From 01c54eb2efe77687504bd107c7673bd82041aec9 Mon Sep 17 00:00:00 2001 From: Robert Sachunsky Date: Mon, 13 Apr 2026 01:15:25 +0200 Subject: [PATCH] reduce inference batch sizes to accommodate 8 GB VRAM (still pending a solution for flexible batch sizes) --- src/eynollah/eynollah.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py index 3b2d00e..a500925 100644 --- a/src/eynollah/eynollah.py +++ b/src/eynollah/eynollah.py @@ -918,7 +918,7 @@ class Eynollah: # img, self.model_zoo.get("region_fl_patched"), prediction_regions, _ = self.do_prediction_new_concept( True, img, self.model_zoo.get("region_fl"), - n_batch_inference=2, + n_batch_inference=1, thresholding_for_heading=True) else: prediction_regions = self.do_prediction( @@ -1129,7 +1129,7 @@ class Eynollah: return None, erosion_hurts, None, None, textline_mask_tot_ea, None #print("inside 2 ", time.time()-t_in) - kwargs = dict(n_batch_inference=2, + kwargs = dict(n_batch_inference=1, thresholding_for_artificial_class=True, threshold_art_class=self.threshold_art_class_layout) if num_col_classifier == 1 or num_col_classifier == 2: