Tensorflow 2.x implementation of Panoramic BlitzNet for object detection and semantic segmentation on indoor panoramic images.
This repository contains an original implementation of the paper: ‘What’s in my Room? Object Recognition on Indoor Panoramic Images’ by Julia Guerrero-Viu, Clara Fernandez-Labrador, Cédric Demonceaux and José J. Guerrero. More info can be found in our project page
We recommend the use of a virtual enviroment for the use of this project. (e.g. anaconda)
$ conda new -n envname python=3.8.5 # replace envname with your prefered name
1. This code has been compiled and tested using:
- python 3.8.5
- cuda 10.1
- cuDNN 7.6
- TensorFlow 2.3
You are free to try different configurations but we do not ensure it had been tested.
2. Install python requirements:
(envname)$ pip install -r requirements.txt
Copy the folder ‘dataset’ to the folder where you have the repository files.
Download the folder ‘Checkpoints’ which includes the model weights and copy it to the folder where you have the repository files.
Ensure the folders ‘dataset’ and ‘Checkpoints’ are in the same folder than the python files.
To run our demo please run:
(envname)$ python3 test.py PanoBlitznet # Runs the test examples and saves results in 'Results' folder
Training and evaluation
If you want to train the model changing some parameters and evaluate the results follow the next steps:
TFDS from SUN360:1. Create a
Do this ONLY if it is the first time using this repository.
Ensure the folder ‘dataset’ is in the same folder than the python files.
Change the line 86 in sun360.py file with your path to the ‘dataset’ folder.
(envname)$ cd /path/to/project/folder
(envname)$ tfds build sun360.py # Creates a TFDS (Tensorflow Datasets) from SUN360
2. Train a model:
To train a model change the parameters you want in the config.py file. You are free to try different configurations but we do not ensure it had been tested.
<div class="snippet-clipboard-content position-relative overflow-auto" data-snippet-clipboard-copy-content="Usage: training_loop.py [–restore_ckpt]
-h –help Show this screen.
–restore_ckpt Restore weights from previous training to continue with the training.”>
Usage: training_loop.py [--restore_ckpt] Options: -h --help Show this screen. --restore_ckpt Restore weights from previous training to continue with the training.