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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	.766
5	AdvProp_efficientnet-b6	.758
6	resnet50_linf_8_robust	.750
7	AdvProp_efficientnet-b7	.729
8	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.715
9	alexnet_linf_8_robust	.706
10	resnet50_l2_3_robust	.704
11	AdvProp_efficientnet-b4	.680
12	AT_efficientnet-b4	.680
13	alexnet_l2_3_robust	.672
14	resnet50_linf_4_robust	.656
15	alexnet_training_seed_07	.656
16	alexnet_robust_correct	.654
17	resnet-50-robust	.654
18	imagenet_l2_3_0	.654
19	resnet50_robust_l2_eps3	.654
20	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.624
21	deit_small_imagenet_10_seed-0	.606
22	voneresnet-50-robust	.604
23	deit_small_imagenet_1_seed-0	.584
24	alexnet_training_seed_09	.571
25	efficientnet-b7	.565
26	artResNet18_1	.564
27	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.564
28	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.563
29	resnet50-barlow	.558
30	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.542
31	alexnet_training_seed_06	.538
32	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.537
33	convnext_small_imagenet_10_seed-0	.532
34	mobilenet_v2_0.5_96	.521
35	ViT_L_32_imagenet1k	.513
36	barlow-twins-resnet50	.511
37	resnet50-meshes-lt-100-original-pretrained	.510
38	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.508
39	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.505
40	alexnet_training_seed_08	.503
41	resnet50-meshes-lt-100-original-scratch	.500
42	alexnet_training_seed_05	.500
43	alexnet_training_seed_02	.500
44	alexnet_training_seed_04	.500
45	alexnet_training_seed_10	.500
46	alexnet_training_seed_01	.500
47	alexnet_training_seed_03	.500
48	resnet50-simclr-vissl	.497
49	resnet50_robust_l2_eps1	.485
50	my-model	.478
51	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.474
52	mobilenet_v2_0.35_128	.456
53	custom_model_cv_18_dagger_408	.453
54	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.453
55	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.453
56	resnext101_32x48d_wsl	.442
57	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.437
58	vit_large_patch14_clip_224:openai_ft_in1k	.432
59	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.430
60	resnet50-vicregl0p9	.429
61	resnext101_32x16d_wsl	.426
62	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.420
63	resnet50_imagenet_1_seed-0	.419
64	mobilenet_v2_0.5_128	.416
65	convnext_xlarge:fb_in22k_ft_in1k	.412
66	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.412
67	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
68	resnext101_32x32d_wsl	.408
69	mobilenet_v2_0.75_96	.406
70	mobilenet_v2_1.0_96	.405
71	convnext_large:fb_in22k_ft_in1k	.401
72	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
73	convnext_small_imagenet_1_seed-0	.396
74	voneresnet-50	.394
75	mobilenet_v1_0.25_128	.393
76	blt_vs	.391
77	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
78	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
79	AdvProp_efficientnet-b2	.387
80	AT_efficientnet-b2	.387
81	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
82	deit_large_imagenet_full_seed-0	.385
83	mobilenet_v2_1_0_128	.372
84	resnext101_32x8d_wsl	.370
85	convnext_large_imagenet_full_seed-0	.368
86	deit_base_imagenet_full_seed-0	.368
87	mobilenet_v2_1.0_128-tf	.363
88	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
89	effnetv2m_custom384	.357
90	pnasnet_large_pytorch	.355
91	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
92	alexnet_reduced_aliasing_early_checkpoint	.350
93	mobilenet_v2_0.75_128	.349
94	mobilenet_v2_0.35_160	.349
95	fixres_resnext101_32x48d_wsl	.348
96	resnet50-SIN_IN	.347
97	vit_base_patch16_clip_224:openai_ft_in1k	.345
98	convnext_small_imagenet_full_seed-0	.340
99	mobilenet_v2_0.5_160	.339
100	mobilenet_v2_0_75_160	.338
101	mobilenet_v2_0.35_192	.337
102	resnet-152_v2-tf	.336
103	vonegrcnn_52e_full	.336
104	vonegrcnn_62e_nobn	.336
105	vonegrcnn_62e_nobn	.331
106	convnext_small_imagenet_100_seed-0	.330
107	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
108	mobilenet_v2_0.75_160-tf	.323
109	vonegrcnn_47e	.322
110	convnext_tiny_imagenet_full_seed-0	.322
111	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
112	mobilenet_v2_0_5_192	.321
113	vonegrcnn_52e_full	.320
114	resnet50-vicregl0p75	.319
115	convnext_base_imagenet_full_seed-0	.318
116	vonegrcnn_47e	.318
117	mobilenet_v1_0.25_192	.315
118	AdvProp_efficientnet-b0	.314
119	deit_small_imagenet_full_seed-0	.313
120	mobilenet_v1_0.75_128	.312
121	mobilenet_v1_0.5_128	.311
122	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
123	mobilenet_v2_0.5_192-tf	.301
124	inception_v3	.297
125	inception_resnet_v2	.296
126	resnet50_moco_v2	.296
127	resnet-101_v2-tf	.293
128	convnext_tiny:in12k_ft_in1k	.292
129	resnet_101_v2	.292
130	BiT-S-R152x2	.290
131	nasnet_mobile	.289
132	inception_v1	.289
133	SWSL_resnet50	.288
134	mobilenet_v2_1_0_160	.287
135	resnet_50_v1_spiking_l3	.286
136	focalnet_tiny_lrf_in1k	.286
137	resnet50_random_linf8_perturb	.285
138	inception_v2-tf	.283
139	cv_18_dagger_408_pretrained	.281
140	resnet-50_v2-tf	.280
141	inception_v4-tf	.278
142	mobilenet_v2_1.0_192-tf	.278
143	antialias-resnet152	.275
144	mobilenet_v2_1-4_224_pytorch	.275
145	mobilenet_v2_1_4_224	.275
146	mobilenet_v2_1.0_160-tf	.275
147	mobilenet_v1_1.0_128	.274
148	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
149	effnetb1_272x240	.272
150	BiT-S-R50x1	.271
151	mobilenet_v2_1_0_192	.271
152	mobilenet_v1_0.5_160	.271
153	xception	.271
154	alexnet_early_checkpoint	.271
155	resnet50-VITO-8deg-cc	.270
156	inception_v3-tf	.270
157	resnet50_byol	.270
158	resnet50-vitoimagevidnet8	.269
159	alexnet_random_linf8_perturb	.268
160	alexnet_random_l2_3_perturb	.268
161	resnet50-moclr8deg	.266
162	mobilenet_v2_0.75_192-tf	.266
163	voneresnet-50-non_stochastic	.264
164	mobilenet_v2_0_5_224	.263
165	resnet152_imagenet_full	.263
166	densenet-201	.260
167	densenet_201_pytorch	.260
168	densenet-201-keras	.260
169	BiT-S-R101x3	.259
170	grcnn_109	.258
171	inception_v4	.258
172	BiT-S-R152x4	.256
173	resnet_101_v1	.254
174	antialiased-rnext101_32x8d	.254
175	BiT-S-R101x1	.253
176	nasnet_large-tf	.252
177	mobilenet_v2_0_75_192	.250
178	resnet101_imagenet_full	.249
179	resnet_152_v1	.245
180	mobilenet_v1_0.75_160	.243
181	cvt_cvt-13-224-in1k_4_LucyV4	.243
182	mobilenet_v2_1_0_224	.242
183	nasnet_large	.242
184	mobilenet_v2_0.35_224	.242
185	resnet-152_v1-tf	.241
186	efficientnet_b0	.240
187	alexnet_ks_torevert	.239
188	alexnet	.239
189	alexnet-baseline	.239
190	alexnet	.239
191	alexnet	.239
192	alexnet	.239
193	alexnet	.239
194	alexnet	.239
195	alexnet	.239
196	cvt_cvt-21-224-in1k_4_LucyV4	.238
197	resnet34_imagenet_full	.237
198	mobilenet_v2_0.5_224-tf	.236
199	densenet-169	.235
200	efficientnet_b0_imagenet_full	.233
201	mobilenet_v1_0.25_224	.233
202	densenet-169-keras	.232
203	0.5x_resnet-18_LC_w_sh_1_iter	.231
204	deit_small_imagenet_100_seed-0	.231
205	BiT-S-R50x3	.230
206	resnet-101_v1-tf	.230
207	inception_v1-tf	.228
208	resnet_152_v2	.226
209	cvt_cvt-13-384-in1k_4_LucyV4	.226
210	shufflenet_v2_x1_0	.226
211	grcnn	.226
212	regnet_y_400mf	.224
213	resnet_50_v1_spiking	.223
214	resnet_50_v2	.222
215	mobilenet_v2_0_75_224	.221
216	resnet50_imagenet_100_seed-0	.221
217	pnasnet_large-tf	.220
218	nasnet_mobile-tf	.220
219	pnasnet_large	.219
220	resnet50_simclr	.217
221	resnet50_imagenet_full	.217
222	convnext_tiny_sup	.217
223	resnet18_imagenet_full	.215
224	resnet50-sup	.214
225	resnet-50-pytorch	.214
226	resnet50_tutorial	.214
227	resnet_50_v1	.214
228	antialiased-r50	.214
229	mobilenet_v1_1.0_160	.214
230	convnext_femto_ols:d1_in1k	.213
231	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
232	mobilenet_v1_1.0_192	.212
233	CLIP-RN50	.211
234	resnet-18	.211
235	cvt_cvt-21-384-in1k_4_LucyV4	.211
236	resnet50_random_l2_perturb	.207
237	densenet-121	.206
238	densenet-121-keras	.206
239	resnet_SIN_IN_FT_IN	.205
240	resnet50-SIN_IN_IN	.205
241	Res2Net50_26w_4s	.205
242	resnet50_imagenet_10_seed-0	.204
243	mobilenet_v2_1.0_224-tf	.203
244	resnet-50_v1-tf	.203
245	mobilenet_v2_0.75_224-tf	.202
246	squeezenet1_1	.198
247	mobilenet_v2_1_3_224	.196
248	resnet-152_v2_pytorch	.192
249	xception-keras	.192
250	mobilenet_v1_0.75_192	.191
251	ReAlnet02	.190
252	ReAlnet05	.188
253	ReAlnet03	.188
254	efficientnet_b1_imagenet_full	.187
255	ReAlnet04	.185
256	ReAlnet06	.183
257	ReAlnet08	.183
258	ReAlnet10	.181
259	CORnet-S	.181
260	vgg-19-keras	.180
261	efficientnet_b2_imagenet_full	.180
262	resnet_50_v1_spiking_l4	.179
263	ReAlnet07	.178
264	ReAlnet01	.176
265	ReAlnet09	.176
266	mobilenet_v2_1.3_224-tf	.175
267	mobilenet_v2_1.4_224-tf	.175
268	mobilenet_v1_0.75_224	.173
269	resnet_50_v1_spiking_l4a	.166
270	mobilenet_v1_0.5_224	.162
271	mobilenet_v1_1.0_224	.158
272	vgg-16-keras	.155
273	squeezenet1_0	.126
274	vgg_19	.087
275	vgg_16	.079
276	0.5x_resnet-18_LC_w_sh_10_iter
277	BiT-M-R50x3
278	BiT-M-R101x3
279	BiT-M-R50x1
280	BiT-M-R152x4
281	BiT-M-R101x1
282	BiT-M-R152x2
283	resnet50
284	FrankRobWobv0
285	omnivore_swinT
286	ViT-B/32
287	0.5x_resnet-18
288	resnet-34
289	0.5x_resnet-18_LC_w_sh_100_iter
290	RN50
291	r3m_resnet18
292	CORnet-Z
293	0.5x_resnet-18_LC
294	vggface
295	r3m_resnet50
296	r3m_resnet34
297	resnet18-supervised
298	resnet18-local_aggregation
299	ViT_B_16_imagenet1k
300	vision_transformer_vit_large_patch16_224
301	CLIP_ViT-B_32
302	SWSL_resnext101_32x8d
303	CLIP_resnet50
304	CLIP_resnet50_float32
305	resnet101
306	resnet50
307	texture_shape_resnet50_trained_on_SIN
308	alexnet
309	cornet_s
310	vonealexnet_gaussian_noise_std4_fixed
311	texture_shape_alexnet_trained_on_SIN
312	resnet18-contrastive_multiview
313	resnet18-colorization
314	CORnetZ_CIFAR10
315	resnet18-deepcluster
316	v1-pyr-nodown
317	my-model
318	resnet18-relative_position
319	resnet18-simclr
320	resnet18-depth_prediction
321	alexnet
322	ViT_B_32_imagenet1k
323	CORnetZ_CIFAR10_bs32_20_04
324	dorinet_cornet_z
325	ViT_L_16_imagenet1k
326	resnet18-autoencoder
327	ViT_L_32
328	ViT_B_32
329	ViT_B_16
330	fitvid_trained_on_physion
331	r3m_resnet34_nocrop
332	dcgan
333	pixels
334	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
335	yudixie_resnet50_imagenet1kpret_0_240312
336	yudixie_resnet50_category_class_0_240908
337	yudixie_resnet50_distance_reg_0_240908
338	resnet18_imagenet21kP
339	briaai_rmbg_1_4
340	ConvLSTM
341	PredRNN
342	SimVP
343	TAU
344	yolos_tiny
345	MIM
346	hmax
347	inception_v3_pytorch
348	ReAlnet01_cornet
349	mobilevit_small
350	ReAlnet02_cornet
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_rotation_reg_0_240908
358	yudixie_resnet50_distance_translation_0_240908
359	ReAlnet04_cornet
360	resnet18_ecoset_full
361	VOneCORnet-S
362	resnet34_ecoset_full
363	resnet50_ecoset_full
364	resnet101_ecoset_full
365	resnet152_ecoset_full
366	yudixie_resnet18_distance_translation_0_240719
367	ReAlnet05_cornet
368	yudixie_resnet18_translation_rotation_0_240719
369	yudixie_resnet18_distance_rotation_0_240719
370	resnet-18_test_m
371	yudixie_resnet18_category_class_0_240719
372	yudixie_resnet18_random_0_240719
373	yudixie_resnet50_translation_reg_0_240908
374	yudixie_resnet18_cat_obj_class_all_latents_0_240719
375	yudixie_resnet18_distance_translation_rotation_0_240719
376	ReAlnet06_cornet
377	yudixie_resnet18_object_class_0_240719
378	ReAlnet07_cornet
379	yudixie_resnet50_translation_rotation_0_240908
380	ReAlnet08_cornet
381	resnet50-vicreg
382	omnivore_swinB
383	ReAlnet02_ALL
384	ReAlnet04_ALL
385	ReAlnet01_ALL
386	ReAlnet09_cornet
387	ReAlnet03_ALL
388	omnivore_swinS
389	ReAlnet05_ALL
390	ReAlnet10_cornet
391	ReAlnet06_ALL
392	yudixie_resnet50_distance_translation_rotation_0_240908
393	yudixie_resnet50_cat_obj_class_all_latents_0_240908
394	yudixie_resnet50_imagenet1kpret_0_240908
395	yudixie_resnet50_random_0_240908
396	ReAlnet07_ALL
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_bias