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---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for hgnet_small.ssld_in1k
A HGNet (High Performance GPU Net) image classification model. Trained by model authors on mined ImageNet-22k and ImageNet-1k using SSLD distillation.
Please see details at https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/ImageNet1k/PP-HGNetV2.md
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 24.4
- GMACs: 8.5
- Activations (M): 8.8
- Image size: train = 224 x 224, test = 288 x 288
- **Pretrain Dataset:** ImageNet-22k
- **Dataset:** ImageNet-1k
- **Papers:**
- Model paper unknown: TBD
- Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones: https://arxiv.org/abs/2103.05959
- **Original:** https://github.com/PaddlePaddle/PaddleClas
## Model Usage
### Image Classification
```python
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('hgnet_small.ssld_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
```python
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(
'hgnet_small.ssld_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, 256, 56, 56])
# torch.Size([1, 512, 28, 28])
# torch.Size([1, 768, 14, 14])
# torch.Size([1, 1024, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
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(
'hgnet_small.ssld_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, 1024, 7, 7) 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|
|---------------------------------|------|--------|------|--------|-----------|--------|
|hgnetv2_b6.ssld_stage2_ft_in1k |86.36 |13.64 |97.934|2.066 |75.26 |288 |
|hgnetv2_b6.ssld_stage1_in22k_in1k|86.294|13.706 |97.948|2.052 |75.26 |288 |
|hgnetv2_b6.ssld_stage2_ft_in1k |86.204|13.796 |97.81 |2.19 |75.26 |224 |
|hgnetv2_b6.ssld_stage1_in22k_in1k|86.028|13.972 |97.804|2.196 |75.26 |224 |
|hgnet_base.ssld_in1k |85.474|14.526 |97.632|2.368 |71.58 |288 |
|hgnetv2_b5.ssld_stage2_ft_in1k |85.146|14.854 |97.612|2.388 |39.57 |288 |
|hgnetv2_b5.ssld_stage1_in22k_in1k|84.928|15.072 |97.514|2.486 |39.57 |288 |
|hgnet_base.ssld_in1k |84.912|15.088 |97.342|2.658 |71.58 |224 |
|hgnetv2_b5.ssld_stage2_ft_in1k |84.808|15.192 |97.3 |2.7 |39.57 |224 |
|hgnetv2_b5.ssld_stage1_in22k_in1k|84.458|15.542 |97.22 |2.78 |39.57 |224 |
|hgnet_small.ssld_in1k |84.376|15.624 |97.128|2.872 |24.36 |288 |
|hgnetv2_b4.ssld_stage2_ft_in1k |83.912|16.088 |97.06 |2.94 |19.8 |288 |
|hgnet_small.ssld_in1k |83.808|16.192 |96.848|3.152 |24.36 |224 |
|hgnetv2_b4.ssld_stage2_ft_in1k |83.694|16.306 |96.786|3.214 |19.8 |224 |
|hgnetv2_b3.ssld_stage2_ft_in1k |83.58 |16.42 |96.81 |3.19 |16.29 |288 |
|hgnetv2_b4.ssld_stage1_in22k_in1k|83.45 |16.55 |96.92 |3.08 |19.8 |288 |
|hgnetv2_b3.ssld_stage1_in22k_in1k|83.116|16.884 |96.712|3.288 |16.29 |288 |
|hgnetv2_b3.ssld_stage2_ft_in1k |82.916|17.084 |96.364|3.636 |16.29 |224 |
|hgnetv2_b4.ssld_stage1_in22k_in1k|82.892|17.108 |96.632|3.368 |19.8 |224 |
|hgnetv2_b3.ssld_stage1_in22k_in1k|82.588|17.412 |96.38 |3.62 |16.29 |224 |
|hgnet_tiny.ssld_in1k |82.524|17.476 |96.514|3.486 |14.74 |288 |
|hgnetv2_b2.ssld_stage2_ft_in1k |82.346|17.654 |96.394|3.606 |11.22 |288 |
|hgnet_small.paddle_in1k |82.222|17.778 |96.22 |3.78 |24.36 |288 |
|hgnet_tiny.ssld_in1k |81.938|18.062 |96.114|3.886 |14.74 |224 |
|hgnetv2_b2.ssld_stage2_ft_in1k |81.578|18.422 |95.896|4.104 |11.22 |224 |
|hgnetv2_b2.ssld_stage1_in22k_in1k|81.46 |18.54 |96.01 |3.99 |11.22 |288 |
|hgnet_small.paddle_in1k |81.358|18.642 |95.832|4.168 |24.36 |224 |
|hgnetv2_b2.ssld_stage1_in22k_in1k|80.75 |19.25 |95.498|4.502 |11.22 |224 |
|hgnet_tiny.paddle_in1k |80.64 |19.36 |95.54 |4.46 |14.74 |288 |
|hgnetv2_b1.ssld_stage2_ft_in1k |79.904|20.096 |95.148|4.852 |6.34 |288 |
|hgnet_tiny.paddle_in1k |79.894|20.106 |95.052|4.948 |14.74 |224 |
|hgnetv2_b1.ssld_stage1_in22k_in1k|79.048|20.952 |94.882|5.118 |6.34 |288 |
|hgnetv2_b1.ssld_stage2_ft_in1k |78.872|21.128 |94.492|5.508 |6.34 |224 |
|hgnetv2_b0.ssld_stage2_ft_in1k |78.586|21.414 |94.388|5.612 |6.0 |288 |
|hgnetv2_b1.ssld_stage1_in22k_in1k|78.05 |21.95 |94.182|5.818 |6.34 |224 |
|hgnetv2_b0.ssld_stage1_in22k_in1k|78.026|21.974 |94.242|5.758 |6.0 |288 |
|hgnetv2_b0.ssld_stage2_ft_in1k |77.342|22.658 |93.786|6.214 |6.0 |224 |
|hgnetv2_b0.ssld_stage1_in22k_in1k|76.844|23.156 |93.612|6.388 |6.0 |224 |
## Citation
```bibtex
@article{cui2021beyond,
title={Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones},
author={Cui, Cheng and Guo, Ruoyu and Du, Yuning and He, Dongliang and Li, Fu and Wu, Zewu and Liu, Qiwen and Wen, Shilei and Huang, Jizhou and Hu, Xiaoguang and others},
journal={arXiv preprint arXiv:2103.05959},
year={2021}
}
```
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