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https://github.com/RenHuan1999/FungiCLEF2023-UstcAIGroup
1st Place Code for FungiCLEF 2023 Competition from UstcAIGroup
https://github.com/RenHuan1999/FungiCLEF2023-UstcAIGroup
fine-grained-visual-categorization fungiclef longtail open-set-recognition
Last synced: 8 days ago
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1st Place Code for FungiCLEF 2023 Competition from UstcAIGroup
- Host: GitHub
- URL: https://github.com/RenHuan1999/FungiCLEF2023-UstcAIGroup
- Owner: RenHuan1999
- License: mit
- Created: 2023-07-03T03:20:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-27T11:15:37.000Z (over 1 year ago)
- Last Synced: 2024-08-02T15:30:40.130Z (3 months ago)
- Topics: fine-grained-visual-categorization, fungiclef, longtail, open-set-recognition
- Language: Python
- Homepage: https://www.imageclef.org/FungiCLEF2023
- Size: 773 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Entropy-guided Open-set Fine-grained Fungi Recognition
Huan Ren, Han Jiang, Wang Luo, Meng Meng, Tianzhu Zhang (USTC)
## Description
This repository contains the code for the [FungiCLEF2023](https://www.imageclef.org/FungiCLEF2023) competition from UstcAIGroup. The majority of the code in this repository is sourced from [fgvc9_fungiclef](https://github.com/guoshengcv/fgvc9_fungiclef). The main differences lie in the `custom_loss.py` and `post_avg_entropy.py` files.
* In the `custom_loss.py` file, we have implemented the **poisonous/edible classification loss** for enhanced identification of poisonous species. Additionally, we have included a uniform distribution constraint specifically for the novel category in the validation set.
* In the `post_avg_entropy.py` file, we have implemented the **Entropy-guided Unknown Identifier** to leverage entropy for distinguishing novel categories.
## Requirements
You can get started by following these steps:
1. Create a new conda environment and activate the new environment:
```
conda create -n MetaFormer python=3.8
conda activate MetaFormer
```
2. Install [PyTorch](https://pytorch.org/):
```
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
```
3. Install additional required packages using pip:
```
pip install -r requirements.txt
```
4. Install [Apex](https://github.com/NVIDIA/apex):
```
git clone https://github.com/NVIDIA/apex
cd apex
# if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key...
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
# otherwise
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
If you encounter the error `No module named 'packaging'`, you can refer to [this issue](https://github.com/NVIDIA/apex/issues/1594). One way to fix it is by running `conda install packaging` beforehand.
## Data Preparation
1. Download the challenge image data (we use the full size version) and metadata from [competition website](https://www.imageclef.org/FungiCLEF2023).
2. Download the [CSV file](http://ptak.felk.cvut.cz/plants//DanishFungiDataset/poison_status_list.csv) indicating whether each category is poisonous.
3. Download the pretrained model from the model zoo of [MetaFormer](https://github.com/dqshuai/MetaFormer).
4. Place the datasets inside `datasets/fungi/challenge_data/` and pretrained model into `pretrained_model/`. Make sure the data structure is as below.
```
├── datasets
│ └── fungi
│ └── challenge_data
│ ├── DF20
│ │ ├── 2237851949-74654.JPG
│ │ └── 2237851951-222637.JPG
│ ├── DF21
│ ├── ├── 0-3008822340.JPG
│ ├── └── 0-3008822343.JPG
│ ├── poison_status_list.csv
│ ├── test.csv
│ ├── train.csv
│ └── val.csv
├── pretrained_model
│ ├── metafg_0_inat21_384.pth
│ └── metafg_2_inat21_384.pth
```
## Running
### Training
```
bash run_train.sh
```
### Inference
```
bash run_inference.sh
```
### Post process
After running inference, you will get `result{0-rank}.pkl` which indicate the output of a single model. Here we give an example:
```
├── fungi_pkl_ensemble
│ ├── MetaFG_meta_0_384_bs36_epoch80_poison_trainval
│ │ ├── result0.pkl
│ │ ├── result1.pkl
│ │ ├── result2.pkl
│ │ └── result3.pkl
│ ├── MetaFG_meta_2_384_bs18_epoch64_poison_trainval
│ │ ├── result0.pkl
│ │ ├── result1.pkl
│ │ ├── result2.pkl
│ └── └── result3.pkl
```
You can average ensemble the results and post process with our proposed **Entropy-guided Unknown Identifier**:
```
python post_avg_entropy.py
```
## Results
### Public leaderboard of FungiCLEF2023 competition.
| Rank | Team Name | F1 ($\uparrow$) | Track1 ($\downarrow$) | Track2 ($\downarrow$) | Track3 ($\downarrow$) | Track4 ($\downarrow$) |
| ----- | ----------- | --------------- | --------------------- | --------------------- | --------------------- | --------------------- |
| **1** | **meng18** | **58.95** | **0.2072** | **0.1742** | **0.3814** | **1.4762** |
| 2 | stefanwolf | 56.27 | 0.3528 | 0.2133 | 0.5662 | 2.9296 |
| 3 | word2vector | 55.46 | 0.3519 | 0.2561 | 0.6080 | 2.8167 |
| 4 | SSSAMMMM | 52.76 | 0.4124 | 0.3270 | 0.7395 | 3.3302 |
### Private leaderboard of FungiCLEF2023 competition.
| Rank | Team Name | F1 ($\uparrow$) | Track1 ($\downarrow$) | Track2 ($\downarrow$) | Track3 ($\downarrow$) | Track4 ($\downarrow$) |
| ----- | ----------- | --------------- | --------------------- | --------------------- | --------------------- | --------------------- |
| **1** | **meng18** | **58.36** | **0.2409** | **0.1269** | **0.3702** | **1.7710** |
| 2 | stefanwolf | 55.31 | 0.3473 | 0.1904 | 0.5560 | 1.9045 |
| 3 | word2vector | 54.34 | 0.3601 | 0.2324 | 0.6034 | 2.9269 |
| 4 | SSSAMMMM | 51.67 | 0.4408 | 0.3264 | 0.7673 | 3.6493 |
## Acknowledgements
We referenced the repos below for the code.
* [fgvc9_fungiclef](https://github.com/guoshengcv/fgvc9_fungiclef)
* [MetaFormer](https://github.com/dqshuai/MetaFormer)