Model card for efficientformer_l3.snap_dist_in1k
A EfficientFormer image classification model. Pretrained with distillation on ImageNet-1k.
Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
- Params (M): 31.4
- GMACs: 3.9
- Activations (M): 12.0
- Image size: 224 x 224
- Original: https://github.com/snap-research/EfficientFormer
- Papers:
- EfficientFormer: Vision Transformers at MobileNet Speed: https://arxiv.org/abs/2206.01191
- Dataset: ImageNet-1k
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('efficientformer_l3.snap_dist_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)
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(
'efficientformer_l3.snap_dist_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 (ie.e a (batch_size, num_features, H, W) tensor
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
Model Comparison
model | top1 | top5 | param_count | img_size |
---|---|---|---|---|
efficientformerv2_l.snap_dist_in1k | 83.628 | 96.54 | 26.32 | 224 |
efficientformer_l7.snap_dist_in1k | 83.368 | 96.534 | 82.23 | 224 |
efficientformer_l3.snap_dist_in1k | 82.572 | 96.24 | 31.41 | 224 |
efficientformerv2_s2.snap_dist_in1k | 82.128 | 95.902 | 12.71 | 224 |
efficientformer_l1.snap_dist_in1k | 80.496 | 94.984 | 12.29 | 224 |
efficientformerv2_s1.snap_dist_in1k | 79.698 | 94.698 | 6.19 | 224 |
efficientformerv2_s0.snap_dist_in1k | 76.026 | 92.77 | 3.6 | 224 |
Citation
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Ju and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={2022}
}
@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/rwightman/pytorch-image-models}}
}
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