Darkmatters

Day 17 - 21 The last blog ended with V2 sitting in a file on my laptop. A hundred megabytes of learned opinions about retinas, trained entirely on a MacBook Air that never once sounded like it was preparing for takeoff. It could look at an eye and tell you how bad the damage was. But it could not actually " talk " to anyone. It was a brain in a jar. Impressive at dinner parties. Useless in a clinic. The next phase was simple in theory and chaotic in practice: give the brain a body. Build the server. Build the interface. Wire everything together. Ship it. The Brain Needed a Safety Net Here is something I did not expect. The model's raw opinion is actually kind of dangerous to use directly. Think about it. The model trained on a dataset where 73% of images were healthy retinas. Remember the 73% problem from way back in the data exploration blog? It never went away. The model "learned" that bias. Deep in its 24.6 million parameters, it has a quiet preference toward...

Day 15 & 16 V1 was chaos. Beautiful, productive chaos. I typed python train.py , watched accuracy hit 67.99% at Epoch 7, then watched it overfit into the ground for thirteen more epochs while I ate noodles and questioned my life choices. It worked, but it was messy. V2 was different. V2 was what happens when you finally read the manual. After V1 finished, I did something I probably should have done before writing any code: I studied the people who actually solved this problem. The 2015 EyePACS Kaggle competition had 661 teams competing for $100K in prizes. The winners didn't just build better models, they built better ways of looking at the data . And that changed everything. The Kaggle Winner Who Changed My Mind Ben Graham won first place. His secret wasn't a fancy neural network. It was preprocessing. His insight: raw retinal photographs are a mess. Different cameras. Different lighting. Different zoom levels. Some images look reddish, some yellowish. Some retinas fill t...

Day 14 7:55 PM to 11:55 PM They say you never forget your first. Your first car. Your first apartment. Your first time training a neural network on 35,000 medical images while sitting on your desk eating noodles and questioning every life decision that led to this moment. Tonight was that night. It was 7:55 PM on a Thursday. I had spent two weeks building the pipeline, downloading the EyePACS dataset, writing the model architecture, debugging preprocessing transforms, and triple-checking my training loop for bugs that would only reveal themselves three hours into training. Everything was ready. The data was clean. The code was tested. The conda environment was activated. This time I remembered to activate it. I typed the command: python inference-engine/train.py And then the terminal did something beautiful and slightly unhinged. It printed the same line nine times: Training on device: mps Training on device: mps Training on device: mps ... Nine times. One for each DataLoader worker th...