Multi-Glimpse Network With Python

Last update: May 10, 2022

Related tags

Deep Learning MGNet

Overview

Multi-Glimpse Network

Our code requires Python ≥ 3.8

Installation

For example, venv + pip:

$ python3 -m venv env
$ source env/bin/activate
(env) $ python3 -m pip install -r requirements.txt

Evaluation

Accuracy on clean images

Create ImageNet100 from ImageNet (using symbolic links).

$ python3 tools/create_imagenet100.py tools/imagenet100.txt \
    /path/to/ImageNet /path/to/ImageNet100

Download checkpoints from Google Drive.
Test accuracy.

$ export dataset="--train_dir /path/to/ImageNet100/train \
    --val_dir /path/to/ImageNet100/val \
    --dataset imagenet --num_class 100"
# Baseline
$ python3 main.py $dataset --test --n_iter 1 --scale 1.0  --model resnet18 \
    --checkpoint resnet18_baseline
# Ours
$ python3 main.py $dataset --test --n_iter 4 --scale 2.33 --model resnet18 \
    --checkpoint resnet18_ours --alpha 0.6 --s 0.02

Add the flag --flop_count to count the approximate FLOPs for the inference of an image. (using fvcore)

Accuracy on adversarial attacks (PGD)

Test adversarial accuracy.

# Baseline
$ python3 main.py $dataset --test --n_iter 1 --scale 1.0  --adv --step_k 10 \
    --model resnet18 --checkpoint resnet18_baseline
# Ours
$ python3 main.py $dataset --test --n_iter 4 --scale 2.33 --adv --step_k 10 \
    --model resnet18 --checkpoint resnet18_ours --alpha 0.6 --s 0.02

Accuracy on common corruptions

Create ImageNet100-C from ImageNet-C (using symbolic links).

$ python3 tools/create_imagenet100c.py  \
    tools/imagenet100.txt  /path/to/ImageNet-C/ /path/to/ImageNet100-C/

Test for a single corruption.

$ export dataset="--train_dir /path/to/ImageNet100/train \
    --val_dir /path/to/ImageNet100-C/pixelate/5 \
    --dataset imagenet --num_class 100"
# Baseline
$ python3 main.py $dataset --test --n_iter 1 --scale 1.0  --model resnet18 \
    --checkpoint resnet18_baseline
# Ours
$ python3 main.py $dataset --test --n_iter 4 --scale 2.33 --model resnet18 \
    --checkpoint resnet18_ours --alpha 0.6 --s 0.02

A simple script to test all corruptions and collect results.

# Modify tools/eval_imagenet100c.py and run it to generate script
$ python3 tools/eval_imagenet100c.py /home2/ImageNet100-C/ > run.sh
# Evaluate
$ bash run.sh
# Collect results
$ python3 tools/collect_imagenet100c.py

Training

$ export dataset="--train_dir /path/to/ImageNet100/train \
    --val_dir /path/to/ImageNet100/val \
    --dataset imagenet --num_class 100"
# Baseline
$ python3 main.py $dataset --epochs 400 --n_iter 1 --scale 1.0 \
    --model resnet18 --gpu 0,1,2,3
# Ours
$ python3 main.py $dataset --epochs 400 --n_iter 4 --scale 2.33 \
    --model resnet18 --alpha 0.6 --s 0.02  --gpu 0,1,2,3

Check tensorboard for the logs. (When training with multiple gpus, the log value may be scaled by the number of gpus except for the validation accuracy)

tensorboard  --logdir=logs

Note that we left our exploration in the code for further study, e.g., self-supervised spatial guidance, dynamic gradient re-scaling operation.

Multi-Glimpse Network With Python

Related tags

Overview

Multi-Glimpse Network

Installation

Evaluation

Accuracy on clean images

Accuracy on adversarial attacks (PGD)

Accuracy on common corruptions

Training

Owner

Extracts data from the database for a graph-node and stores it in parquet files

Object Tracking and Detection Using OpenCV

Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CCT)

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks

Tracking Pipeline helps you to solve the tracking problem more easily

VGGFace2-HQ - A high resolution face dataset for face editing purpose

Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

[NeurIPS'20] Multiscale Deep Equilibrium Models

Existing Literature about Machine Unlearning

MicroNet: Improving Image Recognition with Extremely Low FLOPs (ICCV 2021)

Chatbot in 200 lines of code using TensorLayer

Transfer Learning Shootout for PyTorch's model zoo (torchvision)

DeepGNN is a framework for training machine learning models on large scale graph data.

RL Algorithms with examples in Python / Pytorch / Unity ML agents

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Build and run Docker containers leveraging NVIDIA GPUs

Code for Ditto: Building Digital Twins of Articulated Objects from Interaction