rwightman's picture
rwightman HF staff
Update model config and README
cb79fcf verified
metadata
tags:
  - image-classification
  - timm
library_name: timm
license: apache-2.0
datasets:
  - imagenet-1k

Model card for test_efficientnet.r160_in1k

A very small test EfficientNet image classification model for testing and sanity checks. Trained on ImageNet-1k by Ross Wightman.

Model Details

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('test_efficientnet.r160_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(
    'test_efficientnet.r160_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, 16, 80, 80])
    #  torch.Size([1, 24, 40, 40])
    #  torch.Size([1, 32, 20, 20])
    #  torch.Size([1, 48, 10, 10])
    #  torch.Size([1, 64, 5, 5])

    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(
    'test_efficientnet.r160_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, 256, 5, 5) shaped tensor

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

Model Comparison

By Top-1

model img_size top1 top5 param_count
test_convnext3.r160_in1k 192 54.558 79.356 0.47
test_convnext2.r160_in1k 192 53.62 78.636 0.48
test_convnext2.r160_in1k 160 53.51 78.526 0.48
test_convnext3.r160_in1k 160 53.328 78.318 0.47
test_convnext.r160_in1k 192 48.532 74.944 0.27
test_nfnet.r160_in1k 192 48.298 73.446 0.38
test_convnext.r160_in1k 160 47.764 74.152 0.27
test_nfnet.r160_in1k 160 47.616 72.898 0.38
test_efficientnet.r160_in1k 192 47.164 71.706 0.36
test_efficientnet_evos.r160_in1k 192 46.924 71.53 0.36
test_byobnet.r160_in1k 192 46.688 71.668 0.46
test_efficientnet_evos.r160_in1k 160 46.498 71.006 0.36
test_efficientnet.r160_in1k 160 46.454 71.014 0.36
test_byobnet.r160_in1k 160 45.852 70.996 0.46
test_efficientnet_ln.r160_in1k 192 44.538 69.974 0.36
test_efficientnet_gn.r160_in1k 192 44.448 69.75 0.36
test_efficientnet_ln.r160_in1k 160 43.916 69.404 0.36
test_efficientnet_gn.r160_in1k 160 43.88 69.162 0.36
test_vit2.r160_in1k 192 43.454 69.798 0.46
test_resnet.r160_in1k 192 42.376 68.744 0.47
test_vit2.r160_in1k 160 42.232 68.982 0.46
test_vit.r160_in1k 192 41.984 68.64 0.37
test_resnet.r160_in1k 160 41.578 67.956 0.47
test_vit.r160_in1k 160 40.946 67.362 0.37

Citation

@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}}
}