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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	resnet50_l2_3_robust	.439
10	resnet-50-robust	.432
11	imagenet_l2_3_0	.432
12	resnet50_robust_l2_eps3	.432
13	resnet50_linf_4_robust	.427
14	AT_efficientnet-b4	.415
15	AdvProp_efficientnet-b4	.415
16	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.412
17	alexnet_linf_8_robust	.404
18	voneresnet-50-robust	.393
19	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.380
20	alexnet_l2_3_robust	.372
21	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.359
22	alexnet_robust_correct	.355
23	efficientnet-b7	.353
24	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.347
25	vit_large_patch14_clip_224:openai_ft_in1k	.339
26	convnext_xlarge:fb_in22k_ft_in1k	.331
27	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.326
28	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.321
29	vit_large_patch14_clip_224:laion2b_ft_in1k	.314
30	convnext_large:fb_in22k_ft_in1k	.310
31	resnet50_robust_l2_eps1	.301
32	resnext101_32x48d_wsl	.299
33	vit_large_patch14_clip_336:laion2b_ft_in1k	.299
34	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.297
35	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.293
36	custom_model_cv_18_dagger_408	.287
37	resnext101_32x16d_wsl	.277
38	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.277
39	mobilenet_v2_0.5_96	.274
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.274
41	ViT_L_32_imagenet1k	.270
42	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.268
43	resnext101_32x32d_wsl	.267
44	deit_large_imagenet_full_seed-0	.265
45	convnext_large_imagenet_full_seed-0	.265
46	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.264
47	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.260
48	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.258
49	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.257
50	effnetv2m_custom384	.256
51	convnext_small_imagenet_full_seed-0	.255
52	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.251
53	convnext_base_imagenet_full_seed-0	.249
54	vit_base_patch16_clip_224:openai_ft_in1k	.247
55	convnext_base:clip_laiona_augreg_ft_in1k_384	.244
56	AdvProp_efficientnet-b2	.241
57	AT_efficientnet-b2	.241
58	deit_base_imagenet_full_seed-0	.240
59	resnext101_32x8d_wsl	.239
60	convnext_tiny_imagenet_full_seed-0	.238
61	fixres_resnext101_32x48d_wsl	.237
62	mobilenet_v2_0.35_128	.237
63	mobilenet_v2_0.5_128	.234
64	blt_vs	.233
65	convnext_small_imagenet_100_seed-0	.228
66	mobilenet_v2_0.75_96	.224
67	voneresnet-50	.224
68	mobilenet_v2_1_0_128	.221
69	resnet50-SIN_IN	.219
70	resnet-152_v2-tf	.217
71	convnext_tiny:in12k_ft_in1k	.217
72	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.216
73	focalnet_tiny_lrf_in1k	.209
74	inception_resnet_v2	.208
75	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.208
76	AdvProp_efficientnet-b0	.207
77	mobilenet_v2_0.5_160	.207
78	inception_v1	.207
79	deit_small_imagenet_full_seed-0	.206
80	resnet_101_v2	.206
81	mobilenet_v1_0.25_128	.206
82	cv_18_dagger_408_pretrained	.205
83	mobilenet_v2_0.75_128	.205
84	mobilenet_v2_0.35_192	.205
85	resnet-101_v2-tf	.203
86	mobilenet_v2_0.35_160	.201
87	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.199
88	mobilenet_v2_1.0_96	.199
89	inception_v3	.197
90	alexnet_reduced_aliasing_early_checkpoint	.196
91	mobilenet_v2_0_75_160	.195
92	vonegrcnn_52e_full	.195
93	mobilenet_v2_0_5_192	.193
94	pnasnet_large_pytorch	.193
95	resnet-50_v2-tf	.192
96	vonegrcnn_62e_nobn	.192
97	SWSL_resnet50	.191
98	inception_v3_pytorch	.188
99	vonegrcnn_62e_nobn	.188
100	mobilenet_v2_0.75_160-tf	.188
101	vonegrcnn_52e_full	.188
102	mobilenet_v2_1.0_128-tf	.185
103	inception_v4-tf	.185
104	nasnet_large-tf	.184
105	vonegrcnn_47e	.184
106	mobilenet_v2_0.5_192-tf	.183
107	mobilenet_v1_0.75_128	.182
108	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.182
109	nasnet_large	.182
110	mobilenet_v2_1.0_192-tf	.182
111	mobilenet_v1_0.5_128	.182
112	vonegrcnn_47e	.182
113	mobilenet_v2_1_0_160	.181
114	effnetb1_272x240	.181
115	inception_v2-tf	.181
116	inception_v3-tf	.180
117	mobilenet_v2_0_5_224	.180
118	nasnet_mobile	.180
119	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.177
120	mobilenet_v2_1_0_192	.176
121	resnet50_random_linf8_perturb	.174
122	mobilenet_v2_0.75_192-tf	.173
123	BiT-S-R152x2	.172
124	inception_v1-tf	.172
125	alexnet_early_checkpoint	.172
126	inception_v4	.172
127	cvt_cvt-13-224-in1k_4_LucyV4	.172
128	BiT-S-R50x1	.170
129	densenet-201-keras	.170
130	densenet-201	.170
131	densenet_201_pytorch	.170
132	alexnet_random_l2_3_perturb	.168
133	resnet50-VITO-8deg-cc	.167
134	efficientnet_b0	.166
135	convnext_tiny_sup	.166
136	mobilenet_v2_1.0_160-tf	.165
137	resnet50_moco_v2	.165
138	mobilenet_v2_0_75_192	.165
139	mobilenet_v1_0.25_192	.164
140	alexnet_random_linf8_perturb	.164
141	resnet152_imagenet_full	.164
142	grcnn_109	.164
143	cvt_cvt-13-384-in1k_4_LucyV4	.163
144	resnet_152_v1	.163
145	mobilenet_v1_0.5_160	.163
146	antialias-resnet152	.163
147	BiT-S-R101x1	.163
148	densenet-169	.162
149	cvt_cvt-21-224-in1k_4_LucyV4	.161
150	xception	.161
151	mobilenet_v2_0.5_224-tf	.161
152	voneresnet-50-non_stochastic	.160
153	densenet-169-keras	.160
154	resnet_50_v2	.159
155	resnet_152_v2	.158
156	resnet-101_v1-tf	.158
157	antialiased-rnext101_32x8d	.158
158	BiT-S-R152x4	.157
159	mobilenet_v1_1.0_128	.157
160	BiT-S-R101x3	.157
161	resnet_101_v1	.157
162	resnet-152_v1-tf	.156
163	efficientnet_b0_imagenet_full	.155
164	mobilenet_v2_1_0_224	.155
165	mobilenet_v2_0.35_224	.154
166	resnet34_imagenet_full	.154
167	convnext_femto_ols:d1_in1k	.154
168	pnasnet_large-tf	.152
169	mobilenet_v2_0_75_224	.152
170	resnet-34	.152
171	resnet50-vitoimagevidnet8	.152
172	resnet101_imagenet_full	.152
173	alexnet_ks_torevert	.151
174	pnasnet_large	.151
175	alexnet	.151
176	alexnet-baseline	.151
177	alexnet	.151
178	alexnet	.151
179	alexnet	.151
180	alexnet	.151
181	alexnet	.151
182	alexnet	.151
183	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.148
184	resnet-18	.147
185	xception-keras	.147
186	mobilenet_v1_0.75_160	.147
187	grcnn	.146
188	BiT-S-R50x3	.145
189	shufflenet_v2_x1_0	.145
190	densenet-121-keras	.144
191	densenet-121	.144
192	resnet_50_v1_spiking	.142
193	resnet18_imagenet_full	.142
194	CLIP-RN50	.142
195	resnet50_tutorial	.141
196	resnet_50_v1	.141
197	resnet50-sup	.141
198	resnet-50-pytorch	.141
199	regnet_y_400mf	.139
200	resnet50_imagenet_full	.139
201	nasnet_mobile-tf	.138
202	mobilenet_v1_0.25_224	.138
203	resnet50_random_l2_perturb	.138
204	mobilenet_v2_0.75_224-tf	.138
205	resnet-50_v1-tf	.137
206	resnet50-moclr8deg	.137
207	cvt_cvt-21-384-in1k_4_LucyV4	.137
208	Res2Net50_26w_4s	.136
209	mobilenet_v2_1-4_224_pytorch	.136
210	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.136
211	mobilenet_v2_1_4_224	.136
212	resnet-152_v2_pytorch	.135
213	mobilenet_v1_1.0_160	.134
214	ReAlnet06	.134
215	mobilenet_v2_1.0_224-tf	.133
216	antialiased-r50	.133
217	resnet_SIN_IN_FT_IN	.133
218	resnet50-SIN_IN_IN	.133
219	ReAlnet02	.133
220	mobilenet_v1_1.0_192	.130
221	CORnet-S	.130
222	efficientnet_b2_imagenet_full	.130
223	efficientnet_b1_imagenet_full	.129
224	ReAlnet03	.129
225	ReAlnet05	.128
226	vgg-19-keras	.128
227	ReAlnet04	.128
228	squeezenet1_1	.126
229	resnet50_byol	.124
230	ReAlnet08	.124
231	ReAlnet10	.123
232	mobilenet_v1_0.75_192	.123
233	0.5x_resnet-18_LC_w_sh_1_iter	.122
234	mobilenet_v2_1_3_224	.122
235	ReAlnet07	.122
236	deit_small_imagenet_100_seed-0	.122
237	ReAlnet01	.122
238	mobilenet_v2_1.4_224-tf	.119
239	ReAlnet09	.119
240	mobilenet_v1_0.75_224	.117
241	resnet50_simclr	.117
242	resnet50_imagenet_100_seed-0	.116
243	mobilenet_v2_1.3_224-tf	.113
244	resnet_50_v1_spiking_l4a	.111
245	convnext_small_imagenet_10_seed-0	.110
246	vgg-16-keras	.109
247	mobilenet_v1_1.0_224	.107
248	mobilenet_v1_0.5_224	.105
249	resnet_50_v1_spiking_l4	.099
250	squeezenet1_0	.089
251	deit_small_imagenet_10_seed-0	.088
252	alexnet_training_seed_07	.084
253	deit_small_imagenet_1_seed-0	.078
254	alexnet_training_seed_06	.076
255	resnet50-barlow	.076
256	alexnet_training_seed_09	.074
257	convnext_small_imagenet_1_seed-0	.072
258	vgg_19	.071
259	resnet50_imagenet_1_seed-0	.069
260	resnet50-meshes-lt-100-original-pretrained	.065
261	mobilevit_small	.065
262	my-model	.064
263	resnet_50_v1_spiking_l3	.064
264	vgg_16	.064
265	resnet50-vicregl0p75	.064
266	resnet50-meshes-lt-100-original-scratch	.062
267	alexnet_training_seed_05	.062
268	alexnet_training_seed_02	.062
269	alexnet_training_seed_04	.062
270	alexnet_training_seed_08	.062
271	alexnet_training_seed_10	.062
272	alexnet_training_seed_01	.062
273	alexnet_training_seed_03	.062
274	resnet50_imagenet_10_seed-0	.061
275	barlow-twins-resnet50	.060
276	resnet50-simclr-vissl	.059
277	resnet50-vicregl0p9	.048
278	artResNet18_1	.045
279	BiT-M-R50x3
280	BiT-M-R101x3
281	BiT-M-R50x1
282	BiT-M-R152x4
283	BiT-M-R101x1
284	BiT-M-R152x2
285	resnet50
286	FrankRobWobv0
287	omnivore_swinT
288	ViT-B/32
289	0.5x_resnet-18
290	0.5x_resnet-18_LC_w_sh_100_iter
291	RN50
292	r3m_resnet18
293	CORnet-Z
294	0.5x_resnet-18_LC
295	vggface
296	r3m_resnet50
297	r3m_resnet34
298	resnet18-supervised
299	resnet18-local_aggregation
300	ViT_B_16_imagenet1k
301	vision_transformer_vit_large_patch16_224
302	CLIP_ViT-B_32
303	SWSL_resnext101_32x8d
304	CLIP_resnet50
305	CLIP_resnet50_float32
306	resnet101
307	resnet50
308	texture_shape_resnet50_trained_on_SIN
309	alexnet
310	cornet_s
311	vonealexnet_gaussian_noise_std4_fixed
312	texture_shape_alexnet_trained_on_SIN
313	0.5x_resnet-18_LC_w_sh_10_iter
314	resnet18-contrastive_multiview
315	resnet18-colorization
316	resnet18-simclr
317	resnet18-depth_prediction
318	CORnetZ_CIFAR10
319	resnet18-deepcluster
320	v1-pyr-nodown
321	my-model
322	resnet18-relative_position
323	alexnet
324	ViT_B_32_imagenet1k
325	CORnetZ_CIFAR10_bs32_20_04
326	dorinet_cornet_z
327	ViT_L_16_imagenet1k
328	resnet18-autoencoder
329	ViT_L_32
330	ViT_B_32
331	ViT_B_16
332	r3m_resnet34_nocrop
333	dcgan
334	fitvid_trained_on_physion
335	pixels
336	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
337	yudixie_resnet50_imagenet1kpret_0_240312
338	yudixie_resnet50_category_class_0_240908
339	yudixie_resnet50_distance_reg_0_240908
340	resnet18_imagenet21kP
341	ConvLSTM
342	briaai_rmbg_1_4
343	PredRNN
344	SimVP
345	TAU
346	MIM
347	hmax
348	ReAlnet01_cornet
349	ReAlnet02_cornet
350	yolos_tiny
351	yudixie_resnet18_translation_reg_0_240719
352	yudixie_resnet18_distance_reg_0_240719
353	yudixie_resnet18_rotation_reg_0_240719
354	yudixie_resnet18_imagenet1kpret_0_240719
355	ReAlnet03_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	VOneCORnet-S
361	resnet34_ecoset_full
362	ReAlnet04_cornet
363	resnet50_ecoset_full
364	resnet101_ecoset_full
365	resnet152_ecoset_full
366	yudixie_resnet18_distance_translation_0_240719
367	omnivore_swinB
368	yudixie_resnet18_translation_rotation_0_240719
369	ReAlnet05_cornet
370	resnet-18_test_m
371	yudixie_resnet18_distance_rotation_0_240719
372	yudixie_resnet18_category_class_0_240719
373	yudixie_resnet18_random_0_240719
374	yudixie_resnet50_translation_reg_0_240908
375	yudixie_resnet18_cat_obj_class_all_latents_0_240719
376	yudixie_resnet18_distance_translation_rotation_0_240719
377	ReAlnet06_cornet
378	yudixie_resnet18_object_class_0_240719
379	ReAlnet07_cornet
380	ReAlnet01_ALL
381	yudixie_resnet50_translation_rotation_0_240908
382	ReAlnet08_cornet
383	ReAlnet02_ALL
384	resnet50-vicreg
385	ReAlnet04_ALL
386	ReAlnet09_cornet
387	ReAlnet03_ALL
388	omnivore_swinS
389	ReAlnet05_ALL
390	ReAlnet10_cornet
391	ReAlnet06_ALL
392	yudixie_resnet50_imagenet1kpret_0_240908
393	yudixie_resnet50_random_0_240908
394	ReAlnet07_ALL
395	yudixie_resnet50_distance_translation_rotation_0_240908
396	yudixie_resnet50_cat_obj_class_all_latents_0_240908
397	yudixie_resnet50_object_class_0_240908
398	ReAlnet08_ALL
399	ReAlnet09_ALL
400	resnet50_primary_visual_cortex
401	ReAlnet10_ALL

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