diff --git a/configs/upload_tapecam_mirflickr_ambient.yaml b/configs/upload_tapecam_mirflickr_ambient.yaml
index 0d62238a..f1196ca3 100644
--- a/configs/upload_tapecam_mirflickr_ambient.yaml
+++ b/configs/upload_tapecam_mirflickr_ambient.yaml
@@ -4,13 +4,13 @@ defaults:
   - _self_
 
 repo_id: "Lensless/TapeCam-Mirflickr-Ambient"
-n_files: null
+n_files: 16000
 test_size: 0.15
 # -- to match TapeCam without ambient light
 split: 100   # "first: first `nfiles*test_size` for test, `int`: test_size*split for test (interleaved) as if multimask with this many masks
 
 lensless:
-  dir: data/100_samples
+  dir: /dev/shm/tape_15k_ambient/all_measured_20240805-143922
   ambient: True
   ext: ".png"
 
diff --git a/lensless/recon/utils.py b/lensless/recon/utils.py
index 65c6e238..9c75877f 100644
--- a/lensless/recon/utils.py
+++ b/lensless/recon/utils.py
@@ -875,15 +875,26 @@ def train_epoch(self, data_loader):
             # get batch
             flip_lr = None
             flip_ud = None
-            if self.train_random_flip:
-                X, y, psfs, flip_lr, flip_ud = batch
-                psfs = psfs.to(self.device)
-            elif self.train_multimask:
-                X, y, psfs = batch
+            X = batch[0].to(self.device)
+            y = batch[1].to(self.device)
+            if self.train_multimask or self.train_random_flip:
+                psfs = batch[2]
                 psfs = psfs.to(self.device)
             else:
-                X, y = batch
                 psfs = None
+            if self.train_random_flip:
+                flip_lr = batch[3]
+                flip_ud = batch[4]
+
+            # if self.train_random_flip:
+            #     X, y, psfs, flip_lr, flip_ud = batch
+            #     psfs = psfs.to(self.device)
+            # elif self.train_multimask:
+            #     X, y, psfs = batch
+            #     psfs = psfs.to(self.device)
+            # else:
+            #     X, y = batch
+            #     psfs = None
 
             random_rotate = False
             if self.random_rotate:
diff --git a/scripts/recon/train_learning_based.py b/scripts/recon/train_learning_based.py
index 77e6e7d0..6a048d13 100644
--- a/scripts/recon/train_learning_based.py
+++ b/scripts/recon/train_learning_based.py
@@ -31,7 +31,7 @@
 import wandb
 import logging
 import hydra
-from hydra.utils import get_original_cwd
+from hydra.utils import get_original_cwd, to_absolute_path
 import os
 import numpy as np
 import time
@@ -227,7 +227,7 @@ def train_learned(config):
                 display_res=config.files.image_res,
                 alignment=config.alignment,
                 bg_snr_range=config.files.background_snr_range,  # TODO check if correct
-                bg_fp=config.files.background_fp,
+                bg_fp=to_absolute_path(config.files.background_fp),
             )
 
         else:
@@ -251,7 +251,7 @@ def train_learned(config):
                 simulate_lensless=config.files.simulate_lensless,
                 random_flip=config.files.random_flip,
                 bg_snr_range=config.files.background_snr_range,
-                bg_fp=config.files.background_fp,
+                bg_fp=to_absolute_path(config.files.background_fp),
             )
 
         test_set = HFDataset(
@@ -271,7 +271,7 @@ def train_learned(config):
             n_files=config.files.n_files,
             simulation_config=config.simulation,
             bg_snr_range=config.files.background_snr_range,
-            bg_fp=config.files.background_fp,
+            bg_fp=to_absolute_path(config.files.background_fp),
             force_rgb=config.files.force_rgb,
             simulate_lensless=False,  # in general evaluate on measured (set to False)
         )
@@ -341,6 +341,7 @@ def train_learned(config):
                 return_items = test_set[_idx]
                 lensless = return_items[0]
                 lensed = return_items[1]
+
                 if test_set.bg_sim is not None:
                     background = return_items[-1]
                 if test_set.multimask or test_set.random_flip:
@@ -379,6 +380,9 @@ def train_learned(config):
                 if config.files.random_rotate or config.files.random_shifts:
                     save_image(psf_recon[0].cpu().numpy(), f"psf_{_idx}.png")
 
+                save_image(lensed[0].cpu().numpy(), f"lensed_{_idx}.png")
+                save_image(lensless[0].cpu().numpy(), f"lensless_raw_{_idx}.png")
+
                 # Reconstruct and plot image
                 reconstruct_save(
                     _idx,
@@ -395,7 +399,6 @@ def train_learned(config):
                     rotate_angle,
                     shift,
                 )
-                save_image(lensed[0].cpu().numpy(), f"lensed_{_idx}.png")
                 if test_set.bg_sim is not None:
                     # Reconstruct and plot background subtracted image
                     reconstruct_save(
@@ -665,9 +668,6 @@ def reconstruct_save(
     res_np = res_np / res_np.max()
     lensed_np = lensed[0].cpu().numpy()
 
-    lensless_np = lensless[0].cpu().numpy()
-    save_image(lensless_np, f"lensless_raw_{_idx}.png")
-
     # -- plot lensed and res on top of each other
     cropped = False
     if hasattr(test_set, "alignment"):