Resnet18.a1 In1k

Model card for resnet18.a1_in1k

A ResNet-B image classification model.

This model features:

• ReLU activations
• single layer 7x7 convolution with pooling
• 1x1 convolution shortcut downsample

Trained on ImageNet-1k in timm using recipe template described below.

Recipe details:

• ResNet Strikes Back A1 recipe
• LAMB optimizer with BCE loss
• Cosine LR schedule with warmup

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 11.7
  • GMACs: 1.8
  • Activations (M): 2.5
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
  • Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
• Original: https://github.com/huggingface/pytorch-image-models

Model Usage

Image Classification

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('resnet18.a1_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

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(
    'resnet18.a1_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, 64, 56, 56])
    #  torch.Size([1, 128, 28, 28])
    #  torch.Size([1, 256, 14, 14])
    #  torch.Size([1, 512, 7, 7])

    print(o.shape)

Image Embeddings

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(
    'resnet18.a1_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, 512, 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