Code for object detection adapting EfficientDet.
- Team: The Fastest Kokushimuso in Hsintsu
- Competetion: Kaggle Global Wheat Detection ECCV 2020
- Method: EfficientDet
- Ubuntu 16.04 LTS
- Most of the code based on shonenkov's notebook, we appreciate his awesome work.
First, clone our wheat-detection repo
$ https://github.com/osinoyan/wheat-detection
$ cd wheat-detection
All requirements should be detailed in requirements.txt. Using Anaconda is strongly recommended.
conda create -n wheat python=3.6
source activate wheat
pip install -r requirements.txt
Download the dataset from kaggle and unzip global-wheat-detection.zip under the directory data/. Make sure to place the data like below:
wheat-detection/
+- data/
| +- sample_submission.csv
| +- train.csv
| +- test/
| | +- 2fd875eaa.jpg
| | +- ...
| +- train/
| | +- 0a3cb453f.jpg
| | +- ...
... ...
We briefly provide the instructions to train the model
Unfortunately, you have to download the weights manually. Download efficientdet_d5-ef44aea8.pth from This link and make sure to place it under the directory efficientdet_model/ like below:
wheat-detection/
+- efficientdet_model/
| +- efficientdet_d5-ef44aea8.pth
+- data/
+- timm-efficientdet-pytorch-revised/
+- weighted-boxes-fusion/
|
...
Just run this:
python train.py