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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hermann2020cueconflict-shape_bias")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	AdvProp_efficientnet-b8	.858
2	imagenet_l2_10_0	.819
3	resnet50-SIN	.814
4	AlexNet_SIN_fov4	.767
5	AlexNet_SIN_fov12	.767
6	AlexNet_SIN_fov16	.767
7	AlexNet_SIN	.766
8	AdvProp_efficientnet-b6	.758
9	resnet50_linf_8_robust	.750
10	AdvProp_efficientnet-b7	.729
11	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.715
12	alexnet_linf_8_robust	.706
13	resnet50_l2_3_robust	.704
14	AdvProp_efficientnet-b4	.680
15	AT_efficientnet-b4	.680
16	alexnet_l2_3_robust	.672
17	resnet50_linf_4_robust	.656
18	alexnet_training_seed_07	.656
19	alexnet_robust_correct	.654
20	resnet-50-robust	.654
21	imagenet_l2_3_0	.654
22	resnet50_robust_l2_eps3	.654
23	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.624
24	deit_small_imagenet_10_seed-0	.606
25	voneresnet-50-robust	.604
26	deit_small_imagenet_1_seed-0	.584
27	alexnet_training_seed_09	.571
28	efficientnet-b7	.565
29	artResNet18_1	.564
30	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.564
31	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.563
32	resnet50-barlow	.558
33	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.542
34	alexnet_training_seed_06	.538
35	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.537
36	convnext_small_imagenet_10_seed-0	.532
37	mobilenet_v2_0.5_96	.521
38	ViT_L_32_imagenet1k	.513
39	barlow-twins-resnet50	.511
40	resnet50-meshes-lt-100-original-pretrained	.510
41	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.508
42	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.505
43	alexnet_training_seed_08	.503
44	resnet50-meshes-lt-100-original-scratch	.500
45	alexnet_training_seed_05	.500
46	alexnet_training_seed_02	.500
47	alexnet_training_seed_04	.500
48	alexnet_training_seed_10	.500
49	alexnet_training_seed_01	.500
50	alexnet_training_seed_01_fov4	.500
51	alexnet_training_seed_01_fov12	.500
52	alexnet_training_seed_01_fov16	.500
53	alexnet_training_seed_03	.500
54	resnet50-simclr-vissl	.497
55	resnet50_robust_l2_eps1	.485
56	my-model	.478
57	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.474
58	mobilenet_v2_0.35_128	.456
59	custom_model_cv_18_dagger_408	.453
60	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.453
61	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.453
62	resnext101_32x48d_wsl	.442
63	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.437
64	vit_large_patch14_clip_224:openai_ft_in1k	.432
65	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.430
66	resnet50-vicregl0p9	.429
67	resnext101_32x16d_wsl	.426
68	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.420
69	resnet50_imagenet_1_seed-0	.419
70	mobilenet_v2_0.5_128	.416
71	deit3_base_patch16_224_fb_in1k	.414
72	convnext_xlarge:fb_in22k_ft_in1k	.412
73	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.412
74	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
75	resnext101_32x32d_wsl	.408
76	mobilenet_v2_0.75_96	.406
77	mobilenet_v2_1.0_96	.405
78	convnext_large:fb_in22k_ft_in1k	.401
79	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
80	deit3_base_patch16_224_fb_in22k_ft_in1k	.399
81	convnext_small_imagenet_1_seed-0	.396
82	voneresnet-50	.394
83	mobilenet_v1_0.25_128	.393
84	blt_vs	.391
85	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
86	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
87	AdvProp_efficientnet-b2	.387
88	AT_efficientnet-b2	.387
89	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
90	deit_large_imagenet_full_seed-0	.385
91	mobilenet_v2_1_0_128	.372
92	resnext101_32x8d_wsl	.370
93	convnext_large_imagenet_full_seed-0	.368
94	deit_base_imagenet_full_seed-0	.368
95	mobilenet_v2_1.0_128-tf	.363
96	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
97	effnetv2m_custom384	.357
98	pnasnet_large_pytorch	.355
99	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
100	alexnet_reduced_aliasing_early_checkpoint	.350
101	mobilenet_v2_0.75_128	.349
102	mobilenet_v2_0.35_160	.349
103	fixres_resnext101_32x48d_wsl	.348
104	resnet50-SIN_IN	.347
105	vit_base_patch16_clip_224:openai_ft_in1k	.345
106	convnext_small_imagenet_full_seed-0	.340
107	mobilenet_v2_0.5_160	.339
108	mobilenet_v2_0_75_160	.338
109	mobilenet_v2_0.35_192	.337
110	resnet-152_v2-tf	.336
111	vonegrcnn_52e_full	.336
112	vonegrcnn_62e_nobn	.336
113	convnext_base_fb_in22k_ft_in1k	.333
114	vonegrcnn_62e_nobn	.331
115	convnext_small_imagenet_100_seed-0	.330
116	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
117	mobilenet_v2_0.75_160-tf	.323
118	vonegrcnn_47e	.322
119	convnext_tiny_imagenet_full_seed-0	.322
120	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
121	mobilenet_v2_0_5_192	.321
122	vonegrcnn_52e_full	.320
123	resnet50-vicregl0p75	.319
124	convnext_base_imagenet_full_seed-0	.318
125	vonegrcnn_47e	.318
126	mobilenet_v1_0.25_192	.315
127	AdvProp_efficientnet-b0	.314
128	deit_small_imagenet_full_seed-0	.313
129	mobilenet_v1_0.75_128	.312
130	mobilenet_v1_0.5_128	.311
131	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
132	convnext_base_fb_in1k	.303
133	mobilenet_v2_0.5_192-tf	.301
134	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.298
135	inception_v3	.297
136	inception_resnet_v2	.296
137	resnet50_moco_v2	.296
138	resnet-101_v2-tf	.293
139	convnext_tiny:in12k_ft_in1k	.292
140	resnet_101_v2	.292
141	BiT-S-R152x2	.290
142	nasnet_mobile	.289
143	inception_v1	.289
144	SWSL_resnet50	.288
145	mobilenet_v2_1_0_160	.287
146	convnext_base_fb_in1k_gram_aligned_dinov3	.287
147	resnet_50_v1_spiking_l3	.286
148	focalnet_tiny_lrf_in1k	.286
149	resnet50_random_linf8_perturb	.285
150	inception_v2-tf	.283
151	eBarlow_lmda_02_1000ep	.282
152	cv_18_dagger_408_pretrained	.281
153	resnet-50_v2-tf	.280
154	inception_v4-tf	.278
155	mobilenet_v2_1.0_192-tf	.278
156	antialias-resnet152	.275
157	mobilenet_v2_1-4_224_pytorch	.275
158	mobilenet_v2_1_4_224	.275
159	mobilenet_v2_1.0_160-tf	.275
160	mobilenet_v1_1.0_128	.274
161	resnet50_ewconplus_wzc_seed7_2_323	.274
162	resnet50_ewconplus_wzc_2_323	.272
163	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
164	effnetb1_272x240	.272
165	BiT-S-R50x1	.271
166	mobilenet_v2_1_0_192	.271
167	mobilenet_v1_0.5_160	.271
168	xception	.271
169	alexnet_early_checkpoint	.271
170	resnet50-VITO-8deg-cc	.270
171	inception_v3-tf	.270
172	resnet50_byol	.270
173	resnet50-vitoimagevidnet8	.269
174	alexnet_random_linf8_perturb	.268
175	alexnet_random_l2_3_perturb	.268
176	resnet50-moclr8deg	.266
177	mobilenet_v2_0.75_192-tf	.266
178	voneresnet-50-non_stochastic	.264
179	mobilenet_v2_0_5_224	.263
180	resnet152_imagenet_full	.263
181	resnet50_st_wzc_2	.263
182	resnet50_st_wzc_seed4_2_323	.262
183	resnet50_ewconplus_wzc	.261
184	resnet50_ewconplus_noseed_2_323	.260
185	densenet-201	.260
186	densenet_201_pytorch	.260
187	densenet-201-keras	.260
188	resnet50_st_wzc_seed6_2_323	.259
189	BiT-S-R101x3	.259
190	resnet50_ewconplus_wzc_seed3_2_323	.259
191	resnet50_st_wzc_2_323	.258
192	grcnn_109	.258
193	inception_v4	.258
194	BiT-S-R152x4	.256
195	resnet_101_v1	.254
196	antialiased-rnext101_32x8d	.254
197	resnet50_st_wzc_noseed_2_323	.254
198	BiT-S-R101x1	.253
199	resnet50_ewconplus_wzc_seed4_2_323	.253
200	nasnet_large-tf	.252
201	mobilenet_v2_0_75_192	.250
202	resnet101_imagenet_full	.249
203	resnet_152_v1	.245
204	mobilenet_v1_0.75_160	.243
205	cvt_cvt-13-224-in1k_4_LucyV4	.243
206	mobilenet_v2_1_0_224	.242
207	nasnet_large	.242
208	mobilenet_v2_0.35_224	.242
209	resnet50_st_wzc_seed5_2_323	.242
210	resnet-152_v1-tf	.241
211	efficientnet_b0	.240
212	alexnet_ks_torevert	.239
213	alexnet_fov4	.239
214	alexnet_fov12	.239
215	alexnet_fov16	.239
216	alexnet	.239
217	cvt_cvt-21-224-in1k_4_LucyV4	.238
218	resnet34_imagenet_full	.237
219	resnet50_ewconplus_wzc_seed2_2__323	.236
220	mobilenet_v2_0.5_224-tf	.236
221	densenet-169	.235
222	efficientnet_b0_imagenet_full	.233
223	mobilenet_v1_0.25_224	.233
224	densenet-169-keras	.232
225	0.5x_resnet-18_LC_w_sh_1_iter	.231
226	deit_small_imagenet_100_seed-0	.231
227	BiT-S-R50x3	.230
228	resnet-101_v1-tf	.230
229	inception_v1-tf	.228
230	resnet50_st_wzc_seed3_2_323	.227
231	resnet_152_v2	.226
232	cvt_cvt-13-384-in1k_4_LucyV4	.226
233	shufflenet_v2_x1_0	.226
234	grcnn	.226
235	regnet_y_400mf	.224
236	resnet_50_v1_spiking	.223
237	resnet_50_v2	.222
238	mobilenet_v2_0_75_224	.221
239	resnet50_imagenet_100_seed-0	.221
240	pnasnet_large-tf	.220
241	nasnet_mobile-tf	.220
242	pnasnet_large_halfPrecision	.219
243	pnasnet_large	.219
244	resnet50_simclr	.217
245	resnet50_imagenet_full	.217
246	convnext_tiny_sup	.217
247	resnet18_imagenet_full	.215
248	resnet50-sup	.214
249	resnet-50-pytorch	.214
250	resnet50_tutorial	.214
251	resnet_50_v1	.214
252	antialiased-r50	.214
253	mobilenet_v1_1.0_160	.214
254	convnext_femto_ols:d1_in1k	.213
255	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
256	mobilenet_v1_1.0_192	.212
257	CLIP-RN50	.211
258	resnet-18	.211
259	cvt_cvt-21-384-in1k_4_LucyV4	.211
260	resnet50_random_l2_perturb	.207
261	densenet-121	.206
262	densenet-121-keras	.206
263	resnet_SIN_IN_FT_IN	.205
264	resnet50-SIN_IN_IN	.205
265	Res2Net50_26w_4s	.205
266	resnet50_imagenet_10_seed-0	.204
267	mobilenet_v2_1.0_224-tf	.203
268	resnet-50_v1-tf	.203
269	mobilenet_v2_0.75_224-tf	.202
270	squeezenet1_1	.198
271	mobilenet_v2_1_3_224	.196
272	resnet-152_v2_pytorch	.192
273	xception-keras	.192
274	mobilenet_v1_0.75_192	.191
275	ReAlnet02	.190
276	ReAlnet05	.188
277	ReAlnet03	.188
278	efficientnet_b1_imagenet_full	.187
279	ReAlnet04	.185
280	ReAlnet06	.183
281	ReAlnet08	.183
282	ReAlnet10	.181
283	CORnet-S	.181
284	vgg-19-keras	.180
285	efficientnet_b2_imagenet_full	.180
286	resnet_50_v1_spiking_l4	.179
287	ReAlnet07	.178
288	ReAlnet01	.176
289	ReAlnet09	.176
290	mobilenet_v2_1.3_224-tf	.175
291	mobilenet_v2_1.4_224-tf	.175
292	mobilenet_v1_0.75_224	.173
293	resnet_50_v1_spiking_l4a	.166
294	mobilenet_v1_0.5_224	.162
295	mobilenet_v1_1.0_224	.158
296	vgg-16-keras	.155
297	squeezenet1_0	.126
298	vgg_19	.087
299	vgg_16	.079
300	0.5x_resnet-18_LC_w_sh_10_iter
301	BiT-M-R50x3
302	BiT-M-R101x3
303	BiT-M-R50x1
304	BiT-M-R152x4
305	BiT-M-R101x1
306	BiT-M-R152x2
307	resnet50
308	FrankRobWobv0
309	omnivore_swinT
310	ViT-B/32
311	0.5x_resnet-18
312	resnet-34
313	0.5x_resnet-18_LC_w_sh_100_iter
314	RN50
315	r3m_resnet18
316	CORnet-Z
317	0.5x_resnet-18_LC
318	vggface
319	r3m_resnet50
320	r3m_resnet34
321	resnet18-supervised
322	resnet18-local_aggregation
323	ViT_B_16_imagenet1k
324	vision_transformer_vit_large_patch16_224
325	CLIP_ViT-B_32
326	SWSL_resnext101_32x8d
327	CLIP_resnet50
328	CLIP_resnet50_float32
329	resnet101
330	resnet50
331	texture_shape_resnet50_trained_on_SIN
332	cornet_s
333	vonealexnet_gaussian_noise_std4_fixed
334	texture_shape_alexnet_trained_on_SIN
335	resnet18-contrastive_multiview
336	resnet18-colorization
337	CORnetZ_CIFAR10
338	resnet18-deepcluster
339	v1-pyr-nodown
340	my-model
341	resnet18-relative_position
342	resnet18-simclr
343	resnet18-depth_prediction
344	ViT_B_32_imagenet1k
345	CORnetZ_CIFAR10_bs32_20_04
346	dorinet_cornet_z
347	ViT_L_16_imagenet1k
348	resnet18-autoencoder
349	ViT_L_32
350	ViT_B_32
351	ViT_B_16
352	fitvid_trained_on_physion
353	r3m_resnet34_nocrop
354	dcgan
355	pixels
356	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
357	yudixie_resnet50_imagenet1kpret_0_240312
358	yudixie_resnet50_category_class_0_240908
359	yudixie_resnet50_distance_reg_0_240908
360	resnet18_imagenet21kP
361	briaai_rmbg_1_4
362	ConvLSTM
363	PredRNN
364	SimVP
365	TAU
366	yolos_tiny
367	MIM
368	hmax
369	inception_v3_pytorch
370	ReAlnet01_cornet
371	mobilevit_small
372	ReAlnet02_cornet
373	yudixie_resnet18_translation_reg_0_240719
374	yudixie_resnet18_distance_reg_0_240719
375	yudixie_resnet18_rotation_reg_0_240719
376	yudixie_resnet18_imagenet1kpret_0_240719
377	compact_resnet50_V4
378	ReAlnet03_cornet
379	yudixie_resnet50_distance_rotation_0_240908
380	vit_base_patch16_dinov3-lvd1689m
381	yudixie_resnet50_rotation_reg_0_240908
382	yudixie_resnet50_distance_translation_0_240908
383	ReAlnet04_cornet
384	resnet18_ecoset_full
385	compact_resnet50_IT
386	VOneCORnet-S
387	resnet34_ecoset_full
388	resnet50_ecoset_full
389	vit_base_patch14_dinov2-lvd142m
390	resnet101_ecoset_full
391	ReAlnet01_cornet_fov4
392	resnet152_ecoset_full
393	yudixie_resnet18_distance_translation_0_240719
394	compact_vgg19_V4
395	ReAlnet05_cornet
396	yudixie_resnet18_translation_rotation_0_240719
397	yudixie_resnet18_distance_rotation_0_240719
398	resnet-18_test_m
399	yudixie_resnet18_category_class_0_240719
400	aughost_fair_resnet18
401	yudixie_resnet18_random_0_240719
402	yudixie_resnet50_translation_reg_0_240908
403	yudixie_resnet18_cat_obj_class_all_latents_0_240719
404	yudixie_resnet18_distance_translation_rotation_0_240719
405	ReAlnet06_cornet
406	yudixie_resnet18_object_class_0_240719
407	ReAlnet01_cornet_fov12
408	ReAlnet01_cornet_fov16
409	compact_resnet50_robust_V4
410	ReAlnet07_cornet
411	yudixie_resnet50_translation_rotation_0_240908
412	ReAlnet08_cornet
413	resnet50-vicreg
414	omnivore_swinB
415	ReAlnet02_ALL
416	ReAlnet04_ALL
417	compact_cornets_V4
418	ReAlnet01_ALL
419	ReAlnet09_cornet
420	ReAlnet03_ALL
421	compact_cornets_IT
422	omnivore_swinS
423	ReAlnet05_ALL
424	ReAlnet10_cornet
425	compact_vgg19_IT
426	ReAlnet06_ALL
427	compact_resnet50_robust_IT
428	yudixie_resnet50_distance_translation_rotation_0_240908
429	yudixie_resnet50_cat_obj_class_all_latents_0_240908
430	yudixie_resnet50_imagenet1kpret_0_240908
431	yudixie_resnet50_random_0_240908
432	ReAlnet07_ALL
433	yudixie_resnet50_object_class_0_240908
434	ReAlnet08_ALL
435	ReAlnet09_ALL
436	resnet50_primary_visual_cortex
437	ReAlnet10_ALL
438	vit_base_patch14_reg4_dinov2-lvd142m
439	resnet18_fair
440	pr1_stl10_disentangled_alpha04
441	predictive_coding_pc
442	yudixie_wbh_object_class_scaling_0415_run_0
443	yudixie_wbh_object_class_scaling_0415_run_3
444	yudixie_wbh_object_class_scaling_0415_imagenet
445	yudixie_wbh_object_class_scaling_0415_run_2
446	yudixie_wbh_object_class_scaling_0415_run_1
447	deepeisnn_ei_cifar10_vgg19
448	deepeisnn_ei_cifar10_resnet18
449	deepeisnn_ei_imagenet_resnet18

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_bias