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Training Faster-RCNN on multiclass custom dataset
1. Introduction
Train object detector on multi-class custom dataset using Faster R-CCN in PyTorch.
This framework has the follow features:
- It is based on PyTorch framework
- It is designed to train on custom dataset
- It can train on multi-class dataset
- It automatically creates lables.txt file
2. Installations
2.1 Prerequisites
- Python 3
- Numpy
- PyTorch 1.8.1
- torchvision
- Pandas
- Opencv (cv2)
2.2 Code-Preparing
git clone https://github.com/harshatejas/pytorch_custom_object_detection.git
3. Dataset
This dataset contains images of playing cards.
The cards_dataset containds train folder, valication folder, train.csv and validation.csv
cards_datset/
train/
[xxx].jpg
...
validation/
[xxx].jpg
...
train.csv
validation.csv
4. Train
Modify Hyperparameters in train.py
train.py
5. Test
predict.py is designed to run predictions on the images in validation folder
Change the filename and saved_model in predict.py
predict.py
6. Predicted Images
Here are some sample output images predicted by saved_model/model