- Download the Keras-Retinanet source from https://github.com/fizyr/keras-retinanet
- Install Tensorflow > 2.3.0. In my case I have Tensorflow 2.3.1
- Execute
pip3 install . –user. - To compile Cython code run
python setup.py build_ext –inplace
- Download pre-trained model of Keras-Retinanet from
https://github.com/fizyr/keras-retinanet/releasesand save it to snapshots folder. - Go to examples folder and open ‘’’ resnet50_retinanet.py
and confirm your downloaded model name inmodel_path = os.path.join('..', 'snapshots', 'resnet50_coco_best_v2.1.0.h5')```. - After confirmation run
python resnet50_retinanet.py. If it runs successfully, congratulation you have successfully installed Keras-Retinanet. - If there is an compute_overlap error
File “..\keras_retinanet\utils\anchors.py”, line 20, in <module>
from ..utils.compute_overlap import compute_overlap
ImportError: DLL load failed while importing compute_overlap
Than open “keras_retinanet\utils\ anchors.py” in your editor and edit
from ..utils.compute_overlap import compute_overlap
to
from compute_overlap import compute_overlap
This will solve the error of cumpute_overlap.
- Step1 : Prepare Dataset
- Step2 : Labelling/Annotation
- Step3 : Train
- Step4 : Test
First, you need to define what object you want to detect. In this example i have used "racoon" images to train.[https://public.roboflow.com/object-detection/raccoon]. Copy all your images in “dataset/JPEGImages” folder.
The next step is to Label/annotate the dataset according to respective class. In this case there is only one class : "raccoon". I used labelImg from https://github.com/tzutalin/labelImg. It is a graphical image annotation tool written in Python and it uses Qt for its graphical interface. In labelImg confirm the save directory of “.xml” file to “dataset/Annotations” folder.
I have used PASCAL VOC format since it is supported by Keras-Retinanet.
Images are in “dataset/JPEGImages”
XML file are in “dataset/Annotations”
Divide the whole dataset into training and validation
Open/Run “train_val_divide.py” in and enter your desired percentage_val default is “percentage_val = 10;” . This will save “train.txt” and “val.txt” in directory “ImageSets/Main/”
Directory Structure of dataset
+---dataset
| | train_val_divide.py
| |
| +---Annotations
| | raccoon-1.xml
| | raccoon-10.xml
| | raccoon-100.xml
| | …
| +---ImageSets
| | \---Main
| | train.txt
| | val.txt
| |
| \---JPEGImages
| raccoon-1.jpg
| raccoon-10.jpg
| raccoon-100.jpg
| …
Define the classes to "keras_retinanet/preprocessing/pascal_voc.py" Default is
voc_classes = {
'aeroplane' : 0,
'bicycle' : 1,
'bird' : 2,
'boat' : 3,
'bottle' : 4,
'bus' : 5,
'car' : 6,
'cat' : 7,
'chair' : 8,
'cow' : 9,
'diningtable' : 10,
'dog' : 11,
'horse' : 12,
'motorbike' : 13,
'person' : 14,
'pottedplant' : 15,
'sheep' : 16,
'sofa' : 17,
'train' : 18,
'tvmonitor' : 19
}
Changed to
voc_classes = {
'raccoon' : 0
}
Because i have only one class
python keras_retinanet/bin/train.py --weights snapshots/resnet50_coco_best_v2.1.0.h5 --steps 1000 --epochs 10 --snapshot-path snapshots --tensorboard-dir tensorboard pascal dataset
--weights -> Initialize the model with weights from a file
--steps -> Number of steps per epoch
--Epochs -> Number of epochs to train
--snapshot-path -> Path to store snapshots of models during training
--tensorboard-dir -> Log directory for Tensorboard output
Pascal -> Directory of our dataset
To test our trained model, first we need to convert it from training format to test format.
python keras_retinanet/bin/convert_model.py snapshots/resnet50_pascal_01.h5 directory_to_save_test_model.h5
Now we can this Saved model for our object detection. ** You can use "examples/resnet50_retinanet.py" to test the trained model