diff --git a/codes/trainer/injectors/base_injectors.py b/codes/trainer/injectors/base_injectors.py
index bdfdb272..1030d8c7 100644
--- a/codes/trainer/injectors/base_injectors.py
+++ b/codes/trainer/injectors/base_injectors.py
@@ -1,13 +1,12 @@
-import os
 import random
 
 import torch.nn
-import torchvision
+from kornia.augmentation import RandomResizedCrop
 from torch.cuda.amp import autocast
 
-from utils.weight_scheduler import get_scheduler_for_opt
-from trainer.losses import extract_params_from_state
 from trainer.inject import Injector
+from trainer.losses import extract_params_from_state
+from utils.weight_scheduler import get_scheduler_for_opt
 
 
 # Uses a generator to synthesize an image from [in] and injects the results into [out]
@@ -372,34 +371,17 @@ class MixAndLabelInjector(Injector):
         return {self.out_labels: labels, self.output: output}
 
 
-# Doesn't inject. Rather saves images that meet a specified criteria. Useful for performing classification filtering
-# using ExtensibleTrainer.
-class SaveImages(Injector):
+# Randomly performs a uniform resize & crop from a base image.
+# Never resizes below input resolution or messes with the aspect ratio.
+class RandomCropInjector(Injector):
     def __init__(self, opt, env):
         super().__init__(opt, env)
-        self.logits = opt['logits']
-        self.target = opt['target']
-        self.thresh = opt['threshold']
-        self.index = 0
-        self.rindex = 0
-        self.run_id = random.randint(0, 999999)
-        self.savedir = opt['savedir']
-        os.makedirs(self.savedir, exist_ok=True)
-        self.rejectdir = opt['negatives']
-        if self.rejectdir:
-            os.makedirs(self.rejectdir, exist_ok=True)
-        self.softmax = torch.nn.Softmax(dim=1)
+        dim_in = opt['dim_in']
+        dim_out = opt['dim_out']
+        scale = dim_out / dim_in
+        self.operator = RandomResizedCrop(size=(dim_out, dim_out), scale=(scale, 1), ratio=(1,1),
+                                          resample='NEAREST')
 
     def forward(self, state):
-        logits = self.softmax(state[self.logits])
-        images = state[self.input]
-        bs = images.shape[0]
-        for b in range(bs):
-            if logits[b][self.target] > self.thresh:
-                torchvision.utils.save_image(images[b], os.path.join(self.savedir, f'{self.run_id}_{self.index}.jpg'))
-                self.index += 1
-            elif self.rejectdir:
-                torchvision.utils.save_image(images[b],
-                                             os.path.join(self.rejectdir, f'{self.run_id}_{self.rindex}.jpg'))
-                self.rindex += 1
-        return {}
\ No newline at end of file
+        return {self.output: self.operator(self.input)}
+