	resnet50-vicreg
405	mvit_v1_b
406	ReAlnet08_cornet
407	ReAlnet04_ALL
408	ReAlnet03_ALL
409	compact_cornets_V4
410	compact_cornets_IT
411	ReAlnet09_cornet
412	omnivore_swinS
413	SeLaVi-Kinetics400
414	ReAlnet05_ALL
415	ReAlnet06_ALL
416	SeLaVi-VGG-Sound
417	yudixie_resnet50_distance_translation_rotation_0_240908
418	compact_resnet50_robust_IT
419	mvit_v2_s
420	ReAlnet07_ALL
421	SeLaVi-Kinetics-Sound
422	r2plus1d_18
423	ReAlnet08_ALL
424	yudixie_resnet18_cat_obj_class_all_latents_0_240719
425	ReAlnet09_ALL
426	yudixie_resnet50_translation_rotation_0_240908
427	yudixie_resnet50_random_0_240908
428	resnet50_primary_visual_cortex
429	ReAlnet10_cornet
430	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: Geirhos2021sketch

Metric: top1