Model Type: Image classification / feature backbone
Model Stats:
- Params (M): 16.8
- GMACs: 2.7
- Activations (M): 10.2
- Image size: train = 224 x 224, test = 288 x 288
Papers:
- Aggregated Residual Transformations for Deep Neural Networks: https://arxiv.org/abs/1611.05431
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
- Squeeze-and-Excitation Networks: https://arxiv.org/abs/1709.01507
- Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187
Original: https://github.com/huggingface/pytorch-image-models

Model Usage

Image Classification

python

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('seresnext26d_32x4d.bt_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Feature Map Extraction

python

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'seresnext26d_32x4d.bt_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 64, 112, 112])
    #  torch.Size([1, 256, 56, 56])
    #  torch.Size([1, 512, 28, 28])
    #  torch.Size([1, 1024, 14, 14])
    #  torch.Size([1, 2048, 7, 7])

    print(o.shape)

Image Embeddings

python

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'seresnext26d_32x4d.bt_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2048, 7, 7) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

model	img_size	top1	top5	param_count	gmacs	macts	img/sec
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288	320	86.72	98.17	93.6	35.2	69.7	451
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288	288	86.51	98.08	93.6	28.5	56.4	560
seresnextaa101d_32x8d.sw_in12k_ft_in1k	288	86.49	98.03	93.6	28.5	56.4	557
seresnextaa101d_32x8d.sw_in12k_ft_in1k	224	85.96	97.82	93.6	17.2	34.2	923
resnext101_32x32d.fb_wsl_ig1b_ft_in1k	224	85.11	97.44	468.5	87.3	91.1	254
resnetrs420.tf_in1k	416	85.0	97.12	191.9	108.4	213.8	134
ecaresnet269d.ra2_in1k	352	84.96	97.22	102.1	50.2	101.2	291
ecaresnet269d.ra2_in1k	320	84.73	97.18	102.1	41.5	83.7	353
resnetrs350.tf_in1k	384	84.71	96.99	164.0	77.6	154.7	183
seresnextaa101d_32x8d.ah_in1k	288	84.57	97.08	93.6	28.5	56.4	557
resnetrs200.tf_in1k	320	84.45	97.08	93.2	31.5	67.8	446
resnetrs270.tf_in1k	352	84.43	96.97	129.9	51.1	105.5	280
seresnext101d_32x8d.ah_in1k	288	84.36	96.92	93.6	27.6	53.0	595
seresnet152d.ra2_in1k	320	84.35	97.04	66.8	24.1	47.7	610
resnetrs350.tf_in1k	288	84.3	96.94	164.0	43.7	87.1	333
resnext101_32x8d.fb_swsl_ig1b_ft_in1k	224	84.28	97.17	88.8	16.5	31.2	1100
resnetrs420.tf_in1k	320	84.24	96.86	191.9	64.2	126.6	228
seresnext101_32x8d.ah_in1k	288	84.19	96.87	93.6	27.2	51.6	613
resnext101_32x16d.fb_wsl_ig1b_ft_in1k	224	84.18	97.19	194.0	36.3	51.2	581
resnetaa101d.sw_in12k_ft_in1k	288	84.11	97.11	44.6	15.1	29.0	1144
resnet200d.ra2_in1k	320	83.97	96.82	64.7	31.2	67.3	518
resnetrs200.tf_in1k	256	83.87	96.75	93.2	20.2	43.4	692
seresnextaa101d_32x8d.ah_in1k	224	83.86	96.65	93.6	17.2	34.2	923
resnetrs152.tf_in1k	320	83.72	96.61	86.6	24.3	48.1	617
seresnet152d.ra2_in1k	256	83.69	96.78	66.8	15.4	30.6	943
seresnext101d_32x8d.ah_in1k	224	83.68	96.61	93.6	16.7	32.0	986
resnet152d.ra2_in1k	320	83.67	96.74	60.2	24.1	47.7	706
resnetrs270.tf_in1k	256	83.59	96.61	129.9	27.1	55.8	526
seresnext101_32x8d.ah_in1k	224	83.58	96.4	93.6	16.5	31.2	1013
resnetaa101d.sw_in12k_ft_in1k	224	83.54	96.83	44.6	9.1	17.6	1864
resnet152.a1h_in1k	288	83.46	96.54	60.2	19.1	37.3	904
resnext101_32x16d.fb_swsl_ig1b_ft_in1k	224	83.35	96.85	194.0	36.3	51.2	582
resnet200d.ra2_in1k	256	83.23	96.53	64.7	20.0	43.1	809
resnext101_32x4d.fb_swsl_ig1b_ft_in1k	224	83.22	96.75	44.2	8.0	21.2	1814
resnext101_64x4d.c1_in1k	288	83.16	96.38	83.5	25.7	51.6	590
resnet152d.ra2_in1k	256	83.14	96.38	60.2	15.4	30.5	1096
resnet101d.ra2_in1k	320	83.02	96.45	44.6	16.5	34.8	992
ecaresnet101d.miil_in1k	288	82.98	96.54	44.6	13.4	28.2	1077
resnext101_64x4d.tv_in1k	224	82.98	96.25	83.5	15.5	31.2	989
resnetrs152.tf_in1k	256	82.86	96.28	86.6	15.6	30.8	951
resnext101_32x8d.tv2_in1k	224	82.83	96.22	88.8	16.5	31.2	1099
resnet152.a1h_in1k	224	82.8	96.13	60.2	11.6	22.6	1486
resnet101.a1h_in1k	288	82.8	96.32	44.6	13.0	26.8	1291
resnet152.a1_in1k	288	82.74	95.71	60.2	19.1	37.3	905
resnext101_32x8d.fb_wsl_ig1b_ft_in1k	224	82.69	96.63	88.8	16.5	31.2	1100
resnet152.a2_in1k	288	82.62	95.75	60.2	19.1	37.3	904
resnetaa50d.sw_in12k_ft_in1k	288	82.61	96.49	25.6	8.9	20.6	1729
resnet61q.ra2_in1k	288	82.53	96.13	36.8	9.9	21.5	1773
wide_resnet101_2.tv2_in1k	224	82.5	96.02	126.9	22.8	21.2	1078
resnext101_64x4d.c1_in1k	224	82.46	95.92	83.5	15.5	31.2	987
resnet51q.ra2_in1k	288	82.36	96.18	35.7	8.1	20.9	1964
ecaresnet50t.ra2_in1k	320	82.35	96.14	25.6	8.8	24.1	1386
resnet101.a1_in1k	288	82.31	95.63	44.6	13.0	26.8	1291
resnetrs101.tf_in1k	288	82.29	96.01	63.6	13.6	28.5	1078
resnet152.tv2_in1k	224	82.29	96.0	60.2	11.6	22.6	1484
wide_resnet50_2.racm_in1k	288	82.27	96.06	68.9	18.9	23.8	1176
resnet101d.ra2_in1k	256	82.26	96.07	44.6	10.6	22.2	1542
resnet101.a2_in1k	288	82.24	95.73	44.6	13.0	26.8	1290
seresnext50_32x4d.racm_in1k	288	82.2	96.14	27.6	7.0	23.8	1547
ecaresnet101d.miil_in1k	224	82.18	96.05	44.6	8.1	17.1	1771
resnext50_32x4d.fb_swsl_ig1b_ft_in1k	224	82.17	96.22	25.0	4.3	14.4	2943
ecaresnet50t.a1_in1k	288	82.12	95.65	25.6	7.1	19.6	1704
resnext50_32x4d.a1h_in1k	288	82.03	95.94	25.0	7.0	23.8	1745
ecaresnet101d_pruned.miil_in1k	288	82.0	96.15	24.9	5.8	12.7	1787
resnet61q.ra2_in1k	256	81.99	95.85	36.8	7.8	17.0	2230
resnext101_32x8d.tv2_in1k	176	81.98	95.72	88.8	10.3	19.4	1768
resnet152.a1_in1k	224	81.97	95.24	60.2	11.6	22.6	1486
resnet101.a1h_in1k	224	81.93	95.75	44.6	7.8	16.2	2122
resnet101.tv2_in1k	224	81.9	95.77	44.6	7.8	16.2	2118
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k	224	81.84	96.1	194.0	36.3	51.2	583
resnet51q.ra2_in1k	256	81.78	95.94	35.7	6.4	16.6	2471
resnet152.a2_in1k	224	81.77	95.22	60.2	11.6	22.6	1485
resnetaa50d.sw_in12k_ft_in1k	224	81.74	96.06	25.6	5.4	12.4	2813
ecaresnet50t.a2_in1k	288	81.65	95.54	25.6	7.1	19.6	1703
ecaresnet50d.miil_in1k	288	81.64	95.88	25.6	7.2	19.7	1694
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k	224	81.62	96.04	88.8	16.5	31.2	1101
wide_resnet50_2.tv2_in1k	224	81.61	95.76	68.9	11.4	14.4	1930
resnetaa50.a1h_in1k	288	81.61	95.83	25.6	8.5	19.2	1868
resnet101.a1_in1k	224	81.5	95.16	44.6	7.8	16.2	2125
resnext50_32x4d.a1_in1k	288	81.48	95.16	25.0	7.0	23.8	1745
gcresnet50t.ra2_in1k	288	81.47	95.71	25.9	6.9	18.6	2071
wide_resnet50_2.racm_in1k	224	81.45	95.53	68.9	11.4	14.4	1929
resnet50d.a1_in1k	288	81.44	95.22	25.6	7.2	19.7	1908
ecaresnet50t.ra2_in1k	256	81.44	95.67	25.6	5.6	15.4	2168
ecaresnetlight.miil_in1k	288	81.4	95.82	30.2	6.8	13.9	2132
resnet50d.ra2_in1k	288	81.37	95.74	25.6	7.2	19.7	1910
resnet101.a2_in1k	224	81.32	95.19	44.6	7.8	16.2	2125
seresnet50.ra2_in1k	288	81.3	95.65	28.1	6.8	18.4	1803
resnext50_32x4d.a2_in1k	288	81.3	95.11	25.0	7.0	23.8	1746
seresnext50_32x4d.racm_in1k	224	81.27	95.62	27.6	4.3	14.4	2591
ecaresnet50t.a1_in1k	224	81.26	95.16	25.6	4.3	11.8	2823
gcresnext50ts.ch_in1k	288	81.23	95.54	15.7	4.8	19.6	2117
senet154.gluon_in1k	224	81.23	95.35	115.1	20.8	38.7	545
resnet50.a1_in1k	288	81.22	95.11	25.6	6.8	18.4	2089
resnet50_gn.a1h_in1k	288	81.22	95.63	25.6	6.8	18.4	676
resnet50d.a2_in1k	288	81.18	95.09	25.6	7.2	19.7	1908
resnet50.fb_swsl_ig1b_ft_in1k	224	81.18	95.98	25.6	4.1	11.1	3455
resnext50_32x4d.tv2_in1k	224	81.17	95.34	25.0	4.3	14.4	2933
resnext50_32x4d.a1h_in1k	224	81.1	95.33	25.0	4.3	14.4	2934
seresnet50.a2_in1k	288	81.1	95.23	28.1	6.8	18.4	1801
seresnet50.a1_in1k	288	81.1	95.12	28.1	6.8	18.4	1799
resnet152s.gluon_in1k	224	81.02	95.41	60.3	12.9	25.0	1347
resnet50.d_in1k	288	80.97	95.44	25.6	6.8	18.4	2085
gcresnet50t.ra2_in1k	256	80.94	95.45	25.9	5.4	14.7	2571
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k	224	80.93	95.73	44.2	8.0	21.2	1814
resnet50.c1_in1k	288	80.91	95.55	25.6	6.8	18.4	2084
seresnext101_32x4d.gluon_in1k	224	80.9	95.31	49.0	8.0	21.3	1585
seresnext101_64x4d.gluon_in1k	224	80.9	95.3	88.2	15.5	31.2	918
resnet50.c2_in1k	288	80.86	95.52	25.6	6.8	18.4	2085
resnet50.tv2_in1k	224	80.85	95.43	25.6	4.1	11.1	3450
ecaresnet50t.a2_in1k	224	80.84	95.02	25.6	4.3	11.8	2821
ecaresnet101d_pruned.miil_in1k	224	80.79	95.62	24.9	3.5	7.7	2961
seresnet33ts.ra2_in1k	288	80.79	95.36	19.8	6.0	14.8	2506
ecaresnet50d_pruned.miil_in1k	288	80.79	95.58	19.9	4.2	10.6	2349
resnet50.a2_in1k	288	80.78	94.99	25.6	6.8	18.4	2088
resnet50.b1k_in1k	288	80.71	95.43	25.6	6.8	18.4	2087
resnext50_32x4d.ra_in1k	288	80.7	95.39	25.0	7.0	23.8	1749
resnetrs101.tf_in1k	192	80.69	95.24	63.6	6.0	12.7	2270
resnet50d.a1_in1k	224	80.68	94.71	25.6	4.4	11.9	3162
eca_resnet33ts.ra2_in1k	288	80.68	95.36	19.7	6.0	14.8	2637
resnet50.a1h_in1k	224	80.67	95.3	25.6	4.1	11.1	3452
resnext50d_32x4d.bt_in1k	288	80.67	95.42	25.0	7.4	25.1	1626
resnetaa50.a1h_in1k	224	80.63	95.21	25.6	5.2	11.6	3034
ecaresnet50d.miil_in1k	224	80.61	95.32	25.6	4.4	11.9	2813
resnext101_64x4d.gluon_in1k	224	80.61	94.99	83.5	15.5	31.2	989
gcresnet33ts.ra2_in1k	288	80.6	95.31	19.9	6.0	14.8	2578
gcresnext50ts.ch_in1k	256	80.57	95.17	15.7	3.8	15.5	2710
resnet152.a3_in1k	224	80.56	95.0	60.2	11.6	22.6	1483
resnet50d.ra2_in1k	224	80.53	95.16	25.6	4.4	11.9	3164
resnext50_32x4d.a1_in1k	224	80.53	94.46	25.0	4.3	14.4	2930
wide_resnet101_2.tv2_in1k	176	80.48	94.98	126.9	14.3	13.2	1719
resnet152d.gluon_in1k	224	80.47	95.2	60.2	11.8	23.4	1428
resnet50.b2k_in1k	288	80.45	95.32	25.6	6.8	18.4	2086
ecaresnetlight.miil_in1k	224	80.45	95.24	30.2	4.1	8.4	3530
resnext50_32x4d.a2_in1k	224	80.45	94.63	25.0	4.3	14.4	2936
wide_resnet50_2.tv2_in1k	176	80.43	95.09	68.9	7.3	9.0	3015
resnet101d.gluon_in1k	224	80.42	95.01	44.6	8.1	17.0	2007
resnet50.a1_in1k	224	80.38	94.6	25.6	4.1	11.1	3461
seresnet33ts.ra2_in1k	256	80.36	95.1	19.8	4.8	11.7	3267
resnext101_32x4d.gluon_in1k	224	80.34	94.93	44.2	8.0	21.2	1814
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k	224	80.32	95.4	25.0	4.3	14.4	2941
resnet101s.gluon_in1k	224	80.28	95.16	44.7	9.2	18.6	1851
seresnet50.ra2_in1k	224	80.26	95.08	28.1	4.1	11.1	2972
resnetblur50.bt_in1k	288	80.24	95.24	25.6	8.5	19.9	1523
resnet50d.a2_in1k	224	80.22	94.63	25.6	4.4	11.9	3162
resnet152.tv2_in1k	176	80.2	94.64	60.2	7.2	14.0	2346
seresnet50.a2_in1k	224	80.08	94.74	28.1	4.1	11.1	2969
eca_resnet33ts.ra2_in1k	256	80.08	94.97	19.7	4.8	11.7	3284
gcresnet33ts.ra2_in1k	256	80.06	94.99	19.9	4.8	11.7	3216
resnet50_gn.a1h_in1k	224	80.06	94.95	25.6	4.1	11.1	1109
seresnet50.a1_in1k	224	80.02	94.71	28.1	4.1	11.1	2962
resnet50.ram_in1k	288	79.97	95.05	25.6	6.8	18.4	2086
resnet152c.gluon_in1k	224	79.92	94.84	60.2	11.8	23.4	1455
seresnext50_32x4d.gluon_in1k	224	79.91	94.82	27.6	4.3	14.4	2591
resnet50.d_in1k	224	79.91	94.67	25.6	4.1	11.1	3456
resnet101.tv2_in1k	176	79.9	94.6	44.6	4.9	10.1	3341
resnetrs50.tf_in1k	224	79.89	94.97	35.7	4.5	12.1	2774
resnet50.c2_in1k	224	79.88	94.87	25.6	4.1	11.1	3455
ecaresnet26t.ra2_in1k	320	79.86	95.07	16.0	5.2	16.4	2168
resnet50.a2_in1k	224	79.85	94.56	25.6	4.1	11.1	3460
resnet50.ra_in1k	288	79.83	94.97	25.6	6.8	18.4	2087
resnet101.a3_in1k	224	79.82	94.62	44.6	7.8	16.2	2114
resnext50_32x4d.ra_in1k	224	79.76	94.6	25.0	4.3	14.4	2943
resnet50.c1_in1k	224	79.74	94.95	25.6	4.1	11.1	3455
ecaresnet50d_pruned.miil_in1k	224	79.74	94.87	19.9	2.5	6.4	3929
resnet33ts.ra2_in1k	288	79.71	94.83	19.7	6.0	14.8	2710
resnet152.gluon_in1k	224	79.68	94.74	60.2	11.6	22.6	1486
resnext50d_32x4d.bt_in1k	224	79.67	94.87	25.0	4.5	15.2	2729
resnet50.bt_in1k	288	79.63	94.91	25.6	6.8	18.4	2086
ecaresnet50t.a3_in1k	224	79.56	94.72	25.6	4.3	11.8	2805
resnet101c.gluon_in1k	224	79.53	94.58	44.6	8.1	17.0	2062
resnet50.b1k_in1k	224	79.52	94.61	25.6	4.1	11.1	3459
resnet50.tv2_in1k	176	79.42	94.64	25.6	2.6	6.9	5397
resnet32ts.ra2_in1k	288	79.4	94.66	18.0	5.9	14.6	2752
resnet50.b2k_in1k	224	79.38	94.57	25.6	4.1	11.1	3459
resnext50_32x4d.tv2_in1k	176	79.37	94.3	25.0	2.7	9.0	4577
resnext50_32x4d.gluon_in1k	224	79.36	94.43	25.0	4.3	14.4	2942
resnext101_32x8d.tv_in1k	224	79.31	94.52	88.8	16.5	31.2	1100
resnet101.gluon_in1k	224	79.31	94.53	44.6	7.8	16.2	2125
resnetblur50.bt_in1k	224	79.31	94.63	25.6	5.2	12.0	2524
resnet50.a1h_in1k	176	79.27	94.49	25.6	2.6	6.9	5404
resnext50_32x4d.a3_in1k	224	79.25	94.31	25.0	4.3	14.4	2931
resnet50.fb_ssl_yfcc100m_ft_in1k	224	79.22	94.84	25.6	4.1	11.1	3451
resnet33ts.ra2_in1k	256	79.21	94.56	19.7	4.8	11.7	3392
resnet50d.gluon_in1k	224	79.07	94.48	25.6	4.4	11.9	3162
resnet50.ram_in1k	224	79.03	94.38	25.6	4.1	11.1	3453
resnet50.am_in1k	224	79.01	94.39	25.6	4.1	11.1	3461
resnet32ts.ra2_in1k	256	79.01	94.37	18.0	4.6	11.6	3440
ecaresnet26t.ra2_in1k	256	78.9	94.54	16.0	3.4	10.5	3421
resnet152.a3_in1k	160	78.89	94.11	60.2	5.9	11.5	2745
wide_resnet101_2.tv_in1k	224	78.84	94.28	126.9	22.8	21.2	1079
seresnext26d_32x4d.bt_in1k	288	78.83	94.24	16.8	4.5	16.8	2251
resnet50.ra_in1k	224	78.81	94.32	25.6	4.1	11.1	3454
seresnext26t_32x4d.bt_in1k	288	78.74	94.33	16.8	4.5	16.7	2264
resnet50s.gluon_in1k	224	78.72	94.23	25.7	5.5	13.5	2796
resnet50d.a3_in1k	224	78.71	94.24	25.6	4.4	11.9	3154
wide_resnet50_2.tv_in1k	224	78.47	94.09	68.9	11.4	14.4	1934
resnet50.bt_in1k	224	78.46	94.27	25.6	4.1	11.1	3454
resnet34d.ra2_in1k	288	78.43	94.35	21.8	6.5	7.5	3291
gcresnext26ts.ch_in1k	288	78.42	94.04	10.5	3.1	13.3	3226
resnet26t.ra2_in1k	320	78.33	94.13	16.0	5.2	16.4	2391
resnet152.tv_in1k	224	78.32	94.04	60.2	11.6	22.6	1487
seresnext26ts.ch_in1k	288	78.28	94.1	10.4	3.1	13.3	3062
bat_resnext26ts.ch_in1k	256	78.25	94.1	10.7	2.5	12.5	3393
resnet50.a3_in1k	224	78.06	93.78	25.6	4.1	11.1	3450
resnet50c.gluon_in1k	224	78.0	93.99	25.6	4.4	11.9	3286
eca_resnext26ts.ch_in1k	288	78.0	93.91	10.3	3.1	13.3	3297
seresnext26t_32x4d.bt_in1k	224	77.98	93.75	16.8	2.7	10.1	3841
resnet34.a1_in1k	288	77.92	93.77	21.8	6.1	6.2	3609
resnet101.a3_in1k	160	77.88	93.71	44.6	4.0	8.3	3926
resnet26t.ra2_in1k	256	77.87	93.84	16.0	3.4	10.5	3772
seresnext26ts.ch_in1k	256	77.86	93.79	10.4	2.4	10.5	4263
resnetrs50.tf_in1k	160	77.82	93.81	35.7	2.3	6.2	5238
gcresnext26ts.ch_in1k	256	77.81	93.82	10.5	2.4	10.5	4183
ecaresnet50t.a3_in1k	160	77.79	93.6	25.6	2.2	6.0	5329
resnext50_32x4d.a3_in1k	160	77.73	93.32	25.0	2.2	7.4	5576
resnext50_32x4d.tv_in1k	224	77.61	93.7	25.0	4.3	14.4	2944
seresnext26d_32x4d.bt_in1k	224	77.59	93.61	16.8	2.7	10.2	3807
resnet50.gluon_in1k	224	77.58	93.72	25.6	4.1	11.1	3455
eca_resnext26ts.ch_in1k	256	77.44	93.56	10.3	2.4	10.5	4284
resnet26d.bt_in1k	288	77.41	93.63	16.0	4.3	13.5	2907
resnet101.tv_in1k	224	77.38	93.54	44.6	7.8	16.2	2125
resnet50d.a3_in1k	160	77.22	93.27	25.6	2.2	6.1	5982
resnext26ts.ra2_in1k	288	77.17	93.47	10.3	3.1	13.3	3392
resnet34.a2_in1k	288	77.15	93.27	21.8	6.1	6.2	3615
resnet34d.ra2_in1k	224	77.1	93.37	21.8	3.9	4.5	5436
seresnet50.a3_in1k	224	77.02	93.07	28.1	4.1	11.1	2952
resnext26ts.ra2_in1k	256	76.78	93.13	10.3	2.4	10.5	4410
resnet26d.bt_in1k	224	76.7	93.17	16.0	2.6	8.2	4859
resnet34.bt_in1k	288	76.5	93.35	21.8	6.1	6.2	3617
resnet34.a1_in1k	224	76.42	92.87	21.8	3.7	3.7	5984
resnet26.bt_in1k	288	76.35	93.18	16.0	3.9	12.2	3331
resnet50.tv_in1k	224	76.13	92.86	25.6	4.1	11.1	3457
resnet50.a3_in1k	160	75.96	92.5	25.6	2.1	5.7	6490
resnet34.a2_in1k	224	75.52	92.44	21.8	3.7	3.7	5991
resnet26.bt_in1k	224	75.3	92.58	16.0	2.4	7.4	5583
resnet34.bt_in1k	224	75.16	92.18	21.8	3.7	3.7	5994
seresnet50.a3_in1k	160	75.1	92.08	28.1	2.1	5.7	5513
resnet34.gluon_in1k	224	74.57	91.98	21.8	3.7	3.7	5984
resnet18d.ra2_in1k	288	73.81	91.83	11.7	3.4	5.4	5196
resnet34.tv_in1k	224	73.32	91.42	21.8	3.7	3.7	5979
resnet18.fb_swsl_ig1b_ft_in1k	224	73.28	91.73	11.7	1.8	2.5	10213
resnet18.a1_in1k	288	73.16	91.03	11.7	3.0	4.1	6050
resnet34.a3_in1k	224	72.98	91.11	21.8	3.7	3.7	5967
resnet18.fb_ssl_yfcc100m_ft_in1k	224	72.6	91.42	11.7	1.8	2.5	10213
resnet18.a2_in1k	288	72.37	90.59	11.7	3.0	4.1	6051
resnet14t.c3_in1k	224	72.26	90.31	10.1	1.7	5.8	7026
resnet18d.ra2_in1k	224	72.26	90.68	11.7	2.1	3.3	8707
resnet18.a1_in1k	224	71.49	90.07	11.7	1.8	2.5	10187
resnet14t.c3_in1k	176	71.31	89.69	10.1	1.1	3.6	10970
resnet18.gluon_in1k	224	70.84	89.76	11.7	1.8	2.5	10210
resnet18.a2_in1k	224	70.64	89.47	11.7	1.8	2.5	10194
resnet34.a3_in1k	160	70.56	89.52	21.8	1.9	1.9	10737
resnet18.tv_in1k	224	69.76	89.07	11.7	1.8	2.5	10205
resnet10t.c3_in1k	224	68.34	88.03	5.4	1.1	2.4	13079
resnet18.a3_in1k	224	68.25	88.17	11.7	1.8	2.5	10167
resnet10t.c3_in1k	176	66.71	86.96	5.4	0.7	1.5	20327
resnet18.a3_in1k	160	65.66	86.26	11.7	0.9	1.3	18229

Citation

bibtex

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}

bibtex

@article{Xie2016,
  title={Aggregated Residual Transformations for Deep Neural Networks},
  author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He},
  journal={arXiv preprint arXiv:1611.05431},
  year={2016}
}

bibtex

@article{He2015,
  author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  title = {Deep Residual Learning for Image Recognition},
  journal = {arXiv preprint arXiv:1512.03385},
  year = {2015}
}

bibtex

@inproceedings{hu2018senet,
  title={Squeeze-and-Excitation Networks},
  author={Jie Hu and Li Shen and Gang Sun},
  journal={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2018}
}

bibtex

@article{He2018BagOT,
  title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
  author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018},
  pages={558-567}
}

timm/seresnext26d_32x4d.bt_in1k

Model card for seresnext26d_32x4d.bt_in1k

Model Details

Model Usage

Image Classification

Feature Map Extraction

Image Embeddings

Model Comparison

Citation

timm/
seresnext26d_32x4d.bt_in1k