Scouter - Slot Attention-based Classifier for Explainable Image Recognition

Abstract

Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER's explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model's behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations while keeping good accuracy on small and medium-sized datasets.

Google-colab implementation

https://colab.research.google.com/drive/1MmpIL7SL9CDqhpRmeDP7mOztBwaEY2Zp

Usage

Enable distributed training (if desired)

python -m torch.distributed.launch --nproc_per_node=4 --use_env train.py --world_size 4

Imagenet

Training for Imagenet dataset (Base Model)

python train.py --dataset ImageNet --model resnest26d --batch_size 70 --epochs 20 \
--num_classes 10 --use_slot false \
--vis false --channel 2048 --freeze_layers 0 \
--dataset_dir ../data/imagenet/ILSVRC/Data/CLS-LOC/

Positive Scouter for Imagenet dataset

python train.py --dataset ImageNet --model resnest26d --batch_size 70 --epochs 20 \
--num_classes 10 --use_slot true --use_pre false --loss_status 1 --slots_per_class 1 \
--power 2 --to_k_layer 3 --lambda_value 1 --vis false --channel 2048 --freeze_layers 0 \
--dataset_dir ../data/imagenet/ILSVRC/Data/CLS-LOC/

Negative Scouter for Imagenet dataset

python train.py --dataset ImageNet --model resnest26d --batch_size 70 --epochs 20 \
--num_classes 10 --use_slot true --use_pre false --loss_status -1 --slots_per_class 1 \
--power 2 --to_k_layer 3 --lambda_value 1 --vis false --channel 2048 --freeze_layers 0 \
--dataset_dir ../data/imagenet/ILSVRC/Data/CLS-LOC/

Visualization of Positive Scouter for Imagenet dataset

python test.py --dataset ImageNet --model resnest26d --batch_size 70 --epochs 20 \
--num_classes 10 --use_slot true --use_pre false --loss_status 1 --slots_per_class 1 \
--power 2 --to_k_layer 3 --lambda_value 1 --vis true --channel 2048 --freeze_layers 0 \
--dataset_dir ../data/imagenet/ILSVRC/Data/CLS-LOC/

Visualization of Negative Scouter for Imagenet dataset

python test.py --dataset ImageNet --model resnest26d --batch_size 70 --epochs 20 \
--num_classes 10 --use_slot true --use_pre false --loss_status -1 --slots_per_class 1 \
--power 2 --to_k_layer 3 --lambda_value 1 --vis true --channel 2048 --freeze_layers 0 \
--dataset_dir ../data/imagenet/ILSVRC/Data/CLS-LOC/

Visualization using torchcam for Imagenet dataset

python torchcam_vis.py --dataset ImageNet --model resnest26d --batch_size 70 \
--num_classes 10 --grad true --use_pre true \
--dataset_dir ../data/imagenet/ILSVRC/Data/CLS-LOC/ \
--grad_min_level 0

MNIST Dataset

Pre-training for MNIST dataset

python train.py --dataset MNIST --model resnet18 --batch_size 64 --epochs 10 \
--num_classes 10 --use_slot false --vis false --aug false

Positive Scouter for MNIST dataset

python train.py --dataset MNIST --model resnet18 --batch_size 64 --epochs 10 \
--num_classes 10 --use_slot true --use_pre true --loss_status 1 --slots_per_class 1 \
--power 1 --to_k_layer 1 --lambda_value 1. --vis false --channel 512 --aug false

Negative Scouter for MNIST dataset

python train.py --dataset MNIST --model resnet18 --batch_size 64 --epochs 10 \
--num_classes 10 --use_slot true --use_pre false --loss_status -1 --slots_per_class 2 \
--power 2 --to_k_layer 1 --lambda_value 1.5 --vis false --channel 512 --aug false --freeze_layers 3

Visualization of Positive Scouter for MNIST dataset

python test.py --dataset MNIST --model resnet18 --batch_size 64 --epochs 10 \
--num_classes 10 --use_slot true --use_pre true --loss_status 1 --slots_per_class 1 \
--power 1 --to_k_layer 1 --lambda_value 1. --vis true --channel 512 --aug false

Visualization of Negative Scouter for MNIST dataset

python test.py --dataset MNIST --model resnet18 --batch_size 64 --epochs 10 \
--num_classes 10 --use_slot true --use_pre false --loss_status -1 --slots_per_class 2 \
--power 2 --to_k_layer 1 --lambda_value 1.5 --vis true --channel 512 --aug false --freeze_layers 3

Visualization using torchcam for MNIST dataset

python torchcam_vis.py --dataset MNIST --model resnet18 --batch_size 64 \
--num_classes 10 --grad true --use_pre true

Con-Text Dataset

Pre-training for ConText dataset

python train.py --dataset ConText --model resnest26d --batch_size 200 --epochs 100 \
--num_classes 30 --use_slot false --vis false \
--dataset_dir ../data/con-text/JPEGImages/

Positive Scouter for ConText dataset

python train.py --dataset ConText --model resnest26d --batch_size 200 --epochs 100 \
--num_classes 30 --use_slot true --use_pre true --loss_status 1 --slots_per_class 3 \
--power 2 --to_k_layer 3 --lambda_value .2 --vis false --channel 2048 \
--dataset_dir ../data/con-text/JPEGImages/

Negative Scouter for ConText dataset

python train.py --dataset ConText --model resnest26d --batch_size 200 --epochs 100 \
--num_classes 30 --use_slot true --use_pre true --loss_status -1 --slots_per_class 3 \
--power 2 --to_k_layer 3 --lambda_value 1. --vis false --channel 2048 \
--dataset_dir ../data/con-text/JPEGImages/

Visualization of Positive Scouter for ConText dataset

python test.py --dataset ConText --model resnest26d --batch_size 200 --epochs 100 \
--num_classes 30 --use_slot true --use_pre true --loss_status 1 --slots_per_class 3 \
--power 2 --to_k_layer 3 --lambda_value 1. --vis true --channel 2048 \
--dataset_dir ../data/con-text/JPEGImages/

Visualization of Negative Scouter for ConText dataset

python test.py --dataset ConText --model resnest26d --batch_size 200 --epochs 100 \
--num_classes 30 --use_slot true --use_pre true --loss_status -1 --slots_per_class 3 \
--power 2 --to_k_layer 3 --lambda_value 1. --vis true --channel 2048 \
--dataset_dir ../data/con-text/JPEGImages/

Visualization using torchcam for ConText dataset

python torchcam_vis.py --dataset ConText --model resnest26d --batch_size 200 \
--num_classes 30 --grad true --use_pre true \
--dataset_dir ../data/con-text/JPEGImages/

CUB-200 Dataset

Pre-training for CUB-200 dataset

python train.py --dataset CUB200 --model resnest50d --batch_size 64 --epochs 150 \
--num_classes 25 --use_slot false --vis false --channel 2048 \
--dataset_dir ../data/bird_200/CUB_200_2011/CUB_200_2011/

Positive Scouter for CUB-200 dataset

python train.py --dataset CUB200 --model resnest50d --batch_size 64 --epochs 150 \
--num_classes 25 --use_slot true --use_pre true --loss_status 1 --slots_per_class 5 \
--power 2 --to_k_layer 3 --lambda_value 10 --vis false --channel 2048 --freeze_layers 2 \
--dataset_dir ../data/bird_200/CUB_200_2011/CUB_200_2011/

Negative Scouter for CUB-200 dataset

python train.py --dataset CUB200 --model resnest50d --batch_size 64 --epochs 150 \
--num_classes 25 --use_slot true --use_pre true --loss_status -1 --slots_per_class 3 \
--power 2 --to_k_layer 3 --lambda_value 1. --vis false --channel 2048 --freeze_layers 2 \
--dataset_dir ../data/bird_200/CUB_200_2011/CUB_200_2011/

Visualization of Positive Scouter for CUB-200 dataset

python test.py --dataset CUB200 --model resnest50d --batch_size 64 --epochs 150 \
--num_classes 25 --use_slot true --use_pre true --loss_status 1 --slots_per_class 5 \
--power 2 --to_k_layer 3 --lambda_value 10 --vis true --channel 2048 --freeze_layers 2 \
--dataset_dir ../data/bird_200/CUB_200_2011/CUB_200_2011/

Visualization of Negative Scouter for CUB-200 dataset

python test.py --dataset CUB200 --model resnest50d --batch_size 64 --epochs 150 \
--num_classes 25 --use_slot true --use_pre true --loss_status -1 --slots_per_class 3 \
--power 2 --to_k_layer 3 --lambda_value 1. --vis true --channel 2048 --freeze_layers 2 \
--dataset_dir ../data/bird_200/CUB_200_2011/CUB_200_2011/

Visualization using torchcam for CUB-200 dataset

python torchcam_vis.py --dataset CUB200 --model resnest50d --batch_size 150 \
--num_classes 25 --grad true --use_pre true \
--dataset_dir ../data/bird_200/CUB_200_2011/CUB_200_2011/

To run UI

uvicorn api:app --reload