Originally published on Medium .

Let me start with a confession: my first attempt at building a video time prediction model was a disaster.

I'd spent 3 months reading papers, collecting datasets, and training models.

But when I finally deployed it, the results were laughable.

I was trying to use a 3D CNN to extract features from video frames, and then feed those features into an LSTM to predict the time.

It sounded good on paper, but in practice, it was a mess.

The model was overfitting, underfitting, and just generally not working.

I tried tweaking the architecture, adjusting the hyperparameters, and even switching to a different dataset.

But no matter what I did, I just couldn't seem to get it to work.

And then, one day, I stumbled upon a paper about SlowFast networks, and everything changed.

The Before: When Everything Technically Works But Nothing Really Does

My model was technically working, in the sense that it was producing outputs and not crashing.

But in terms of actually predicting time…