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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes. It supports Auto-Labeling.
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## LabelTool lite (update!!!)
---
This repository provides you with a image data annotation tool with little to no configuration needed! The lite version of the tool focuses on bounding box based training data annotation. You can start labeling your images for state-of-the-art deep learning training purposes right away with our dockerized _LabelTool lite_. You can also directly use the labels provided by the _LabelTool lite_ to train with our Yolov4 and Tensorflow Training GUI repositories:
[BMW-TensorFlow-Training-GUI](https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI)
[BMW-YOLOv4-Training-Automation](https://github.com/BMW-InnovationLab/BMW-YOLOv4-Training-Automation)
Additionally, it is possible to connect a pre-trained or a custom-trained model to the _LabelTool lite_. This functionality allows one to accelerate the labeling process whereby the connected model can be actively used to suggest appropriate labels for each image.
<img src="./data/repo-data/Intro.gif" >
## Prerequisites
- Windows or Ubuntu
- Docker and Docker-compose
**PS: You may need to increase the shared memory docker is using**
## Running the _LabelTool lite_:
We provide a sample dataset in case you don't have your own custom dataset.
- To run the LabelTool lite and start labeling, simply go to your projects repository and run the command below:
```bash
docker-compose up
```
- Open any browser, go to localhost:8081 and start labeling!
<img src="./data/repo-data/Labeltool.png" >
## Navigation options in LabelTool lite
- The _LabelTool lite_ offers different options that facilitates you to comfortably navigate through the data set whilst labeling:
- Navigate to the next image in the data set - ">"
- Navigate to the previous image in the data set - "<"
- Navigate to the next image that has no bounding boxes - ">>"
- Navigate to the previous image that has no bounding boxes - "<<"
- Navigate to a particular image by inputting the image number directly
- Navigate to any image by clicking or dragging the cursor on the scroll line
<img src="./data/repo-data/Navigation.gif" >
- In addition to the above mentioned features, the _LabelTool lite_ offers:
- Zoom-in and zoom-out functionality for the images
- Increase and decrease of brightness for the images
- New images (one at a time) can be uploaded to the data set from your local computer via the Upload button
- Image attributes (name and resolution) can be displayed for each image
- Images (one at a time) and the corresponding bounding boxes can be deleted
<img src="./data/repo-data/Features.gif" >
## Bounding box features
- The LabelTool lite offers a variety of functionalities for bounding box creation
- The bounding boxes can be resized after creation
- The bounding boxes can be moved via drag and drop
- The bounding boxes can be copied
- The bounding boxes can be set to Fill or Unfill
- The line thickness of the bounding boxes can be changed
<img src="./data/repo-data/BBoxes.gif" >
## Preparing your custom dataset:
In your project's repository, this is how the training-data folder structure should look like after preparing your dataset (follow steps below):
```shell
│──/data/training-data
│──myfirstdataset
│ │──images
│ │──labels
│ │ │──json
│ │
│ │──objectclasses.json
│
│──topics.json
```
- The training-data folder is where all your datasets should be.
- For a new dataset, create a folder inside training-data (for example a folder called "myfirstdataset")
- Let's say that the topics.json file that is inside training-data currently looks like this:
<img src="./data/repo-data/Capture.PNG" height="352">
please add the following to your topics.json file just above the "]":
```json
,
{
"Id": 3,
"Name": "myfirstdataset",
"FolderPath": "myfirstdataset",
"Path": null
}
```
**Make sure you replace "myfirstdataset" with your dataset's name.**
After fiExcerpt of 8,233 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f1c9f04346bbaaa9, topic:deep-learning, topic:neural-network, topic:tensorflow
matched fp:f1c9f04346bbaaa9, topic:computer-vision, topic:object-detection