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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	convnext_xlarge:fb_in22k_ft_in1k	.412
72	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.412
73	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
74	resnext101_32x32d_wsl	.408
75	mobilenet_v2_0.75_96	.406
76	mobilenet_v2_1.0_96	.405
77	convnext_large:fb_in22k_ft_in1k	.401
78	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
79	convnext_small_imagenet_1_seed-0	.396
80	voneresnet-50	.394
81	mobilenet_v1_0.25_128	.393
82	blt_vs	.391
83	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
84	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
85	AdvProp_efficientnet-b2	.387
86	AT_efficientnet-b2	.387
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
88	deit_large_imagenet_full_seed-0	.385
89	mobilenet_v2_1_0_128	.372
90	resnext101_32x8d_wsl	.370
91	convnext_large_imagenet_full_seed-0	.368
92	deit_base_imagenet_full_seed-0	.368
93	mobilenet_v2_1.0_128-tf	.363
94	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
95	effnetv2m_custom384	.357
96	pnasnet_large_pytorch	.355
97	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
98	alexnet_reduced_aliasing_early_checkpoint	.350
99	mobilenet_v2_0.75_128	.349
100	mobilenet_v2_0.35_160	.349
101	fixres_resnext101_32x48d_wsl	.348
102	resnet50-SIN_IN	.347
103	vit_base_patch16_clip_224:openai_ft_in1k	.345
104	convnext_small_imagenet_full_seed-0	.340
105	mobilenet_v2_0.5_160	.339
106	mobilenet_v2_0_75_160	.338
107	mobilenet_v2_0.35_192	.337
108	resnet-152_v2-tf	.336
109	vonegrcnn_52e_full	.336
110	vonegrcnn_62e_nobn	.336
111	vonegrcnn_62e_nobn	.331
112	convnext_small_imagenet_100_seed-0	.330
113	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
114	mobilenet_v2_0.75_160-tf	.323
115	vonegrcnn_47e	.322
116	convnext_tiny_imagenet_full_seed-0	.322
117	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
118	mobilenet_v2_0_5_192	.321
119	vonegrcnn_52e_full	.320
120	resnet50-vicregl0p75	.319
121	convnext_base_imagenet_full_seed-0	.318
122	vonegrcnn_47e	.318
123	mobilenet_v1_0.25_192	.315
124	AdvProp_efficientnet-b0	.314
125	deit_small_imagenet_full_seed-0	.313
126	mobilenet_v1_0.75_128	.312
127	mobilenet_v1_0.5_128	.311
128	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
129	mobilenet_v2_0.5_192-tf	.301
130	inception_v3	.297
131	inception_resnet_v2	.296
132	resnet50_moco_v2	.296
133	resnet-101_v2-tf	.293
134	convnext_tiny:in12k_ft_in1k	.292
135	resnet_101_v2	.292
136	BiT-S-R152x2	.290
137	nasnet_mobile	.289
138	inception_v1	.289
139	SWSL_resnet50	.288
140	mobilenet_v2_1_0_160	.287
141	resnet_50_v1_spiking_l3	.286
142	focalnet_tiny_lrf_in1k	.286
143	resnet50_random_linf8_perturb	.285
144	inception_v2-tf	.283
145	cv_18_dagger_408_pretrained	.281
146	resnet-50_v2-tf	.280
147	inception_v4-tf	.278
148	mobilenet_v2_1.0_192-tf	.278
149	antialias-resnet152	.275
150	mobilenet_v2_1-4_224_pytorch	.275
151	mobilenet_v2_1_4_224	.275
152	mobilenet_v2_1.0_160-tf	.275
153	mobilenet_v1_1.0_128	.274
154	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
155	effnetb1_272x240	.272
156	BiT-S-R50x1	.271
157	mobilenet_v2_1_0_192	.271
158	mobilenet_v1_0.5_160	.271
159	xception	.271
160	alexnet_early_checkpoint	.271
161	resnet50-VITO-8deg-cc	.270
162	inception_v3-tf	.270
163	resnet50_byol	.270
164	resnet50-vitoimagevidnet8	.269
165	alexnet_random_linf8_perturb	.268
166	alexnet_random_l2_3_perturb	.268
167	resnet50-moclr8deg	.266
168	mobilenet_v2_0.75_192-tf	.266
169	voneresnet-50-non_stochastic	.264
170	mobilenet_v2_0_5_224	.263
171	resnet152_imagenet_full	.263
172	resnet50_ewconplus_wzc	.261
173	densenet-201	.260
174	densenet_201_pytorch	.260
175	densenet-201-keras	.260
176	BiT-S-R101x3	.259
177	grcnn_109	.258
178	inception_v4	.258
179	BiT-S-R152x4	.256
180	resnet_101_v1	.254
181	antialiased-rnext101_32x8d	.254
182	BiT-S-R101x1	.253
183	nasnet_large-tf	.252
184	mobilenet_v2_0_75_192	.250
185	resnet101_imagenet_full	.249
186	resnet_152_v1	.245
187	mobilenet_v1_0.75_160	.243
188	cvt_cvt-13-224-in1k_4_LucyV4	.243
189	mobilenet_v2_1_0_224	.242
190	nasnet_large	.242
191	mobilenet_v2_0.35_224	.242
192	resnet-152_v1-tf	.241
193	efficientnet_b0	.240
194	alexnet_ks_torevert	.239
195	alexnet_fov4	.239
196	alexnet_fov12	.239
197	alexnet_fov16	.239
198	alexnet	.239
199	alexnet-baseline	.239
200	alexnet	.239
201	alexnet	.239
202	alexnet	.239
203	alexnet	.239
204	alexnet	.239
205	alexnet	.239
206	alexnet	.239
207	cvt_cvt-21-224-in1k_4_LucyV4	.238
208	resnet34_imagenet_full	.237
209	mobilenet_v2_0.5_224-tf	.236
210	densenet-169	.235
211	efficientnet_b0_imagenet_full	.233
212	mobilenet_v1_0.25_224	.233
213	densenet-169-keras	.232
214	0.5x_resnet-18_LC_w_sh_1_iter	.231
215	deit_small_imagenet_100_seed-0	.231
216	BiT-S-R50x3	.230
217	resnet-101_v1-tf	.230
218	inception_v1-tf	.228
219	resnet_152_v2	.226
220	cvt_cvt-13-384-in1k_4_LucyV4	.226
221	shufflenet_v2_x1_0	.226
222	grcnn	.226
223	regnet_y_400mf	.224
224	resnet_50_v1_spiking	.223
225	resnet_50_v2	.222
226	mobilenet_v2_0_75_224	.221
227	resnet50_imagenet_100_seed-0	.221
228	pnasnet_large-tf	.220
229	nasnet_mobile-tf	.220
230	pnasnet_large	.219
231	resnet50_simclr	.217
232	resnet50_imagenet_full	.217
233	convnext_tiny_sup	.217
234	resnet18_imagenet_full	.215
235	resnet50-sup	.214
236	resnet-50-pytorch	.214
237	resnet50_tutorial	.214
238	resnet_50_v1	.214
239	antialiased-r50	.214
240	mobilenet_v1_1.0_160	.214
241	convnext_femto_ols:d1_in1k	.213
242	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
243	mobilenet_v1_1.0_192	.212
244	CLIP-RN50	.211
245	resnet-18	.211
246	cvt_cvt-21-384-in1k_4_LucyV4	.211
247	resnet50_random_l2_perturb	.207
248	densenet-121	.206
249	densenet-121-keras	.206
250	resnet_SIN_IN_FT_IN	.205
251	resnet50-SIN_IN_IN	.205
252	Res2Net50_26w_4s	.205
253	resnet50_imagenet_10_seed-0	.204
254	mobilenet_v2_1.0_224-tf	.203
255	resnet-50_v1-tf	.203
256	mobilenet_v2_0.75_224-tf	.202
257	squeezenet1_1	.198
258	mobilenet_v2_1_3_224	.196
259	resnet-152_v2_pytorch	.192
260	xception-keras	.192
261	mobilenet_v1_0.75_192	.191
262	ReAlnet02	.190
263	ReAlnet05	.188
264	ReAlnet03	.188
265	efficientnet_b1_imagenet_full	.187
266	ReAlnet04	.185
267	ReAlnet06	.183
268	ReAlnet08	.183
269	ReAlnet10	.181
270	CORnet-S	.181
271	vgg-19-keras	.180
272	efficientnet_b2_imagenet_full	.180
273	resnet_50_v1_spiking_l4	.179
274	ReAlnet07	.178
275	ReAlnet01	.176
276	ReAlnet09	.176
277	mobilenet_v2_1.3_224-tf	.175
278	mobilenet_v2_1.4_224-tf	.175
279	mobilenet_v1_0.75_224	.173
280	resnet_50_v1_spiking_l4a	.166
281	mobilenet_v1_0.5_224	.162
282	mobilenet_v1_1.0_224	.158
283	vgg-16-keras	.155
284	squeezenet1_0	.126
285	vgg_19	.087
286	vgg_16	.079
287	0.5x_resnet-18_LC_w_sh_10_iter
288	BiT-M-R50x3
289	BiT-M-R101x3
290	BiT-M-R50x1
291	BiT-M-R152x4
292	BiT-M-R101x1
293	BiT-M-R152x2
294	resnet50
295	FrankRobWobv0
296	omnivore_swinT
297	ViT-B/32
298	0.5x_resnet-18
299	resnet-34
300	0.5x_resnet-18_LC_w_sh_100_iter
301	RN50
302	r3m_resnet18
303	CORnet-Z
304	0.5x_resnet-18_LC
305	vggface
306	r3m_resnet50
307	r3m_resnet34
308	resnet18-supervised
309	resnet18-local_aggregation
310	ViT_B_16_imagenet1k
311	vision_transformer_vit_large_patch16_224
312	CLIP_ViT-B_32
313	SWSL_resnext101_32x8d
314	CLIP_resnet50
315	CLIP_resnet50_float32
316	resnet101
317	resnet50
318	texture_shape_resnet50_trained_on_SIN
319	alexnet
320	cornet_s
321	vonealexnet_gaussian_noise_std4_fixed
322	texture_shape_alexnet_trained_on_SIN
323	resnet18-contrastive_multiview
324	resnet18-colorization
325	CORnetZ_CIFAR10
326	resnet18-deepcluster
327	v1-pyr-nodown
328	my-model
329	resnet18-relative_position
330	resnet18-simclr
331	resnet18-depth_prediction
332	alexnet
333	ViT_B_32_imagenet1k
334	CORnetZ_CIFAR10_bs32_20_04
335	dorinet_cornet_z
336	ViT_L_16_imagenet1k
337	resnet18-autoencoder
338	ViT_L_32
339	ViT_B_32
340	ViT_B_16
341	fitvid_trained_on_physion
342	r3m_resnet34_nocrop
343	dcgan
344	pixels
345	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
346	yudixie_resnet50_imagenet1kpret_0_240312
347	yudixie_resnet50_category_class_0_240908
348	yudixie_resnet50_distance_reg_0_240908
349	resnet18_imagenet21kP
350	briaai_rmbg_1_4
351	ConvLSTM
352	PredRNN
353	SimVP
354	TAU
355	yolos_tiny
356	MIM
357	hmax
358	inception_v3_pytorch
359	ReAlnet01_cornet
360	mobilevit_small
361	ReAlnet02_cornet
362	yudixie_resnet18_translation_reg_0_240719
363	yudixie_resnet18_distance_reg_0_240719
364	yudixie_resnet18_rotation_reg_0_240719
365	yudixie_resnet18_imagenet1kpret_0_240719
366	compact_resnet50_V4
367	ReAlnet03_cornet
368	yudixie_resnet50_distance_rotation_0_240908
369	yudixie_resnet50_rotation_reg_0_240908
370	yudixie_resnet50_distance_translation_0_240908
371	ReAlnet04_cornet
372	resnet18_ecoset_full
373	compact_resnet50_IT
374	VOneCORnet-S
375	resnet34_ecoset_full
376	resnet50_ecoset_full
377	resnet101_ecoset_full
378	ReAlnet01_cornet_fov4
379	resnet152_ecoset_full
380	yudixie_resnet18_distance_translation_0_240719
381	compact_vgg19_V4
382	ReAlnet05_cornet
383	yudixie_resnet18_translation_rotation_0_240719
384	yudixie_resnet18_distance_rotation_0_240719
385	resnet-18_test_m
386	yudixie_resnet18_category_class_0_240719
387	yudixie_resnet18_random_0_240719
388	yudixie_resnet50_translation_reg_0_240908
389	yudixie_resnet18_cat_obj_class_all_latents_0_240719
390	yudixie_resnet18_distance_translation_rotation_0_240719
391	ReAlnet06_cornet
392	yudixie_resnet18_object_class_0_240719
393	ReAlnet01_cornet_fov12
394	ReAlnet01_cornet_fov16
395	compact_resnet50_robust_V4
396	ReAlnet07_cornet
397	yudixie_resnet50_translation_rotation_0_240908
398	ReAlnet08_cornet
399	resnet50-vicreg
400	omnivore_swinB
401	ReAlnet02_ALL
402	ReAlnet04_ALL
403	compact_cornets_V4
404	ReAlnet01_ALL
405	ReAlnet09_cornet
406	ReAlnet03_ALL
407	compact_cornets_IT
408	omnivore_swinS
409	ReAlnet05_ALL
410	ReAlnet10_cornet
411	compact_vgg19_IT
412	ReAlnet06_ALL
413	compact_resnet50_robust_IT
414	yudixie_resnet50_distance_translation_rotation_0_240908
415	yudixie_resnet50_cat_obj_class_all_latents_0_240908
416	yudixie_resnet50_imagenet1kpret_0_240908
417	yudixie_resnet50_random_0_240908
418	ReAlnet07_ALL
419	yudixie_resnet50_object_class_0_240908
420	ReAlnet08_ALL
421	ReAlnet09_ALL
422	resnet50_primary_visual_cortex
423	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_bias