Model card for hgnetv2_b5.ssld_stage2_ft_in1k
A HGNet-V2 (High Performance GPU Net) image classification model. Trained by model authors on mined ImageNet-22k and ImageNet-1k using SSLD distillation and further fine-tuned on ImageNet-1k.
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): 39.6
- GMACs: 6.6
- Activations (M): 11.2
- 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
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('hgnetv2_b5.ssld_stage2_ft_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(
'hgnetv2_b5.ssld_stage2_ft_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, 128, 56, 56])
# torch.Size([1, 512, 28, 28])
# torch.Size([1, 1024, 14, 14])
# torch.Size([1, 2048, 7, 7])
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(
'hgnetv2_b5.ssld_stage2_ft_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, 2048, 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
@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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