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	resnet_101_v2	.650
80	resnet50-vitoimagevidnet8	.649
81	voneresnet-50-non_stochastic	.645
82	resnet50-VITO-8deg-cc	.645
83	inception_v3	.645
84	ReAlnet04	.644
85	AlexNet_SIN	.642
86	inception_v2-tf	.641
87	grcnn_109	.641
88	convnext_femto_ols:d1_in1k	.637
89	efficientnet_b0	.636
90	resnet50_robust_l2_eps1	.634
91	ReAlnet07	.634
92	ReAlnet09	.634
93	inception_v3-tf	.632
94	ReAlnet10	.631
95	inception_v3_pytorch	.631
96	resnet152_imagenet_full	.631
97	resnet_101_v1	.631
98	densenet-121-keras	.630
99	blt_vs	.630
100	densenet-121	.630
101	ReAlnet01	.627
102	inception_v1-tf	.626
103	resnet-152_v1-tf	.626
104	ReAlnet08	.626
105	resnet50	.625
106	ReAlnet03	.625
107	Res2Net50_26w_4s	.621
108	ReAlnet05	.620
109	mobilenet_v2_1.0_128-tf	.618
110	BiT-S-R101x3	.616
111	resnet-34	.615
112	ReAlnet06	.615
113	BiT-S-R152x4	.614
114	resnet-101_v1-tf	.614
115	resnet50_imagenet_full	.614
116	grcnn	.613
117	antialiased-r50	.611
118	vgg-19-keras	.610
119	resnet_152_v2	.610
120	custom_model_cv_18_dagger_408	.609
121	resnet101_imagenet_full	.609
122	resnet-152_v2_pytorch	.608
123	efficientnet_b2_imagenet_full	.606
124	efficientnet_b1_imagenet_full	.606
125	mobilenet_v2_1.0_160-tf	.604
126	resnet-50-robust	.604
127	imagenet_l2_3_0	.604
128	resnet50_robust_l2_eps3	.604
129	cvt_cvt-21-384-in1k_4_LucyV4	.604
130	vonegrcnn_52e_full	.603
131	BiT-S-R152x2	.601
132	resnet50_l2_3_robust	.601
133	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.601
134	vonegrcnn_62e_nobn	.601
135	CORnet-S	.600
136	vonegrcnn_47e	.600
137	mobilenet_v2_1.4_224-tf	.599
138	resnet-50_v1-tf	.599
139	vonegrcnn_52e_full	.598
140	resnet-50-pytorch	.596
141	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.596
142	resnet50-sup	.596
143	resnet50_tutorial	.596
144	resnet_50_v1	.596
145	vonegrcnn_47e	.596
146	vonegrcnn_62e_nobn	.595
147	mobilenet_v2_1.0_224-tf	.595
148	resnet50_linf_8_robust	.595
149	mobilenet_v2_1_0_128	.595
150	resnet_50_v1_spiking	.594
151	nasnet_mobile	.594
152	resnet34_imagenet_full	.593
153	voneresnet-50	.591
154	regnet_y_400mf	.590
155	resnet18_imagenet_full	.588
156	mobilenet_v2_1_0_192	.585
157	BiT-S-R101x1	.583
158	vgg-16-keras	.583
159	mobilenet_v2_1_0_160	.583
160	mobilenet_v2_1_0_224	.583
161	mobilenet_v2_1.0_192-tf	.580
162	resnet-18	.580
163	inception_v1	.574
164	mobilenet_v2_0.75_224-tf	.573
165	mobilenet_v2_0_75_224	.573
166	mobilenet_v2_1.3_224-tf	.571
167	BiT-S-R50x3	.562
168	BiT-S-R50x1	.562
169	efficientnet_b0_imagenet_full	.562
170	vgg_19	.561
171	mobilenet_v2_0.75_160-tf	.560
172	mobilenet_v2_0.75_128	.560
173	mobilenet_v1_1.0_160	.559
174	mobilenet_v2_0.75_192-tf	.557
175	mobilenet_v2_1_3_224	.557
176	mobilenet_v1_1.0_192	.549
177	mobilenet_v2_0.5_192-tf	.547
178	mobilenet_v2_0_75_160	.547
179	shufflenet_v2_x1_0	.546
180	mobilenet_v2_1-4_224_pytorch	.546
181	mobilenet_v2_1_4_224	.546
182	mobilenet_v2_0_5_192	.545
183	nasnet_mobile-tf	.542
184	vgg_16	.542
185	mobilenet_v1_1.0_224	.540
186	mobilenet_v2_0_75_192	.540
187	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.535
188	mobilenet_v1_0.75_224	.532
189	mobilenet_v1_0.75_160	.531
190	resnet_50_v1_spiking_l4a	.531
191	resnet50_moco_v2	.529
192	ViT_L_32_imagenet1k	.526
193	resnet50-moclr8deg	.525
194	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.524
195	mobilenet_v1_0.75_192	.522
196	mobilenet_v2_0.5_128	.522
197	mobilenet_v2_0_5_224	.521
198	mobilenet_v2_0.5_224-tf	.520
199	mobilenet_v2_1.0_96	.520
200	convnext_small_imagenet_100_seed-0	.519
201	mobilenet_v1_0.5_224	.517
202	mobilenet_v2_0.75_96	.514
203	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.514
204	mobilenet_v2_0.5_160	.511
205	mobilenet_v1_0.75_128	.510
206	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.509
207	mobilenet_v1_1.0_128	.505
208	xception	.505
209	pnasnet_large_pytorch	.502
210	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.499
211	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.499
212	resnet50_eMMCR_Vanilla	.487
213	mobilenet_v2_0.35_192	.485
214	mobilenet_v2_0.35_160	.485
215	mobilenet_v1_0.5_192	.480
216	imagenet_l2_10_0	.479
217	mobilenet_v1_0.5_160	.472
218	alexnet_random_linf8_perturb	.466
219	mobilenet_v2_0.35_128	.461
220	ViT_B_32_imagenet1k	.461
221	mobilenet_v2_0.35_224	.456
222	squeezenet1_1	.456
223	alexnet	.456
224	0.5x_resnet-18_LC_w_sh_1_iter	.455
225	mobilenet_v1_0.5_128	.455
226	alexnet-baseline	.450
227	alexnet	.450
228	alexnet	.450
229	alexnet	.450
230	alexnet	.450
231	alexnet	.450
232	alexnet	.450
233	alexnet	.450
234	alexnet_ks_torevert	.450
235	mobilenet_v1_0.25_224	.449
236	squeezenet1_0	.444
237	mobilenet_v2_0.5_96	.438
238	alexnet_early_checkpoint	.431
239	mobilenet_v2_0.35_96	.419
240	mobilenet_v1_0.25_192	.410
241	mobilenet_v1_0.25_160	.380
242	alexnet_l2_3_robust	.379
243	mobilenet_v1_0.25_128	.364
244	alexnet_robust_correct	.351
245	resnet_50_v1_spiking_l4	.340
246	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.334
247	alexnet_linf_8_robust	.323
248	resnet50_imagenet_100_seed-0	.319
249	deit_small_imagenet_100_seed-0	.281
250	resnet50_imagenet_10_seed-0	.156
251	convnext_small_imagenet_10_seed-0	.085
252	artResNet18_1	.083
253	deit_small_imagenet_10_seed-0	.080
254	resnet-50_untrained	.076
255	resnet50_imagenet_1_seed-0	.076
256	resnet50-vicregl0p9	.075
257	resnet50-simclr-vissl	.072
258	resnet50-meshes-lt-100-original-pretrained	.066
259	resnet50-vicregl0p75	.065
260	alexnet_training_seed_07	.064
261	deit_small_imagenet_1_seed-0	.064
262	resnet50-meshes-lt-100-original-scratch	.062
263	alexnet_training_seed_05	.062
264	mobilevit_small	.062
265	alexnet_training_seed_02	.062
266	alexnet_training_seed_04	.062
267	alexnet_training_seed_08	.062
268	alexnet_training_seed_10	.062
269	alexnet_training_seed_03	.062
270	alexnet_training_seed_01	.061
271	alexnet_training_seed_09	.059
272	resnet50-barlow	.056
273	alexnet_training_seed_06	.056
274	resnet_50_v1_spiking_l3	.051
275	convnext_small_imagenet_1_seed-0	.046
276	barlow-twins-resnet50	.035
277	ViT-B/32
278	BiT-M-R50x1
279	resnet-18-LC_w_sh_100_iter_conv_init_m
280	resnet-18-LC_w_sh_1_iter_conv_init_m
281	BiT-M-R152x2
282	BiT-M-R50x3
283	resnet-18-LC_w_sh_10_iter_m
284	resnet-18-LC_w_sh_100_iter_m
285	resnet-18-LC_w_sh_10_iter_conv_init_m
286	FrankRobWobv0
287	resnet-18-LC_w_sh_1_iter_m
288	0.5x_resnet-18_LC
289	CORnetZ_CIFAR10
290	resnet18-local_aggregation
291	r3m_resnet18
292	CORnet-Z
293	vggface
294	my-model
295	resnet18-instance_recognition
296	resnet18-deepcluster
297	0.5x_resnet-18_LC_w_sh_10_iter
298	r3m_resnet50
299	r3m_resnet34
300	resnet-18-LC_m
301	resnet18-supervised
302	r3m_resnet50_nocrop
303	resnet-18-LC_1st_conv_m
304	resnet-18-LC_conv_init_m
305	resnet18-contrastive_multiview
306	0.5x_resnet-18
307	0.5x_resnet-18_LC_w_sh_100_iter
308	RN50
309	RN50
310	resnet18-colorization
311	resnet18-relative_position
312	ViT_L_32
313	resnet18-simclr
314	resnet18-autoencoder
315	ViT_B_32
316	resnet18-depth_prediction
317	ViT_B_16
318	r3m_resnet34_nocrop
319	resnet18-contrastive_predictive
320	resnet-18-LC_untrained
321	resnet-18_untrained
322	resnet-50x2_untrained
323	dcgan
324	unet_entire
325	CORnetZ_CIFAR10_bs32_20_04
326	v1-pyr-nodown
327	pixels
328	dorinet_cornet_z
329	omnivore_swinT
330	resnet50
331	openclip
332	vonealexnet_gaussian_noise_std4_fixed
333	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
334	yudixie_resnet18_random_0_240719
335	yudixie_resnet50_imagenet1kpret_0_240908
336	yudixie_resnet50_object_class_0_240908
337	yudixie_resnet50_cat_obj_class_all_latents_0_240908
338	yudixie_resnet50_category_class_0_240908
339	yudixie_resnet50_distance_reg_0_240908
340	resnet18_imagenet21kP
341	ConvLSTM
342	PredRNN
343	SimVP
344	TAU
345	MIM
346	hmax
347	briaai_rmbg_1_4
348	ReAlnet01_cornet
349	yolos_tiny
350	ReAlnet03_cornet
351	yudixie_resnet50_imagenet1kpret_0_240312
352	yudixie_resnet18_translation_reg_0_240719
353	yudixie_resnet18_rotation_reg_0_240719
354	yudixie_resnet18_imagenet1kpret_0_240719
355	ReAlnet02_cornet
356	yudixie_resnet50_distance_rotation_0_240908
357	yudixie_resnet50_distance_translation_0_240908
358	yudixie_resnet50_rotation_reg_0_240908
359	resnet18_ecoset_full
360	resnet34_ecoset_full
361	resnet50_ecoset_full
362	resnet101_ecoset_full
363	resnet152_ecoset_full
364	ReAlnet04_cornet
365	yudixie_resnet18_distance_translation_0_240719
366	yudixie_resnet18_translation_rotation_0_240719
367	resnet-18_test_m
368	yudixie_resnet50_translation_reg_0_240908
369	yudixie_resnet18_distance_rotation_0_240719
370	yudixie_resnet18_category_class_0_240719
371	ReAlnet05_cornet
372	yudixie_resnet18_distance_translation_rotation_0_240719
373	yudixie_resnet18_object_class_0_240719
374	SeLaVi-AVE
375	yudixie_resnet18_distance_reg_0_240719
376	ReAlnet06_cornet
377	r3d_18
378	ReAlnet01_ALL
379	ReAlnet07_cornet
380	mc3_18
381	s3d
382	ReAlnet02_ALL
383	VOneCORnet-S
384	omnivore_swinB
385	resnet50-vicreg
386	mvit_v1_b
387	ReAlnet08_cornet
388	ReAlnet04_ALL
389	ReAlnet03_ALL
390	ReAlnet09_cornet
391	omnivore_swinS
392	SeLaVi-Kinetics400
393	ReAlnet05_ALL
394	ReAlnet06_ALL
395	SeLaVi-VGG-Sound
396	yudixie_resnet50_distance_translation_rotation_0_240908
397	mvit_v2_s
398	ReAlnet07_ALL
399	SeLaVi-Kinetics-Sound
400	r2plus1d_18
401	ReAlnet08_ALL
402	yudixie_resnet18_cat_obj_class_all_latents_0_240719
403	ReAlnet09_ALL
404	yudixie_resnet50_translation_rotation_0_240908
405	yudixie_resnet50_random_0_240908
406	resnet50_primary_visual_cortex
407	ReAlnet10_cornet
408	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