  The TESS mission produces a large amount of time series data, only a small
fraction of which contain detectable exoplanetary transit signals. Deep
learning techniques such as neural networks have proved effective at
differentiating promising astrophysical eclipsing candidates from other
phenomena such as stellar variability and systematic instrumental effects in an
efficient, unbiased and sustainable manner. This paper presents a high quality
dataset containing light curves from the Primary Mission and 1st Extended
Mission full frame images and periodic signals detected via Box Least Squares
(Kov\'acs et al. 2002; Hartman 2012). The dataset was curated using a thorough
manual review process then used to train a neural network called
Astronet-Triage-v2. On our test set, for transiting/eclipsing events we achieve
a 99.6% recall (true positives over all data with positive labels) at a
precision of 75.7% (true positives over all predicted positives). Since 90% of
our training data is from the Primary Mission, we also test our ability to
generalize on held-out 1st Extended Mission data. Here, we find an area under
the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage (Yu
et al. 2019). On the TESS Object of Interest (TOI) Catalog through April 2022,
a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to
recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets
at an equal level of precision. In other words, upgrading to Astronet-Triage-v2
helps save at least 200 planet candidates from being lost. The new model is
currently used for planet candidate triage in the Quick-Look Pipeline (Huang et
al. 2020a,b; Kunimoto et al. 2021).
