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mopad's Introduction

MOPAD

These codes and datsets are from the paper "Growing status observation for oil palm tree using Unmanned Aerial Vehicle (UAV) images", which is under review in ISPRS Photogrammetry and Remote Sensing (Major Revision).

Training

CUDA_VISIBLE_DEVICES=gpu_id python tools/train.py configs/oilPalmUav/mopad.py

Inference for the whole image

CUDA_VISIBLE_DEVICES=gpu_id python demo/demoFull.py configs/oilPalmUav/mopad.py work_dirs/mopad/latest.pth mopad-det.txt test_images

Models

Our training models for Site 2 can be downloaded from

Baidu Wangpan Access: 7n61

Our training models for Site 1 can be downloaded from

Baidu Wangpan Access: 8mwa

Dataset

Our dataset for Site 2 can be downloaded from

Google Drive

Baidu Wangpan Access: qpaw

Our dataset for Site 1 can be downloaded from

Baidu Wangpan Access: fgfv

The data should be saved in the folder ./data

Details of Dataset

We followed COCO format basically.

The structure of the dataset is as follows:

  • train2017: images for training dataset (like <id>.jpg)
  • val2017: images for validation dataset (like <id>.jpg)
  • annotations: annotations including instances_train2017.json and instances_val2017.json for training and validation dataset, respectively

mopad's People

Contributors

darrelllim avatar rs-dl avatar

Stargazers

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