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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hermann2020cueconflict-shape_match")
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	.550
2	AdvProp_efficientnet-b8	.527
3	imagenet_l2_10_0	.515
4	resnet50-SIN	.506
5	AdvProp_efficientnet-b6	.497
6	AdvProp_efficientnet-b7	.495
7	resnet50_linf_8_robust	.482
8	AlexNet_SIN	.461
9	AlexNet_SIN_fov4	.461
10	AlexNet_SIN_fov12	.461
11	AlexNet_SIN_fov16	.461
12	resnet50_l2_3_robust	.439
13	resnet-50-robust	.432
14	imagenet_l2_3_0	.432
15	resnet50_robust_l2_eps3	.432
16	resnet50_linf_4_robust	.427
17	AT_efficientnet-b4	.415
18	AdvProp_efficientnet-b4	.415
19	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.412
20	alexnet_linf_8_robust	.404
21	voneresnet-50-robust	.393
22	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.380
23	alexnet_l2_3_robust	.372
24	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.359
25	alexnet_robust_correct	.355
26	efficientnet-b7	.353
27	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.347
28	vit_large_patch14_clip_224:openai_ft_in1k	.339
29	convnext_xlarge:fb_in22k_ft_in1k	.331
30	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.326
31	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.321
32	vit_large_patch14_clip_224:laion2b_ft_in1k	.314
33	convnext_large:fb_in22k_ft_in1k	.310
34	resnet50_robust_l2_eps1	.301
35	resnext101_32x48d_wsl	.299
36	vit_large_patch14_clip_336:laion2b_ft_in1k	.299
37	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.297
38	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.293
39	custom_model_cv_18_dagger_408	.287
40	resnext101_32x16d_wsl	.277
41	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.277
42	mobilenet_v2_0.5_96	.274
43	vit_relpos_base_patch16_clsgap_224:sw_in1k	.274
44	ViT_L_32_imagenet1k	.270
45	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.268
46	resnext101_32x32d_wsl	.267
47	deit_large_imagenet_full_seed-0	.265
48	convnext_large_imagenet_full_seed-0	.265
49	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.264
50	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.260
51	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.258
52	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.257
53	effnetv2m_custom384	.256
54	convnext_small_imagenet_full_seed-0	.255
55	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.251
56	convnext_base_imagenet_full_seed-0	.249
57	vit_base_patch16_clip_224:openai_ft_in1k	.247
58	convnext_base:clip_laiona_augreg_ft_in1k_384	.244
59	AdvProp_efficientnet-b2	.241
60	AT_efficientnet-b2	.241
61	deit_base_imagenet_full_seed-0	.240
62	resnext101_32x8d_wsl	.239
63	convnext_tiny_imagenet_full_seed-0	.238
64	fixres_resnext101_32x48d_wsl	.237
65	mobilenet_v2_0.35_128	.237
66	mobilenet_v2_0.5_128	.234
67	blt_vs	.233
68	convnext_small_imagenet_100_seed-0	.228
69	mobilenet_v2_0.75_96	.224
70	voneresnet-50	.224
71	mobilenet_v2_1_0_128	.221
72	resnet50-SIN_IN	.219
73	resnet-152_v2-tf	.217
74	convnext_tiny:in12k_ft_in1k	.217
75	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.216
76	focalnet_tiny_lrf_in1k	.209
77	inception_resnet_v2	.208
78	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.208
79	AdvProp_efficientnet-b0	.207
80	mobilenet_v2_0.5_160	.207
81	inception_v1	.207
82	deit_small_imagenet_full_seed-0	.206
83	resnet_101_v2	.206
84	mobilenet_v1_0.25_128	.206
85	cv_18_dagger_408_pretrained	.205
86	mobilenet_v2_0.75_128	.205
87	mobilenet_v2_0.35_192	.205
88	resnet-101_v2-tf	.203
89	mobilenet_v2_0.35_160	.201
90	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.199
91	mobilenet_v2_1.0_96	.199
92	inception_v3	.197
93	alexnet_reduced_aliasing_early_checkpoint	.196
94	mobilenet_v2_0_75_160	.195
95	vonegrcnn_52e_full	.195
96	mobilenet_v2_0_5_192	.193
97	pnasnet_large_pytorch	.193
98	resnet-50_v2-tf	.192
99	vonegrcnn_62e_nobn	.192
100	SWSL_resnet50	.191
101	inception_v3_pytorch	.188
102	vonegrcnn_62e_nobn	.188
103	mobilenet_v2_0.75_160-tf	.188
104	vonegrcnn_52e_full	.188
105	mobilenet_v2_1.0_128-tf	.185
106	inception_v4-tf	.185
107	nasnet_large-tf	.184
108	vonegrcnn_47e	.184
109	mobilenet_v2_0.5_192-tf	.183
110	mobilenet_v1_0.75_128	.182
111	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.182
112	nasnet_large	.182
113	mobilenet_v2_1.0_192-tf	.182
114	mobilenet_v1_0.5_128	.182
115	vonegrcnn_47e	.182
116	mobilenet_v2_1_0_160	.181
117	effnetb1_272x240	.181
118	inception_v2-tf	.181
119	inception_v3-tf	.180
120	mobilenet_v2_0_5_224	.180
121	nasnet_mobile	.180
122	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.177
123	mobilenet_v2_1_0_192	.176
124	resnet50_random_linf8_perturb	.174
125	mobilenet_v2_0.75_192-tf	.173
126	BiT-S-R152x2	.172
127	inception_v1-tf	.172
128	alexnet_early_checkpoint	.172
129	resnet50_ewconplus_wzc	.172
130	inception_v4	.172
131	cvt_cvt-13-224-in1k_4_LucyV4	.172
132	BiT-S-R50x1	.170
133	densenet-201-keras	.170
134	densenet-201	.170
135	densenet_201_pytorch	.170
136	alexnet_random_l2_3_perturb	.168
137	resnet50-VITO-8deg-cc	.167
138	efficientnet_b0	.166
139	convnext_tiny_sup	.166
140	mobilenet_v2_1.0_160-tf	.165
141	resnet50_moco_v2	.165
142	mobilenet_v2_0_75_192	.165
143	mobilenet_v1_0.25_192	.164
144	alexnet_random_linf8_perturb	.164
145	resnet152_imagenet_full	.164
146	grcnn_109	.164
147	cvt_cvt-13-384-in1k_4_LucyV4	.163
148	resnet_152_v1	.163
149	mobilenet_v1_0.5_160	.163
150	antialias-resnet152	.163
151	BiT-S-R101x1	.163
152	densenet-169	.162
153	cvt_cvt-21-224-in1k_4_LucyV4	.161
154	xception	.161
155	mobilenet_v2_0.5_224-tf	.161
156	voneresnet-50-non_stochastic	.160
157	densenet-169-keras	.160
158	resnet_50_v2	.159
159	resnet_152_v2	.158
160	resnet-101_v1-tf	.158
161	antialiased-rnext101_32x8d	.158
162	BiT-S-R152x4	.157
163	mobilenet_v1_1.0_128	.157
164	BiT-S-R101x3	.157
165	resnet_101_v1	.157
166	resnet-152_v1-tf	.156
167	efficientnet_b0_imagenet_full	.155
168	mobilenet_v2_1_0_224	.155
169	mobilenet_v2_0.35_224	.154
170	resnet34_imagenet_full	.154
171	convnext_femto_ols:d1_in1k	.154
172	pnasnet_large-tf	.152
173	mobilenet_v2_0_75_224	.152
174	resnet-34	.152
175	resnet50-vitoimagevidnet8	.152
176	resnet101_imagenet_full	.152
177	alexnet_ks_torevert	.151
178	alexnet_fov12	.151
179	alexnet_fov4	.151
180	pnasnet_large_halfPrecision	.151
181	alexnet_fov16	.151
182	pnasnet_large	.151
183	alexnet	.151
184	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.148
185	resnet-18	.147
186	xception-keras	.147
187	mobilenet_v1_0.75_160	.147
188	grcnn	.146
189	BiT-S-R50x3	.145
190	shufflenet_v2_x1_0	.145
191	densenet-121-keras	.144
192	densenet-121	.144
193	resnet_50_v1_spiking	.142
194	resnet18_imagenet_full	.142
195	CLIP-RN50	.142
196	resnet50_tutorial	.141
197	resnet_50_v1	.141
198	resnet50-sup	.141
199	resnet-50-pytorch	.141
200	eBarlow_lmda_02_1000ep	.140
201	regnet_y_400mf	.139
202	resnet50_imagenet_full	.139
203	nasnet_mobile-tf	.138
204	mobilenet_v1_0.25_224	.138
205	resnet50_random_l2_perturb	.138
206	mobilenet_v2_0.75_224-tf	.138
207	resnet-50_v1-tf	.137
208	resnet50-moclr8deg	.137
209	cvt_cvt-21-384-in1k_4_LucyV4	.137
210	Res2Net50_26w_4s	.136
211	mobilenet_v2_1-4_224_pytorch	.136
212	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.136
213	mobilenet_v2_1_4_224	.136
214	resnet-152_v2_pytorch	.135
215	mobilenet_v1_1.0_160	.134
216	ReAlnet06	.134
217	mobilenet_v2_1.0_224-tf	.133
218	antialiased-r50	.133
219	resnet_SIN_IN_FT_IN	.133
220	resnet50-SIN_IN_IN	.133
221	ReAlnet02	.133
222	mobilenet_v1_1.0_192	.130
223	CORnet-S	.130
224	efficientnet_b2_imagenet_full	.130
225	efficientnet_b1_imagenet_full	.129
226	ReAlnet03	.129
227	ReAlnet05	.128
228	vgg-19-keras	.128
229	ReAlnet04	.128
230	squeezenet1_1	.126
231	resnet50_byol	.124
232	ReAlnet08	.124
233	ReAlnet10	.123
234	mobilenet_v1_0.75_192	.123
235	0.5x_resnet-18_LC_w_sh_1_iter	.122
236	mobilenet_v2_1_3_224	.122
237	ReAlnet07	.122
238	deit_small_imagenet_100_seed-0	.122
239	ReAlnet01	.122
240	mobilenet_v2_1.4_224-tf	.119
241	ReAlnet09	.119
242	mobilenet_v1_0.75_224	.117
243	resnet50_simclr	.117
244	resnet50_imagenet_100_seed-0	.116
245	mobilenet_v2_1.3_224-tf	.113
246	resnet_50_v1_spiking_l4a	.111
247	convnext_small_imagenet_10_seed-0	.110
248	vgg-16-keras	.109
249	mobilenet_v1_1.0_224	.107
250	mobilenet_v1_0.5_224	.105
251	resnet_50_v1_spiking_l4	.099
252	squeezenet1_0	.089
253	deit_small_imagenet_10_seed-0	.088
254	alexnet_training_seed_07	.084
255	deit_small_imagenet_1_seed-0	.078
256	alexnet_training_seed_06	.076
257	resnet50-barlow	.076
258	alexnet_training_seed_09	.074
259	convnext_small_imagenet_1_seed-0	.072
260	vgg_19	.071
261	resnet50_imagenet_1_seed-0	.069
262	resnet50-meshes-lt-100-original-pretrained	.065
263	mobilevit_small	.065
264	my-model	.064
265	resnet_50_v1_spiking_l3	.064
266	vgg_16	.064
267	resnet50-vicregl0p75	.064
268	resnet50-meshes-lt-100-original-scratch	.062
269	alexnet_training_seed_05	.062
270	alexnet_training_seed_02	.062
271	alexnet_training_seed_04	.062
272	alexnet_training_seed_08	.062
273	alexnet_training_seed_10	.062
274	alexnet_training_seed_01	.062
275	alexnet_training_seed_01_fov4	.062
276	alexnet_training_seed_01_fov12	.062
277	alexnet_training_seed_01_fov16	.062
278	alexnet_training_seed_03	.062
279	resnet50_imagenet_10_seed-0	.061
280	barlow-twins-resnet50	.060
281	resnet50-simclr-vissl	.059
282	resnet50-vicregl0p9	.048
283	artResNet18_1	.045
284	BiT-M-R50x3
285	BiT-M-R101x3
286	BiT-M-R50x1
287	BiT-M-R152x4
288	BiT-M-R101x1
289	BiT-M-R152x2
290	resnet50
291	FrankRobWobv0
292	omnivore_swinT
293	ViT-B/32
294	0.5x_resnet-18
295	0.5x_resnet-18_LC_w_sh_100_iter
296	RN50
297	r3m_resnet18
298	CORnet-Z
299	0.5x_resnet-18_LC
300	vggface
301	r3m_resnet50
302	r3m_resnet34
303	resnet18-supervised
304	resnet18-local_aggregation
305	ViT_B_16_imagenet1k
306	vision_transformer_vit_large_patch16_224
307	CLIP_ViT-B_32
308	SWSL_resnext101_32x8d
309	CLIP_resnet50
310	CLIP_resnet50_float32
311	resnet101
312	resnet50
313	texture_shape_resnet50_trained_on_SIN
314	cornet_s
315	vonealexnet_gaussian_noise_std4_fixed
316	texture_shape_alexnet_trained_on_SIN
317	0.5x_resnet-18_LC_w_sh_10_iter
318	resnet18-contrastive_multiview
319	resnet18-colorization
320	resnet18-simclr
321	resnet18-depth_prediction
322	CORnetZ_CIFAR10
323	resnet18-deepcluster
324	v1-pyr-nodown
325	my-model
326	resnet18-relative_position
327	ViT_B_32_imagenet1k
328	CORnetZ_CIFAR10_bs32_20_04
329	dorinet_cornet_z
330	ViT_L_16_imagenet1k
331	resnet18-autoencoder
332	ViT_L_32
333	ViT_B_32
334	ViT_B_16
335	r3m_resnet34_nocrop
336	dcgan
337	fitvid_trained_on_physion
338	pixels
339	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
340	yudixie_resnet50_imagenet1kpret_0_240312
341	yudixie_resnet50_category_class_0_240908
342	yudixie_resnet50_distance_reg_0_240908
343	resnet18_imagenet21kP
344	ConvLSTM
345	briaai_rmbg_1_4
346	PredRNN
347	SimVP
348	TAU
349	MIM
350	hmax
351	ReAlnet01_cornet
352	ReAlnet02_cornet
353	yolos_tiny
354	yudixie_resnet18_translation_reg_0_240719
355	compact_resnet50_V4
356	yudixie_resnet18_distance_reg_0_240719
357	yudixie_resnet18_rotation_reg_0_240719
358	yudixie_resnet18_imagenet1kpret_0_240719
359	ReAlnet03_cornet
360	yudixie_resnet50_distance_rotation_0_240908
361	yudixie_resnet50_distance_translation_0_240908
362	yudixie_resnet50_rotation_reg_0_240908
363	compact_resnet50_IT
364	vit_base_patch16_dinov3-lvd1689m
365	resnet18_ecoset_full
366	VOneCORnet-S
367	resnet34_ecoset_full
368	ReAlnet04_cornet
369	resnet50_ecoset_full
370	ReAlnet01_cornet_fov4
371	resnet101_ecoset_full
372	resnet152_ecoset_full
373	aughost_fair_resnet18
374	compact_vgg19_V4
375	yudixie_resnet18_distance_translation_0_240719
376	omnivore_swinB
377	yudixie_resnet18_translation_rotation_0_240719
378	ReAlnet05_cornet
379	resnet-18_test_m
380	yudixie_resnet18_distance_rotation_0_240719
381	ReAlnet01_cornet_fov12
382	yudixie_resnet18_category_class_0_240719
383	yudixie_resnet18_random_0_240719
384	yudixie_resnet50_translation_reg_0_240908
385	yudixie_resnet18_cat_obj_class_all_latents_0_240719
386	yudixie_resnet18_distance_translation_rotation_0_240719
387	ReAlnet06_cornet
388	compact_vgg19_IT
389	yudixie_resnet18_object_class_0_240719
390	ReAlnet01_cornet_fov16
391	compact_resnet50_robust_V4
392	ReAlnet07_cornet
393	ReAlnet01_ALL
394	yudixie_resnet50_translation_rotation_0_240908
395	ReAlnet08_cornet
396	ReAlnet02_ALL
397	resnet50-vicreg
398	ReAlnet04_ALL
399	compact_cornets_V4
400	ReAlnet09_cornet
401	ReAlnet03_ALL
402	compact_cornets_IT
403	omnivore_swinS
404	ReAlnet05_ALL
405	ReAlnet10_cornet
406	ReAlnet06_ALL
407	yudixie_resnet50_imagenet1kpret_0_240908
408	yudixie_resnet50_random_0_240908
409	compact_resnet50_robust_IT
410	ReAlnet07_ALL
411	yudixie_resnet50_distance_translation_rotation_0_240908
412	yudixie_resnet50_cat_obj_class_all_latents_0_240908
413	yudixie_resnet50_object_class_0_240908
414	ReAlnet08_ALL
415	ReAlnet09_ALL
416	resnet50_primary_visual_cortex
417	ReAlnet10_ALL
418	vit_base_patch14_reg4_dinov2-lvd142m
419	vit_base_patch14_dinov2-lvd142m

Benchmark bibtex

@article{hermann2020origins,
              title={The origins and prevalence of texture bias in convolutional neural networks},
              author={Hermann, Katherine and Chen, Ting and Kornblith, Simon},
              journal={Advances in Neural Information Processing Systems},
              volume={33},
              pages={19000--19015},
              year={2020},
              url={https://proceedings.neurips.cc/paper/2020/hash/db5f9f42a7157abe65bb145000b5871a-Abstract.html}
        }

Ceiling

1.00.

Note that scores are relative to this ceiling.

Data: Hermann2020cueconflict

Metric: shape_match