This project is a pytorch implementation of a Faster R-CNN for fruit detection suitable with multi-modal images (up to 5 channels). It's based on implementation of:
- jwyang/faster_rcnn.pytorch, developed based on Pytorch + Numpy
This implementation has been used to train and test the KFuji RGB-DS dataset, which contains images with 3 different modalities: colour (RGB), depth(D), and range-corrected intensity signal (S). Find more information in:
- Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities.
First of all, clone the code
git clone https://github.com/GRAP-UdL-AT/RGBD_fruit_detection_faster-rcnn.pytorch.git
Then, create a folder:
cd kinect_fruit_detection_faster-rcnn.pytorch && mkdir data
In the data folder is where datasets and models must be stored
- Python 2.7
- Pytorch 0.2.0
- CUDA 8.0 or higher
- KFuji RGB-DS dataset: Save the [KFuji RGB-DS dataset] (http://www.grap.udl.cat/en/publications/datasets.html) in data/kinect_fruits_dataset folder. If data is anotated using [Pychet Labeller] (https://github.com/imatge-upc/pychetlabeller), it is necessary to execute square_annot_from_pychet_rectangle.py.
We used VGG pretrained model in our experiments. You can download this model from:
Download and put it into the data/kinect_fruits_models/.
NOTE. That is not a faster-rcnn pretrained model, is just a pretrained VGG16 model to start to train "from scratch" the faster_rcnn part
As pointed out by ruotianluo/pytorch-faster-rcnn, choose the right -arch in lib/make.sh file, to compile the cuda code:
| GPU model | Architecture |
|---|---|
| TitanX (Maxwell/Pascal) | sm_52 |
| GTX 960M | sm_50 |
| GTX 1080 (Ti) | sm_61 |
| Grid K520 (AWS g2.2xlarge) | sm_30 |
| Tesla K80 (AWS p2.xlarge) | sm_37 |
More details about setting the architecture can be found here or here
For selecting the gpu architecture there are examples in makesh_examples/ so copy that as lib/make.sh
Information of the GPUs architecture of the imatge server is at: imatge.upc.edu information
Install all the python dependencies using pip:
pip install -r requirements.txt
Compile the cuda dependencies using following simple commands:
cd lib
srun --gres=gpu:pascal:1,gmem:6G --mem 12G sh make.sh
or
srun --gres=gpu:maxwell:1,gmem:6G --mem 12G sh make.sh
It will compile all the modules you need, including NMS, ROI_Pooing, ROI_Align and ROI_Crop. The default version is compiled with Python 2.7, please compile by yourself if you are using a different python version.
IMPORTANT NOTE The srun --gres... until python script execution has to be the same of what used in compilation of make.sh
Execute trainval to do the train and validation in the same script
srun --gres:$architecture:1,gmem:6G --mem 30G -c 2 python trainval_net.py \
--dataset kinect_fruits --net vgg16_5ch \
--bs $BATCH_SIZE --lr $LEARNING_RATE \
--lr_decay_step $DECAY_STEP --RGB --NIR --DEPTH \
--epochs $NUM_EPOCHS --o $OPTIMIZER \
--s $SESSION --anchor $ANCHOR_SCALE --cuda
example:
srun --gres=gpu:1,gmem:10G --mem 30G -c 2 python trainval_net.py --dataset kinect_fruits_k --net vgg16_5ch --bs 4 --lr 0.0001 --lr_decay_step 10 --RGB --NIR --DEPTH --epochs 45 --o adam --s 60 --anchor 4 --anchor 8 --anchor 16 --cuda
These script only compute the loss
If you want to evlauate the detection performance of a pre-trained faster-rcnn model on kinect_fruits test set, simply run
srun --gres:$architecture:1,gmem:6G --mem 30G python test_net.py\
--dataset kinect_fruits --net vgg16_5ch \
--RGB --DEPTH --NIR --cheksession $SESSION\
--checkpoint $POINT --anchor $ANCHOR_SCALE \
--ovthresh $minIOU --minconfid $minCONFIDENCE\
--cuda
example:
srun --gres=gpu:1,gmem:10G --mem 30G -c 2 python test_net.py --dataset kinect_fruits_k --net vgg16_5ch --RGB --DEPTH --NIR --checksession 60 --checkpoint 309 --anchor 4 --anchor 8 --anchor 16 --ovthresh 0.2 --minconfid 0.4 --minconfid 0.45 --minconfid 0.5 --minconfid 0.55 --minconfid 0.6 --minconfid 0.65 --minconfid 0.7 --cuda
This script computes mean average precision, precision, recall, F1-score and the number of inferred images per second.
If you want to run detection on your own images with a pre-trained model, download the pretrained model or train your own models at first, then add images to folder $ROOT/images_kinect_fruits, and then run
srun --gres=gpu:1,gmem:10G --mem 30G -c 2 python demo.py \
--dataset kinect fruits --net vgg16_5ch \
--RGB --DEPTH --NIR --cheksession $SESSION\
--checkpoint $POINT --checkepoch $epoch \
--anchor $ANCHOR_SCALE --minconfid $minCONFIDENCE\
--image_dir images_kinect_fruits --cuda
example:
srun --gres=gpu:1,gmem:10G --mem 30G -c 2 python demo.py --dataset kinect_fruits --net vgg16_5ch --RGB --DEPTH --NIR --checksession 42 --checkpoint 309 --checkepoch 12 --anchor 4 --anchor 8 --anchor 16 --minconfid 0.6 --image_dir images_kinect_fruits --cuda
Then you will find the detection results in folder $ROOT/images_kinect_fruits.
This project is contributed by GRAP-UdL-AT and ImageProcessingGroup-UPC and it is based on the implementation of Jianwei Yang.
Please contact authors to report bugs @ [email protected]
If you find this implementation or the analysis conducted in our report helpful, please consider citing:
@article{Gene-Mola2019,
author = {Gen{\'e}-Mola, Jordi and Vilaplana, Ver{\'o}nica and Rosell-Polo, Joan R and Morros, Josep-Ramon and Ruiz-Hidalgo, Javier and Gregorio, Eduard},
title = {Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities},
journal={Computers and Electronics in Agriculture},
volume={162},
pages={689--698},
year={2019},
publisher={Elsevier}
}
For convenience, here is the Faster RCNN citation:
@inproceedings{renNIPS15fasterrcnn,
Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
Title = {Faster {R-CNN}: Towards Real-Time Object Detection
with Region Proposal Networks},
Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},
Year = {2015}
}