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https://github.com/edisonslightbulbs/tfod-model-training
Transfer learning using TensorFlow's object detection API
https://github.com/edisonslightbulbs/tfod-model-training
ipython-notebook object-detection opencv python-scripts tensorflow transfer-learning
Last synced: 14 days ago
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Transfer learning using TensorFlow's object detection API
- Host: GitHub
- URL: https://github.com/edisonslightbulbs/tfod-model-training
- Owner: edisonslightbulbs
- Created: 2021-08-24T07:04:39.000Z (about 3 years ago)
- Default Branch: develop
- Last Pushed: 2023-04-06T06:01:03.000Z (over 1 year ago)
- Last Synced: 2024-11-05T05:03:51.682Z (14 days ago)
- Topics: ipython-notebook, object-detection, opencv, python-scripts, tensorflow, transfer-learning
- Language: Python
- Homepage:
- Size: 22.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# A minimal template for training an object detection model
[](https://github.com/edisonslightbulbs/tfod-model-training/stargazers)
[](https://github.com/edisonslightbulbs/tfod-model-training/network)
[](https://github.com/edisonslightbulbs/tfod-model-training/blob/main/LICENSE)
This project demonstrates transfer learning using Tensorflow.
The underlying concepts provide a solid foundation for training machine learning models.
### Quick Start: Setup the development environment
1. Install [Anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html) or [Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html).
2. Create a virtual environment with Python 3.7.3, no default packages, and activate it.
```zsh
conda create --name myenv --no-default-packages python=3.7
conda activate myenv
```
3. Install the required packages.
```zsh
# from the repository's root directory:
pip install -r requirements.txt
```
4. Install `ipykernel`:
```zsh
python -m ipykernel install --user --name=myenv
```
### Quick Start: Collect and label images
1. Collect target images in `./data/raw/`.
2. Label images using `labelImg`.
a. From terminal run:
```zsh
labelImg
```
b. Using `labelImg`, navigate to `./data/raw/` and label images.
## Running Project: Using CLI
1. Setup:
`python cli.py --setup --image-format < > --pretrained-model-url < > --pretrained-model-name < >`
E.g.
```zsh
python cli.py --setup --image-format jpg --pretrained-model-url http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz --pretrained-model-name ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8
```
2. Train:
`python cli.py --train --batch-size < > --epochs < > --train-test-split < > --pretrained-model-ckpt < >`
E.g.
```zsh
python cli.py --train --batch-size 4 --epochs 1000 --train-test-split 0.7 --pretrained-model-ckpt 0
```
3. Smoke test:
`python cli.py --validate --trained-model-ckpt < >`
E.g.
```zsh
python cli.py --validate --trained-model-ckpt 1
```
### Running Project: Using notebooks
From the repository's root directory, start a Jupyter Notebook session and select the kernel initialized during setup.
1. Step through `./notebooks/setup.ipynb` to set up the project.
2. Step through `./notebooks/train.ipynb` to train an object detection model.
3. Step through `./notebooks/validate.ipynb` to smoke test the trained image-object detection model.
---
###### N.b., to enable GPU-base training, check your TensorFlow version and ensure [tallying CUDA and CUDNN versions](https://www.tensorflow.org/install/source_windows) are installed.
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