Knowledge Base

Deep Learning Requirements

Graphics Card (GPU)

Neural networks make calculations over huge data matrices, and therefore require substantial computation power. In order for the Deep learning tracking technique to work in EthoVision XT, you need a Graphics Processing Unit (GPU, or graphics card) that is able to sustain those computations.

Furthermore, Deep learning makes use of TensorRT software development kit (version 8.6.1.6) which is built on the cuDNN deep neural network library, which in its turn relies on the CUDA computing platform. For this reason, the GPU driver must support CUDA runtime version 12.2.

Supported GPUs

• Quadro P2200
• T1000
• GeForce RTX 4060
• th Gen boards are NOT YET supported

Video Source

Choose:

• When tracking one subject per arena: Live tracking limited to up to four arenas, or From video file.
• When tracking two subjects per arena: From video file.

Tracking two subjects live using Deep learning may lead to a high number of missing samples, depending on the power of GPU. Always test your setup first. If you note a significant number of missing samples (e.g. > 5%), do not track live. Instead, record video first, then acquire the trial later using Deep learning.

Sample Rate

Accuracy of individual discrimination may go down when reducing the sample rate, for example from 25 to 12.5 samples/s. We strongly recommend that you track at 25 or 30 fps (unless using DavioVision, then use 60 fps). Choose the sample rate in the Detection Settings.

Subject Species, Color and Size

• The neural network has been trained with images of rats and mice of uniform color.
• For hooded rats, like Lister and Long-Evans rats:
  • When tracking one subject per arena: Select Hooded rats in the Detection settings. This loads a neural network model specific for those animals.
  • When tracking two subjects per arena: The neural network for two interacting subjects has not been trained for the fur patterns of hooded rats. If you want to use hooded rats, test pairs of animals before the actual experiments, to check that the software produces acceptable results.
• The apparent length of the subjects should be at least 10% of the size of arena. We recommend that the apparent length of the subject's body is at least 120 pixels for rats and 50 pixels for mice (nose to tail-base).

Number of Subjects and Arenas

You can select Deep learning as body point detection technique in experiments with:

• One subject per arena, in a maximum of four arenas.
• Two subjects per arena, in a maximum of four arenas.

IMPORTANT: Always draw an arena in the Arena Settings for optimal results.

Video Image

• When working with one arena at a time, choose a resolution of 640 x 480 or higher.
• When working with two to four arenas, choose a resolution of 1280x960 or 1280x1024, or similar.
• In any case, use video of resolution equal to or higher than PAL/NTSC (PAL: 704 x 576; NTSC/EIA: 640 x 480). A higher resolution is not necessarily better, also considering that it makes tracking slower when tracking offline, or can cause missing samples when tracking live. First try a low resolution, and switch to a higher resolution if the results are not good.
• For live tracking, not all video resolutions and frame rates are compatible with Deep learning. See Test results and then click on the camera type for configurations tested with Live tracking combined with Deep learning: GigE cameras, USB 3.0 cameras, and Analog cameras.
• EthoVision XT converts video to grayscale before feeding it to the neural network. Therefore, both monochrome and color video work fine.

Video Length

• One subject per arena: No restrictions. Maximal trial duration tested 72 hours.
• Two subjects per arena: We recommend performing trials of at least five minutes. Maximal trial duration tested 1 hour.

During the trials, the two subjects should be separated for at least three minutes. If the subjects are in contact for most of the trial duration, individual recognition may fail. Also consider that, with two-subject tracking:

• EthoVision XT saves additional files during acquisition, which increase the storage space needed on your PC. A 1-hour video produces 10 MB of additional files.
• With long trials, the marker could change or droppings would likely cumulate in the arena. Both factors could potentially interfere with marker recognition.

Source: EthoVision XT 18 - GigE Install and Setup, Noldus Information Technology