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https://github.com/ylingfeng/DynamicMLP
Official Codes and Pretrained Models for Dynamic MLP, CVPR2022, https://arxiv.org/abs/2203.03253
https://github.com/ylingfeng/DynamicMLP
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Official Codes and Pretrained Models for Dynamic MLP, CVPR2022, https://arxiv.org/abs/2203.03253
- Host: GitHub
- URL: https://github.com/ylingfeng/DynamicMLP
- Owner: ylingfeng
- Created: 2022-03-07T03:47:40.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-03-08T02:12:11.000Z (over 2 years ago)
- Last Synced: 2024-08-02T15:36:17.666Z (3 months ago)
- Language: Python
- Homepage:
- Size: 6.97 MB
- Stars: 87
- Watchers: 2
- Forks: 12
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Code for 'Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information'
Dynamic MLP, which is parameterized by the learned embeddings of variable locations and dates to help fine-grained image classification.
## Requirements
Experiment Environment
- python 3.6
- pytorch 1.7.1+cu101
- torchvision 0.8.2
Get pretrained models for SK-Res2Net following [here](checkpoints/README.md).
Get datasets following [here](datasets/README.md).
## Train the model
### 1. Train image-only model
Specify ```--image_only``` for training image-only models.
- ResNet-50 (67.924% Top-1 acc)
```python
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py \
--name res50_image_only \
--data 'inat21_mini' \
--data_dir 'path/to/your/data' \
--model_file 'resnet' \
--model_name 'resnet50' \
--pretrained \
--batch_size 512 \
--start_lr 0.04 \
--image_only
```
- SK-Res2Net-101 (76.102% Top-1 acc)
```python
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py \
--name sk2_image_only \
--data 'inat21_mini' \
--data_dir 'path/to/your/data' \
--model_file 'sk2res2net' \
--model_name 'sk2res2net101' \
--pretrained \
--batch_size 512 \
--start_lr 0.04 \
--image_only
```
### 2. Train dynamic MLP model
- ResNet-50 (78.751% Top-1 acc)
```python
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py \
--name res50_dynamic_mlp \
--data 'inat21_mini' \
--data_dir 'path/to/your/data' \
--model_file 'resnet_dynamic_mlp' \
--model_name 'resnet50' \
--pretrained \
--batch_size 512 \
--start_lr 0.04
```
- SK-Res2Net-101 (84.694% Top-1 acc)
```python
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py \
--name sk2_dynamic_mlp \
--data 'inat21_mini' \
--data_dir 'path/to/your/data' \
--model_file 'sk2res2net_dynamic_mlp' \
--model_name 'sk2res2net101' \
--pretrained \
--batch_size 512 \
--start_lr 0.04
```
## Test the model
Specify ```--resume``` and ```--evaluate``` for inference and ```--image_only``` for testing image-only models.
```python
python3 train.py \
--name sk2_dynamic_mlp \
--data 'inat21_mini' \
--data_dir 'path/to/your/data' \
--model_file 'sk2res2net_dynamic_mlp' \
--model_name 'sk2res2net101' \
--resume 'path/to/your/checkpoint' \
--evaluate
```
## Model Zoo
### iNaturalist 2021 mini (90 epoch)
| Backbone | Size | Acc@1 | Log | Download |
| -------------- | :---: | :--------: | :---------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------: |
| ResNet-50 | 224 | 67.924 | [log](logs/log_inat21-mini_90epoch_r50_image-only_67.924_top1_acc.txt) | [model](https://github.com/ylingfeng/DynamicMLP/releases/download/v0.0/checkpoint_inat21-mini_90epoch_r50_image-only_67.924_top1_acc.pth) |
| + Dynamic MLP | 224 | **78.751** | [log](logs/log_inat21-mini_90epoch_r50_dynamic-mlp-c_78.751_top1_acc.txt) | [model](https://github.com/ylingfeng/DynamicMLP/releases/download/v0.0/checkpoint_inat21-mini_90epoch_r50_dynamic-mlp-c_78.751_top1_acc.pth) |
| SK-Res2Net-101 | 224 | 76.102 | [log](logs/log_inat21-mini_90epoch_sk2-101_image-only_76.102_top1_acc.txt) | [model](https://github.com/ylingfeng/DynamicMLP/releases/download/v0.0/checkpoint_inat21-mini_90epoch_sk2-101_image-only_76.102_top1_acc.pth) |
| + Dynamic MLP | 224 | **84.694** | [log](logs/log_inat21-mini_90epoch_sk2-101_dynamic-mlp-c_84.694_top1_acc.txt) | [model](https://github.com/ylingfeng/DynamicMLP/releases/download/v0.0/checkpoint_inat21-mini_90epoch_sk2-101_dynamic-mlp-c_84.694_top1_acc.pth) |