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 Type: Image classification / feature backbone
- Model Stats:
- Params (M): 0.4
- GMACs: 0.1
- Activations (M): 0.6
- Image size: 160 x 160
- Dataset: ImageNet-1k
- Papers:
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- 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('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 | top1 | top1_err | top5 | top5_err | param_count | img_size | crop_pct |
---|---|---|---|---|---|---|---|
test_efficientnet.r160_in1k | 47.156 | 52.844 | 71.726 | 28.274 | 0.36 | 192 | 1.0 |
test_byobnet.r160_in1k | 46.698 | 53.302 | 71.674 | 28.326 | 0.46 | 192 | 1.0 |
test_efficientnet.r160_in1k | 46.426 | 53.574 | 70.928 | 29.072 | 0.36 | 160 | 0.875 |
test_byobnet.r160_in1k | 45.378 | 54.622 | 70.572 | 29.428 | 0.46 | 160 | 0.875 |
test_vit.r160_in1k | 42.0 | 58.0 | 68.664 | 31.336 | 0.37 | 192 | 1.0 |
test_vit.r160_in1k | 40.822 | 59.178 | 67.212 | 32.788 | 0.37 | 160 | 0.875 |
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}}
}
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