xview3-kohei-solution

Usage

This repository assumes that the given data set is stored in the following locations:

$ ls data/input/xview3/*.csv
data/input/xview3/train.csv  data/input/xview3/validation.csv

$ ls data/input/xview3/downloaded
00a035722196ee86t.tar.gz  4455faa0cb4824f4t.tar.gz  85fe34d1aee53a7ft.tar.gz  c07f6ec980c2c149t.tar.gz
014261f774287442t.tar.gz  4518c556b38a5fa4t.tar.gz  864390795b0439b1t.tar.gz  c0831dbd6d7f3c56t.tar.gz
(snip)

In the case of training, create a virtual env as follows:

# Setup: Create the virtual env
$ poetry config virtualenvs.in-project true
$ poetry install
$ poetry run pip install albumentations timm segmentation_models_pytorch torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

Training

To save storage space, my preprocessing code assumes that the input image file is the original file in .tar.gz format. It does NOT assume pre-extracted files. The batch size and other settings are based on the assumption that two RTX3080 GPU cards are used.

$ bash train.sh

Inference

My containerized inference code follows the xView3 evaluation protocol. The detailed usage of the xview3 evaluation protocol is described in https://iuu.xview.us/verify.

To build the Docker Image, you need the model weights generated by train.sh. The pre-computed files can be downloaded from my Google Drive.

You will also need to download and extract gshhg-shp-2.3.7.zip as external data. GSHHG data can be found at https://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/latest/

The Docker image containing these model weights and external data has already been uploaded to Docker Hub (smly/kohei-xview3:latest).

## Setup: Build the docker image -----
$ docker build --no-cache -t kohei-xview3 .

## Inference -----
$ docker run \
    --shm-size 16G \
    --gpus=1 \
    --mount type=bind,source=/home/xv3data,target=/on-docker/xv3data \
    kohei-xview3 \
    /on-docker/xv3data/ \
    0157baf3866b2cf9v \
    /on-docker/xv3data/prediction/prediction.csv

Kohei's 5th place solution for xview3 challenge

Related tags

Overview

xview3-kohei-solution

Usage

Training

Inference

Owner

Kohei Ozaki

A unet implementation for Image semantic segmentation

Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

v objective diffusion inference code for JAX.

GPU Programming with Julia - course at the Swiss National Supercomputing Centre (CSCS), ETH Zurich

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Using machine learning to predict undergrad college admissions.

implementation for paper "ShelfNet for fast semantic segmentation"

CATE: Computation-aware Neural Architecture Encoding with Transformers

ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Scalable machine learning based time series forecasting

領域を指定し、キーを入力することで画像を保存するツールです。クラス分類用のデータセット作成を想定しています。

DIR-GNN - Discovering Invariant Rationales for Graph Neural Networks

Pytorch Implementation of Various Point Transformers

Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, Pattern Recognition

Imagededup - 😎 Finding duplicate images made easy

Loopy belief propagation for factor graphs on discrete variables, in JAX!

tmm_fast is a lightweight package to speed up optical planar multilayer thin-film device computation.

Nest Protect integration for Home Assistant. This will allow you to integrate your smoke, heat, co and occupancy status real-time in HA.

A Python type explainer!

A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021)