- Pytorch version 0.4 or higher.
Given a test image, the trained model outputs blobs in the image, then counts the number of predicted blobs (see Figure below).
We test a trained ResNet on a Trancos example image as follows:
python main.py -image_path figures/test.png \
-model_path checkpoints/best_model_trancos_ResFCN.pth \
-model_name ResFCN
The expected output is shown below, and the output image will be saved in the same directory as the test image.
Trancos test image | Trancos predicted image |
---|---|
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- Download the checkpoints,
bash checkpoints/download.sh
- Output the saved results,
python main.py -m summary -e trancos
- Re-evaluate the saved model,
python main.py -m test -e trancos
To train the model,
python main.py -m train -e trancos
Method | Trancos | Pascal |
---|---|---|
ResFCN | 3.39 | 0.31 |
Paper | 3.32 | 0.31 |
If you find the code useful for your research, please cite:
@Article{laradji2018blobs,
title={Where are the Blobs: Counting by Localization with Point Supervision},
author={Laradji, Issam H and Rostamzadeh, Negar and Pinheiro, Pedro O and Vazquez, David and Schmidt, Mark},
journal = {ECCV},
year = {2018}
}