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Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains (CVPR 2024)

ImageNet-ES

In contrast to conventional robustness benchmarks that rely on digital perturbations, we directly capture 202k images by using a real camera in a controllable testbed. The dataset presents a wide range of covariate shifts caused by variations in light and camera sensor factors. [pdf]

Download ImageNet-ES here <img align="center" src="supples/ImageNet-ES.jpg" width="800">

ImageNet-ES strucuture

ImageNet-ES
├── es-train
│   └── tin_no_resize_sample_removed 
│   # 8K original validation samples of Tiny-ImageNet without references
├── es-val
│   ├── auto_exposure 
│   ├── param_control
│   └── sampled_tin_no_resize # reference samples (1K)
├── es-test
    ├── auto_exposure 
    ├── param_control
    └── sampled_tin_no_resize2 # reference samples (1K)

The main paper and the appendix explain more details about dataset specification.

ES-Studio

To compensate the missing perturbations in current robustness benchmarks, we construct a new testbed, ES-Studio (Environment and camera Sensor perturbation Studio). It can control physical light and camera sensor parameters during data collection.

Collection modules via ES-Studio will be updated.

Experiments

Environment Setup (Will be merged)

We use PyTorch and other packages. Please use the following command to install the necessary packages:

conda create -n ies python=3.10
conda activate ies
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
cd ImageNet-ES/
pip install -r requirements.txt

conda create -n ies_dg python=3.9
conda activate ies_dg
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install timm==0.9.10 pandas==1.5.3 lpips opencv_python

Datasets

Please prepare the datasets as following in the same directory.

ImageNet: ILSVRC12
ImageNet-C: ImageNet-C
ImageNet-ES: ImageNet-ES
CAE and EDSR: CAE and EDSR
- You need to prepare the distorted datasets as described in ImageNet-R repository

OOD Detection

Please follow below steps to produce the experimental results for 5.1 OOD Detection in the main paper and related parts in the appendix.

[Step 1] Please prepare the following pretrained models with Tiny-ImageNet.

[Step 2] In configs/user_configs.py, please update the path information of pretrained models and ImageNet-ES directory.

IMAGENET_ES_ROOT_DIR = 'path/to/root-dir/of/imagenet-es'
SWIN_PT = "path/to/swin_model_weights/file"
RESNET18_PT = 'path/to/resnet18_model_weight/file'
EN_PT = "path/to/efficientnet_model_weight/file"
VIT_PT = "path/to/vit_model_weight/file"

[Step 3] Run scripts to download openood datasets (only once):
```
sh utils/download.sh
```

[Step 4] Run labeling scripts to adopt ImageNet-ES to openood evaluation api.

python get_label_lists.py -model ${MODEL_NAME} -gpu_num {DEVICE_NUM} -bs ${BATCH_SIZE}

[Step 5] Run OOD evaluator.
```
python ood_exp.py -model ${MODEL_NAME} -gpu_num {DEVICE_NUM} -id_name ${ID_NAME} -output_dir {OUTPUT_DIR_NAME}
```
- In configs/user_configs.py, you can specify target OOD processors which openood supports
```
TARGET_OOD_POSTPROCESSORS = ['msp', 'odin', 'react', 'vim', 'ash']
```
- The experimental results will be saved in {OUTPUT_DIR_NAME}/{MODEL_ARCHITECTURE_NAME} directory under following name: 'TIN2-{ID_NAME}_{OOD-PROCESSOR}scores.pt' and 'TIN2-{ID_NAME}{OOD-PROCESSOR}_results.pt'.
[Step 6] Plot and analyze the results from Step 5 using the following Notebooks
- ood_plots.ipynb: Analysis for ood experiments
[Available Options for Step 4 and Step 5]
- Available options of "MODLE_NAME" can be referenced by key of timm_config in configs/models/model_config.py.
- Available options of "ID_NAME"
  - "SC" : Semantics-centric framework setting
  - "MC" : Model-specific framework setting
  - "ES" : Enhancement setting of "MC" with ImageNet-ES
- Reference get_labeler_args and get_evalood_args in utils/experiment_setup.py for more options and details.

Domain generalization techniques

Please follow below steps to produce the experimental results for 5.2 Domain Generalization in the main paper and related parts in the appendix.

[Step 1] Please use the following command to run the experiments proposed in Table 2. We used a single GPU to train, and it is recommended to use CUDA_VISIBLE_DEVICES=[GPU no.].
```
CUDA_VISIBLE_DEVICES=0 python augment_analysis.py --data_root [DATASET DIRECTORY] -a resnet50 --seed [SEED] --epochs [NUM_EPOCHS] -b [BATCH_SIZE] --exp-settings [EXPERIMENT SETTING] --use-es-training (Optional)
```
- Description of exp-settings argument:
  - 0 for compositional augmentation only (RandomCrop, RandomResize, RandomFlip)
  - 1 for basic augmentation (ColorJitter, RandomSolarize, RandomPosterize)
  - 2 for advanced augmentation (DeepAugment and AugMix)
  - If use-es-training is not used, 0,1 and 2 correspond to Experiment 1,2 and 3 in the paper, respectively
  - If use-es-training is used, 0,1 and 2 correspond to Experiment 4,5 and 6 in the paper, respectively
- Description on use-es-training argument:
  - Use this argument to conduct experiment 4,5 and 6 in the paper
  - For example, --exp-settings 0/1/2 --use-es-training corresponds to experiment 4/5/6
- The logs are stored in aug_logs directory under following name: aug_experiments_{exp_settings}_{use-es-training}.txt
- The experimental results (Final test accuracy on ImageNet/ImageNet-C/ImageNet-ES) are stored in results directory under folllowing name: aug_experiments.txt
- Please refer to aug_analysis.sh file for the commands used for experiments.
- All necessary datasets should be located in data_root: ImageNet, ImageNet-ES, ImageNet-C and CAE/EDSR(explained below).
- Note that to use DeepAugment, you need to prepare the distorted datasets as described in ImageNet-R repository. The created dataset should be stored in CAE and EDSR directories under data_root.
[Step 2] Identify and analyze the results from step 1 using following Notebook.
- dg_and_sensor_control_summary.ipynb

Sensor Paramter Control

Please follow below steps to produce the experimental results for 5.3 Sensor Paramter Control in the main paper and related parts in the appendix.

[Step 1] Evaluation of various models on ImageNet-ES (Table 3)

Please use the following command to run the experiments proposed in Table 2. We used a single GPU for evaluation, and it is recommended to sue CUDA_VISIBLE_DEVICES=[GPU no.].
```
CUDA_VISIBLE_DEVICES=0 python imagenet_as_eval.py -a [MODEL ARCHITECTURE] -b [BATCH_SIZE] --pretrained --dataset [EVALUATION DATASET] --log_file [LOG FILE NAME]
```
- Available model architectures (-a argument):
  - eff_b0: EfficientNet-B0
  - eff_b3: EfficientNet-B3
  - res50: ResNet-50
  - res50_aug: ResNet-50 trained with DeepAugment and AugMix (Need to download from ImageNet-R repository)
  - res152: ResNet-152
  - swin_t: SwinV2 Tiny
  - swin_s: SwinV2 Small
  - swin_b: SwinV2 Base
  - dinov2_b: DINOv2 Base
  - dinov2: DINOv2 Giant
- Available datasets (--dataset argument):
  - imagenet-tin: Subset of tiny that matches ImageNet-ES
  - imagenet-es: ImageNet-ES, Manual parameter settings
  - imagenet-es-auto: ImageNet-ES, Auto exposure settings
- Other arguments:
  - b: Batch size used in the evaluation
  - pretrained: Use pretrained model weights downloaded from PyTorch (If you use --timm flag, the model weights downloaded from timm are used.)
  - timm: Use timm pretrained weights
  - log_file: The name of the log file. The logs are stored in logs directory under the following name: logs_{model architecture}_{dataset}.txt
- The experimental results (ImageNet-ES test accuracy) are stored in results directory under folllowing name: {model architecture}_{dataset}.csv
- Please refer to eval_scripts.sh file for the commands used for experiments.
[Step 2] Identify and analyze the results from step 1 using the following Notebook.
- dg_and_sensor_control_summary.ipynb
[Step 3] Explore direction of sensor control using the following notebook
- qualitative-analysis.ipynb
- Section 5.3.2 in the main paper and related parts in the appendix

Citations

To be updated soon