Quick Start - Synthetic Data Demo
This example demonstrates the process of generating synthetic data, specifically for training models to detect animals in road scenarios.
The image synthesis involves introducing a dog into the scene of a road, where the objective is to analyze potential hazards and responses from drivers.
The synthetic process includes six steps: uploading images, annotating objects (like the dog), synthesizing the scene, selecting relevant variations, verifying the model’s ability to detect both cars and animals, and downloading the final model for further training or deployment.
In this case, the dog is artificially placed to simulate real-world challenges of animal detection on the road.
The specific steps for model development are as follows:
1. Create Model
Click on "Create a model".
Enter the Model name (up to 20 objects), select the Model type as "Image Synthetic", and click "Confirm" to create the model.
2. Upload Images
Click on "Dataset" to upload images. Click on "Upload Image".
Check the requirements for uploading images, click on "Upload now", select the images, and upload them.
Batch uploading is supported.
After the upload is complete, the upload results will be displayed, and you can see that all the images have been successfully uploaded. This completes the image uploading process.
If you want to upload locally labeled images,click here for more information! Upload img+LBL
3. Label Images
Enter the Labeling interface where you can label the images. The red box in the image represents the toolbar.
On the left side of the toolbar is the labeling area. The first tool is a irregular box that can be used to enclose objects. In the middle of the toolbar, you can zoom in and out of the image (you can also use the mouse scroll wheel to zoom). On the right side of the toolbar, there are some advanced features.
The Image Synthetic annotation process mainly consists of two steps:
1. Use the synthetic tool in the toolbar to enclose objects.
2. Type the label for each object.
For detailed label rules and examples, please refer to "More" -> "Label Examples".
4. To Synthesis
Click on “To Generate”.
Upload a base image and select a label, then click “commit” to begin synthesis.
The following screen will appear, indicating that the task is in the queue. Please wait.
5. Model Validation
If you want to validate synthetic data, you can train the model and train the images.
5.1 Train model
Enter the "Training" interface and click on "Start Training".
Then the model will start training, which will take approximately 10 minutes.
If you want to check if the generated model performs well, click here for more information! Average dectetion rate
5.2 Test Images
Enter the Test page and click on "Start Testing" to perform model testing.
Once the testing is complete, the following message will appear, indicating that the testing is finished. If you want to view the testing results, click on "View Test Results".
If you want to obtain a large number of pre-labeled images but label few picture , click here for more information! Test result transfer
If you want to know how confident the model's prediction results are, click here for more information! Show confidence level
6.Export Model
If you need, you can export the model.
Enter the download page, select "Current Model" and corresponding GPU, and click on "Export Model".
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