SightX V2: A New Hope

A New Hope

I thought I was done with SightX as an Idea but I was never done with its underlying architecture and design. Now we Begin the journey of migrating SightX to new horizons. Below is a sneek peak but that is all for now.



Will be posting again soon > ~ <. Chao



 

Comments

Post a Comment

Popular posts from this blog

SightX: We Shipped It (The Journey Comes to an End)

Image
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...
Read more

SightX: Data Acquisition & Exploration - 88GB of Reality, Data Acquisition and the 73% Problem

Image
Day 6 & 7 They say you cannot build an inference engine without data. That is technically true, but what they don't tell you is that getting the data, understanding the data, and actually seeing what your model will be trained on is a journey of its own. And by journey, I mean downloading 88GB, realizing the unzipped dataset is now 100GB, spending an embarrassing amount of time wondering why Jupyter could not find my libraries, and then finally understanding what conda activate sightx actually meant. Why Data Exploration Comes Before Everything Else? The urge to skip straight to model training is real. The environment is set up, PyTorch confirmed the M4 MPS backend is active, and ResNet50 is waiting. But training a model on data you have not actually looked at is like building a house without checking if the land is stable. You will get something, but it probably won't be what you wanted. This phase was about three things: acquiring the EyePACS dataset from Kaggle, underst...
Read more

SightX: Teaching the Model to Learn - The Training Loop

Day 12 & 13   The architecture is built. The preprocessing pipeline is done. The model knows how to accept an image and produce five output logits . What it does not know yet is what those logits should mean. That is what the training loop is for. This phase is where the model stops being a static structure and starts being something that learns. It is also the phase with the most moving parts, the most ways to silently fail, and the most satisfying moment when the loss curve finally starts going down. The Dataset Class: Teaching PyTorch to Read Your Data Before the training loop can run, PyTorch needs to know how to load your data. That is not automatic. You must write a Dataset class that tells it exactly how to find an image, open it, and pair it with the correct label. It is essentially a contract given an index, returning a sample. The EyePACS dataset gives you a CSV file mapping image filenames to DR grades, and a folder of JPEG retinal scans. The Dataset class reads the ...
Read more