https://github.com/edgeimpulse/yolov2
YoloV2 Keras Implementation
https://github.com/edgeimpulse/yolov2
Last synced: about 2 months ago
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YoloV2 Keras Implementation
- Host: GitHub
- URL: https://github.com/edgeimpulse/yolov2
- Owner: edgeimpulse
- License: bsd-3-clause-clear
- Created: 2024-02-23T14:29:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-10T10:18:09.000Z (3 months ago)
- Last Synced: 2025-03-07T00:58:42.550Z (about 2 months ago)
- Language: Python
- Size: 156 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Local training pipeline for "Brainchip / FOMO project using BrainChip MetaTF and Akidanet models"
This is the local training pipeline (based on Keras / TensorFlow) for your Edge Impulse project [Brainchip / FOMO project using BrainChip MetaTF and Akidanet models](http://localhost:4800/studio/148833) (http://localhost:4800/studio/148833). Use it to train your model locally or run experiments. Once you're done with experimentation you can push the model back into Edge Impulse, and retrain from there.
## Running the pipeline
You run this pipeline via Docker. This encapsulates all dependencies and packages for you.
### Running via Docker
1. Install [Docker Desktop](https://www.docker.com/products/docker-desktop/).
2. Open a command prompt or terminal window.
3. Build the container:
```
$ docker build -t custom-block-148833 .
```
4. Train your model:
**macOS, Linux**
```
$ docker run --rm -v $PWD:/scripts custom-block-148833 --data-directory data --out-directory out
```
**Windows**
```
$ docker run --rm -v "%cd%":/scripts custom-block-148833 --data-directory data --out-directory out
```
5. This will write your model (in TFLite, Saved Model and H5 format) to the `out/` directory.
#### Adding extra dependencies
If you have extra packages that you want to install within the container, add them to `requirements.txt` and rebuild the container.
#### Adding new arguments
To add new arguments, see [Custom learning blocks > Arguments to your script](https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/adding-custom-learning-blocks#arguments-to-your-script).
## Fetching new data
To get up-to-date data from your project:
1. Install the [Edge Impulse CLI](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-installation) v1.16 or higher.
2. Open a command prompt or terminal window.
3. Fetch new data via:
```
$ edge-impulse-blocks runner --download-data data/
```
## Pushing the block back to Edge Impulse
You can also push this block back to Edge Impulse, that makes it available like any other ML block so you can retrain your model when new data comes in, or deploy the model to device. See [Docs > Adding custom learning blocks](https://docs.edgeimpulse.com/docs/edge-impulse-studio/organizations/adding-custom-transfer-learning-models) for more information.
1. Push the block:
```
$ edge-impulse-blocks push
```
2. The block is now available under any of your projects via **Create impulse > Add new learning block**.