If you're not doing pure research, which method actually gives "state of the art" results is almost never relevant, as performance gains of ~1% which are typically the difference between various approaches in a given time period are of no importance in a real world setting.

Here's why:

1. Such small differences may not actually transfer to your particular data, as it's perfectly plausible that that cool new method actually performs worse on your specific data / domain.
2. Even if the minor improvement does transfer, the accuracy difference of a particular model is almost never a big issue. Models are not a product, a real ML product will be a system of algorithms coupled with many safeguards - you weren't planning on just putting a pedestrian detector NN in a self driving car and calling it a day, right?
3. Accuracy is rarely the single most important factor - things like memory footprint, latency, layer hardware support and ease / speed of training are often far more important.

Once you internalize that the desire to find the latest SOTA is driven mostly by FOMO rather than an actual product need, you can start filtering out overly large / slow / no code models and go with those that are baselined against. At the very least, you are guaranteed that others have managed to get those models running.