Thanks! The quantization tradeoffs have been a grind. We do not have an exact number but we found that a few thousand images was not enough once you account for the variance on farm. Lighting changes throughout the day, water clarity shifts between feedings, fish density varies by tank. Early on our calibration sets were too homogenous and the INT8 models would work great in testing and then fall apart when conditions shifted.

We also found that segmentation required significantly fewer images compared to keypoint pose detection models. Segmentation generalizes faster since you are just finding body boundaries. Keypoints are more finicky because anatomical landmarks vary a lot more across species, life stages, and body deformation while swimming. We had to be much more intentional about diversity in the keypoint training data. What made the difference overall was building calibration sets that intentionally captured edge cases. Low light, high turbidity, dense occlusion, different life stages. We also started stratifying by time of day and tank conditions rather than just grabbing random frames. It is still not perfect but the models are much more stable now.

This is a great comment. You are absolutely right about the data opportunity. The industry is so data sparse right now that even basic measurements at scale would be a step change. We are seeing that firsthand with our customer. They went from sampling a few dozen fish by hand to continuous measurement and the insights are already compounding.

Thank you for the fish pump link. We have looked at pump based systems as a way to create controlled measurement environments. You get consistent lighting, predictable fish orientation, and the fish are already moving through a constrained path. The challenge is you are still dealing with water turbidity, particulates, and bubbles in the flow which can mess with imaging. It is better than open water but not a free pass on the vision problems.

We have also been looking at pescalators which use an Archimedes screw design to lift fish out of the water. Some setups combine this with anesthetization for operations that require handling. The tradeoff is you are adding stress and complexity but you get a much cleaner imaging environment. There is no single right answer here and the best approach depends on the species, life stage, and what you are trying to measure. This is definitely technology that will develop over time as the industry matures.

What species are you working with in your aquaponics setup?

Thanks! Yes turbidity has been one of our bigger challenges. Water clarity can shift dramatically throughout the day depending on feeding, fish activity, and weather. We have had to build our calibration datasets to capture that variance otherwise the quantized models degrade fast in production. We are also experimenting with different lighting setups to cut through particulate but it is still a work in progress :)

Great question. We are building our entire labeling and data management system in house. Early on we tried existing platforms but they did not fit our workflow. We have a lot of video data and need custom labeling for things like keypoints, body outlines, and deformity classification that off the shelf tools do not handle well. Building it ourselves is cheaper at our scale, gives us tighter integration between labeling, training pipelines, and deployment, and lets us iterate faster. We can assign tasks to annotators, version datasets, and push models to edge devices from one system. When you are trying to close the loop between data collection on farm and deployment you cannot afford fragmented tooling.

Shinkei definitely has cool tech! Aquaculture has already surpassed commercial fishing in terms of production and has become the cheapest source of protein in many countries. We are excited to help the industry grow even further.

I like it, but it should just be visual. It jacks my default scrollpad/mouse behavior, which is the annoying part.

Agreed. Feels icky. Made me want to leave the page as quickly as possible again.

^ no way this was tested by anyone with eyes before it was deployed

I am a fan of the fish cursor. We should make the internet quirky again. This is like a modern take on Geocities websites, and they should do things like fish cursors now while they still can before a board of VCs comes in and makes them remove the fish cursor.