metadata

license: apache-2.0
library_name: timm
tags:
  - image-classification
  - timm
datasets:
  - imagenet-1k

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}}
}