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Data Curation For Labelling
The Issue
Issue Statement
Sifting through large visual datasets and building good training datasets is cumbersome and time consuming process
What exactly is the issue?
- Often image sensors are video streams which inherently means multiple frames per second (30 FPS – 60 FPS).
- If you are trying to build a robust training set, you probably want to have a diverse set of examples that is representative of the scenario you want the model to learn.
- Naturally, this implies that if you want a frame in a video sequence, the neighboring 30/60 frames will be nearly identical. Picking identical frames may result in having a less diverse dataset and less impact on model performance.
- Primitive methods such as downsampling or random sampling may miss out on valuable information and a hope-based approach.
The Akridata Solution
Here’s how Akridata Data Explorer helps to solve this issue.
Exploring the Dataset
Get a holistic view of the data
Let’s take a look at the dataset we have. For the purpose of the illustration, we will refer to the nuScenes dataset.
The images are a random sample of the dataset which reflects different scenes – sunny days, traffic lights, pedestrian crossings, night shots, and rainy days.
Using Our Patch Search Feature
Find more images similar to your images of interest
Let’s suppose you want to use our Patch Search feature to find images that have a traffic light.
You will see that the search results have faithfully captured the neighbouring frames in the video sequence that have the traffic light.
Find more images similar to your images of interest
Let’s suppose you want to use our Patch Search feature to find images that have a traffic light.
You will see that the search results have faithfully captured the neighbouring frames in the video sequence that have the traffic light.
Applying Coreset Sampling
Capturing diversity of scenes
To capture the diversity of scenes that have the traffic light, we can apply Coreset sampling and reduce the dataset in the feature space.
As shown in the image, we select Coreset in the sampling and the sampling fraction to 0.01 (1%) in the Tunables panel.
Finding The Traffic Lights
Run Patch Search
Let’s suppose you want to use our Patch Search feature to find images that have a traffic light.
You will see that the search results have faithfully captured the neighbouring frames in the video sequence that have the traffic light.
Get Search results
Let’s suppose you want to use our Patch Search feature to find images that have a traffic light.
You will see that the search results have faithfully captured the neighbouring frames in the video sequence that have the traffic light.