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https://github.com/mgthetrain/python-yolo-training-with-jupyter-notebooks
Repository demonstrating how to train a custom CNN model based on yolo-v4-tiny architecture. This can be utilized for image classification, image localization or object detection applications.
https://github.com/mgthetrain/python-yolo-training-with-jupyter-notebooks
cnn model-training python yolov4-tiny
Last synced: about 1 month ago
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Repository demonstrating how to train a custom CNN model based on yolo-v4-tiny architecture. This can be utilized for image classification, image localization or object detection applications.
- Host: GitHub
- URL: https://github.com/mgthetrain/python-yolo-training-with-jupyter-notebooks
- Owner: MGTheTrain
- Created: 2023-11-12T12:21:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-12T19:32:24.000Z (11 months ago)
- Last Synced: 2024-01-13T10:49:32.911Z (11 months ago)
- Topics: cnn, model-training, python, yolov4-tiny
- Language: Jupyter Notebook
- Homepage:
- Size: 38.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# python-yolo-training-with-jupyter-notebooks
## Table of Contents
+ [Summary](#summary)
+ [References](#references)
+ [How to use](#how-to-use)
+ [Labeling images](#labeling-images)
+ [Updating the yolov4-tiny-custom.cfg file](#updating-the-yolov4-tiny-customcfg-file)
+ [Uploading the custom-data folder to Google Drive](#uploading-the-custom-data-folder-to-google-drive)
+ [Running code blocks of the custom Jupyter notebook in Google Colab](#running-code-blocks-of-the-custom-jupyter-notebook-in-google-colab)
+ [Init and update git submodules](#init-and-update-git-submodules)
+ [Utilize your trained weights in the sample object detector app](#utilize-your-trained-weights-in-the-sample-object-detector-app)
## Summary
Repository demonstrating how to train a custom CNN model based on yolo-v4-tiny architecture. This can be utilized for image classification, image localization or object detection applications.
[](https://colab.research.google.com/github/MGTheTrain/python-yolo-training-with-jupyter-notebooks/blob/main/notebooks/custom-yolov4-tiny-training.ipynb)
## References
- [Github repository of Techzizou - yolov4-tiny-custom_Training](https://github.com/techzizou/yolov4-tiny-custom_Training/tree/main)
- [Medium post by Techzizou - Train a custom yolov4 tiny object detector using google colab](https://medium.com/analytics-vidhya/train-a-custom-yolov4-tiny-object-detector-using-google-colab-b58be08c9593)
- [Jupyter notebook of Techzizou](https://colab.research.google.com/drive/1hQO4nOoD6RDxdbz3C1YSiifTsyZjZpYm?usp=sharing#scrollTo=afZcMjuiLEUi)
- [darknet](https://github.com/AlexeyAB/darknet)
## How to use
### Labeling images
Add your images (e.g. .JPG files) to the [img folder](data-custom/img) and label your images. For labeling images utilize for example one of the following:
- [labelimg](https://github.com/tzutalin/labelImg#labelimg)
- [Yolo_mark](https://github.com/AlexeyAB/Yolo_mark)
Modify [train.txt](data-custom/train.txt) to encompass all JPG files for training purposes, and adjust [test.txt](data-custom/test.txt) to incorporate all JPG files for validation.
Also update the [obj.names file](data-custom/obj.names) to list your classes.
### Updating the yolov4-tiny-custom.cfg file
Adjust in the [yolov4-tiny-custom.cfg](data-custom/yolov4-tiny-custom.cfg) the `width`, `height`, `batch`, `subdivision`, `max_batches`, `steps`, `classes` and `filters` hyperparameter values. Refer to [section 3(a) Create and upload the labeled custom dataset “obj.zip” file to the “yolov4-tiny” folder on your drive here](https://medium.com/analytics-vidhya/train-a-custom-yolov4-tiny-object-detector-using-google-colab-b58be08c9593) or copy the original file which can be found here [yolov4-tiny-custom.cfg](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov4-tiny-custom.cfg) and replace [this yolov4-tiny-custom.cfg](data-custom/yolov4-tiny-custom.cfg).
### Uploading the custom-data folder to Google Drive
Upload the [data-custom folder](data-custom) to [Google Drive](https://www.google.com/intl/de/drive/).
### Running code blocks of the custom Jupyter notebook in Google Colab
Open [custom-yolov4-tiny-training.ipynb in Colab](https://colab.research.google.com/github/MGTheTrain/python-yolo-training-with-jupyter-notebooks/blob/main/notebooks/custom-yolov4-tiny-training.ipynb) and run each code block
### Init and update git submodules
Execute the following steps in order to initialize the git submodule containing [the object detector app](https://github.com/MGTheTrain/python-object-detection-with-yolo-and-opencv/tree/main/object_detector_app.py):
```sh
# On Unix terminals
git submodule init --update
# or on Windows OS
git submodule init
git submodule update
```
### Utilize your trained weights in the sample object detector app
Copy custom `.weights`, `.cfg` and `.names` files to appropriate destination pathes:
```sh
# On Unix terminals
cp /yolov4-tiny-custom_best.weights python-object-detection-with-yolo-and-opencv/weights
cp data-custom/yolov4-tiny-custom.cfg python-object-detection-with-yolo-and-opencv/cfg
cp data-custom/obj.names python-object-detection-with-yolo-and-opencv/object-names
# On Powershell (Windows OS)
Copy-Item "\yolov4-tiny-custom_best.weights" -Destination "python-object-detection-with-yolo-and-opencv\weights"
Copy-Item "data-custom\yolov4-tiny-custom.cfg" -Destination "python-object-detection-with-yolo-and-opencv\cfg"
Copy-Item "data-custom\obj.names" -Destination "python-object-detection-with-yolo-and-opencv\object-names"
```
In [python-object-detection-with-yolo-and-opencv](https://github.com/MGTheTrain/python-object-detection-with-yolo-and-opencv/tree/main/) install the pip package requirements if not yet done and launch the object detector app via
```sh
cd python-object-detection-with-yolo-and-opencv
python object_detector_app.py --model custom-yolov4-tiny
```