diff --git a/4c_6c_regression.pt b/4c_6c_regression.pt
deleted file mode 100644
index f0471117437bd625786fc310fda9fd7f1a114e53..0000000000000000000000000000000000000000
--- a/4c_6c_regression.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:158f2a49f42e9e3ef40c12de1d86ddff9465f834cb8c006bb38a3fd6a8f99b15
-size 5998089
diff --git a/app.py b/app.py
index 532a79560552863ef2966f9734fb59cfd10582df..1cfe6600589a33245f1429317e5f9e794fe0dd2d 100644
--- a/app.py
+++ b/app.py
@@ -29,7 +29,7 @@ except Exception as e:
     ndvi_model = None
 
 try:
-    yolo_model = load_yolo_model("4c_6c_regression.pt")
+    yolo_model = load_yolo_model("best_yolo_model.pt")
     logger.info("YOLO model loaded successfully")
 except Exception as e:
     logger.error(f"Failed to load YOLO model: {e}")
diff --git a/best_yolo_model.pt b/best_yolo_model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..9d20312ea2be77a088057187f94e6613ed83b009
--- /dev/null
+++ b/best_yolo_model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5be2b24cd635b1e8f6e6b56f0ec49ba7751f627439670597807cec7e6f6fdf3e
+size 45153455
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diff --git a/ultralytics/data/__pycache__/augment.cpython-312.pyc b/ultralytics/data/__pycache__/augment.cpython-312.pyc
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--- a/ultralytics/data/__pycache__/augment.cpython-312.pyc
+++ b/ultralytics/data/__pycache__/augment.cpython-312.pyc
@@ -1,3 +1,3 @@
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+oid sha256:9f7f6b212a14a5068a553d00be673361718d528f2e6b2e324ea2a2e113a00191
 size 139583
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diff --git a/ultralytics/data/__pycache__/build.cpython-312.pyc b/ultralytics/data/__pycache__/build.cpython-312.pyc
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diff --git a/ultralytics/data/__pycache__/converter.cpython-312.pyc b/ultralytics/data/__pycache__/converter.cpython-312.pyc
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diff --git a/ultralytics/data/__pycache__/loaders.cpython-312.pyc b/ultralytics/data/__pycache__/loaders.cpython-312.pyc
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diff --git a/ultralytics/data/__pycache__/utils.cpython-312.pyc b/ultralytics/data/__pycache__/utils.cpython-312.pyc
index e36193347505e59b9b144bb2a193822154226dea..34ec372d4708cf982805c5a5c6d6e723cc9e144a 100644
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diff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py
index d1802aab41431f6cfbf12436b0d9a2c29120a549..d65c8523b02b165e385e6fa9cc8bb6b58eb09dfc 100644
--- a/ultralytics/data/augment.py
+++ b/ultralytics/data/augment.py
@@ -1541,7 +1541,12 @@ class LetterBox:
         self.stride = stride
         self.center = center  # Put the image in the middle or top-left
 
-    def __call__(self, labels=None, image=None):
+    def __call__(self, labels=None, image=None): # gets image during inference (1544, 2064, 4) but not during validation
+        # during validation, labels["img"].shape = (479, 640, 4)
+        # function returns labels with labels["img"].shape = (512, 672, 4)
+
+        # during inference the image is already provided, image.shape = (1544, 2064, 4)
+        # returns image with image.shape = (480, 640, 4)
         """
         Resizes and pads an image for object detection, instance segmentation, or pose estimation tasks.
 
diff --git a/ultralytics/data/base.py b/ultralytics/data/base.py
index be3350ce7ee4e825d45575574281eb51e84cfc54..5467cfb8f106800b52df73166c0286b74ac9059e 100644
--- a/ultralytics/data/base.py
+++ b/ultralytics/data/base.py
@@ -160,8 +160,10 @@ class BaseDataset(Dataset):
                     LOGGER.warning(f"{self.prefix}WARNING ⚠️ Removing corrupt *.npy image file {fn} due to: {e}")
                     Path(fn).unlink(missing_ok=True)
                     im = cv2.imread(f, cv2.IMREAD_UNCHANGED)  # Load with all channels, including NDVI if present
+                    # assert 0, '=========== Read unchanged image ============='
             else:  # read image
                 im = cv2.imread(f, cv2.IMREAD_UNCHANGED)  # Load with all channels, including NDVI if present
+                # assert 0, '=========== Read unchanged image ============='
             if im is None:
                 raise FileNotFoundError(f"Image Not Found {f}")
 
@@ -208,6 +210,7 @@ class BaseDataset(Dataset):
         f = self.npy_files[i]
         if not f.exists():
             np.save(f.as_posix(), cv2.imread(self.im_files[i], cv2.IMREAD_UNCHANGED), allow_pickle=False)
+            # assert 0, '=========== Read unchanged image ============='
 
 
     def check_cache_disk(self, safety_margin=0.5):
@@ -219,6 +222,7 @@ class BaseDataset(Dataset):
         for _ in range(n):
             im_file = random.choice(self.im_files)
             im = cv2.imread(im_file, cv2.IMREAD_UNCHANGED)  # Load with all channels, including NDVI if present
+            # assert 0, '=========== Read unchanged image ============='
             if im is None:
                 continue
             b += im.nbytes
@@ -244,6 +248,7 @@ class BaseDataset(Dataset):
         n = min(self.ni, 30)  # extrapolate from 30 random images
         for _ in range(n):
             im = cv2.imread(random.choice(self.im_files), cv2.IMREAD_UNCHANGED)  # sample image
+            # assert 0, '=========== Read unchanged image ============='
             if im is None:
                 continue
             ratio = self.imgsz / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
diff --git a/ultralytics/data/dataset.py b/ultralytics/data/dataset.py
index a5a13e4a4f52fbcb52b970b7876390ef0a08c165..6da1a06554482d3c4be264e4fcd2b9c1949320d2 100644
--- a/ultralytics/data/dataset.py
+++ b/ultralytics/data/dataset.py
@@ -560,12 +560,15 @@ class ClassificationDataset:
         if self.cache_ram:
             if im is None:  # Warning: two separate if statements required here, do not combine this with previous line
                 im = self.samples[i][3] = cv2.imread(f, cv2.IMREAD_UNCHANGED)
+                # assert 0, '=========== Read unchanged image ============='
         elif self.cache_disk:
             if not fn.exists():  # load npy
                 np.save(fn.as_posix(), cv2.imread(f, cv2.IMREAD_UNCHANGED), allow_pickle=False)
+                # assert 0, '=========== Read unchanged image ============='
             im = np.load(fn)
         else:  # read image
             im = cv2.imread(f, cv2.IMREAD_UNCHANGED)  # BGR
+            # assert 0, '=========== Read unchanged image ============='
         # Convert NumPy array to PIL image
         im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
         sample = self.torch_transforms(im)
diff --git a/ultralytics/data/loaders.py b/ultralytics/data/loaders.py
index 5e649d3ec843a351dded35925d45c91e250a35b3..b6db7d5aabcf229380d647205422b0d68f2e925e 100644
--- a/ultralytics/data/loaders.py
+++ b/ultralytics/data/loaders.py
@@ -585,14 +585,37 @@ class LoadTensor:
         return self.bs
 
 
-def autocast_list(source):
+def autocast_list(source): # this function (as per experimentation) isn't called during validation or training, just inference
     """Merges a list of sources into a list of numpy arrays or PIL images for Ultralytics prediction."""
     files = []
     # assert 0, f'in def autocast_list() source: {source}'
+    # import itertools
     for im in source:
         if isinstance(im, (str, Path)):  # filename or uri
             # assert 0, f'im -> isinstance(im, (str, Path)) = True im=  {im}'
-            files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im))
+            # assert 0, 'is the same func used in validation?'
+            # if str(im).startswith("http"):
+            # files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im))
+
+            # pilimg = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im)
+            # orig_np_im = np.array(pilimg)
+            
+            # for perm in itertools.permutations([0, 1, 2, 3]):
+            #     np_im = orig_np_im[..., perm]
+            #     # np_im = np.ascontiguousarray(np_im)
+            #     files.append(Image.fromarray(np_im))
+
+            # np_im = orig_np_im[..., (0, 3, 2, 1)]
+            # np_im = np.ascontiguousarray(np_im)
+            # files.append(np_im)
+            if str(im).startswith("http"):
+                img = Image.open(requests.get(im, stream=True).raw)
+            else:
+                img = cv2.imread(im, cv2.IMREAD_UNCHANGED)
+                img = np.array(img)
+                img = img[..., (3,2,1,0)]
+                img = np.ascontiguousarray(img)
+            files.append(img)
         elif isinstance(im, (Image.Image, np.ndarray)):  # PIL or np Image
             files.append(im)
         else:
@@ -605,7 +628,7 @@ def autocast_list(source):
     # import numpy as np
     # np_arr = np.array(files[0])
     # print(f'np_arr.shape: {np_arr.shape}')
-    # assert 0, 'bye bye sucker'
+    # assert 0, 'bye bye'
     return files
 
 
diff --git a/ultralytics/engine/__pycache__/__init__.cpython-312.pyc b/ultralytics/engine/__pycache__/__init__.cpython-312.pyc
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diff --git a/ultralytics/engine/__pycache__/exporter.cpython-312.pyc b/ultralytics/engine/__pycache__/exporter.cpython-312.pyc
index 15623bdfa5115d5fa9a19bd0ec7940d54e9b0fde..0a53dcbbe9eaa4c5fa0b852503cc512a865a1268 100644
--- a/ultralytics/engine/__pycache__/exporter.cpython-312.pyc
+++ b/ultralytics/engine/__pycache__/exporter.cpython-312.pyc
@@ -1,3 +1,3 @@
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-oid sha256:d43eee4a96cf869121d1bc528ae7c8eed58d0f81baea7930b416f4a122dcf549
+oid sha256:b45eb6f61fa73af51b7cc3a8f73f05a4692f429d0d123cacdc8ee2af11704d11
 size 101374
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diff --git a/ultralytics/engine/__pycache__/predictor.cpython-312.pyc b/ultralytics/engine/__pycache__/predictor.cpython-312.pyc
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diff --git a/ultralytics/engine/__pycache__/trainer.cpython-312.pyc b/ultralytics/engine/__pycache__/trainer.cpython-312.pyc
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diff --git a/ultralytics/engine/__pycache__/validator.cpython-312.pyc b/ultralytics/engine/__pycache__/validator.cpython-312.pyc
index a308cda4a2c7af77d5eebf180a94e5850ff5f633..cda32722b659ba5c6f425f6d7eda5e74266ef1f2 100644
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diff --git a/ultralytics/engine/model.py b/ultralytics/engine/model.py
index 0416a2365efeaa7ad065d9064e4cd38d2c036928..fe4bf4261638f7f186cc9b98599532f99e5c40e6 100644
--- a/ultralytics/engine/model.py
+++ b/ultralytics/engine/model.py
@@ -639,7 +639,7 @@ class Model(torch.nn.Module):
         args = {**self.overrides, **custom, **kwargs, "mode": "val"}  # highest priority args on the right
 
         validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks)
-        validator(model=self.model)
+        validator(model=self.model) # everything during validation happens here
         self.metrics = validator.metrics
         return validator.metrics
 
diff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py
index f6aa67be4a3390f1720e1dc283d8a253d55f198e..d3a76aeb4dd5f3b4bbfcb1fbcfb79c34dd8f62d1 100644
--- a/ultralytics/engine/predictor.py
+++ b/ultralytics/engine/predictor.py
@@ -117,7 +117,7 @@ class BasePredictor:
         self._lock = threading.Lock()  # for automatic thread-safe inference
         callbacks.add_integration_callbacks(self)
 
-    def preprocess(self, im):
+    def preprocess(self, im): # this preprocess is called during inference
         # assert 0, 'def preprocess() called in predictory.py file'
         # the line above confirmed that this function is not called during
         # model.train() or model.val()
@@ -140,9 +140,37 @@ class BasePredictor:
             
             im = np.stack(self.pre_transform(im))
             print(f'im.shape : {im.shape} in def preprocess() predictor.py after self.pre_transform()')
-            # im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
-            im = im[..., [2, 1, 0, 3]].transpose((0, 3, 1, 2)) # adjusted to handle NDVI band. [B, G, R, NDVI] to [R, G, B, NDVI]
-            # the transpose formats    BHWC  to   BCHW 
+            # im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w) 
+            # im = im.transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w) 
+            
+            im = im.transpose((0, 3, 1, 2)) # adjusted to handle NDVI band. [B, G, R, NDVI] to [R, G, B, NDVI]
+            # im = im[..., [0, 1, 2, 3]].transpose((0, 3, 1, 2)) # adjusted to handle NDVI band. [B, G, R, NDVI] to [R, G, B, NDVI]
+            
+            # im = im[..., [0, 1, 2, 3]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [0, 1, 3, 2]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [0, 2, 1, 3]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [0, 2, 3, 1]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [0, 3, 1, 2]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [0, 3, 2, 1]].transpose((0, 3, 1, 2)) # ✅❌
+            # im = im[..., [1, 0, 2, 3]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [1, 0, 3, 2]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [1, 2, 0, 3]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [1, 2, 3, 0]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [1, 3, 0, 2]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [1, 3, 2, 0]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [2, 0, 1, 3]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [2, 0, 3, 1]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [2, 1, 0, 3]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [2, 1, 3, 0]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [2, 3, 0, 1]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [2, 3, 1, 0]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [3, 0, 1, 2]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [3, 0, 2, 1]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [3, 1, 0, 2]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [3, 1, 2, 0]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [3, 2, 0, 1]].transpose((0, 3, 1, 2)) # ❌
+            # im = im[..., [3, 2, 1, 0]].transpose((0, 3, 1, 2)) # ❌
+
             im = np.ascontiguousarray(im)  # contiguous
             # print(f'im.shape: {im.shape} in def preprocess() predictor.py')
             im = torch.from_numpy(im)
@@ -276,6 +304,22 @@ class BasePredictor:
                     # print(f'l276 pred.py im0s[0].shape: {im0s[0].shape}')
                     im = self.preprocess(im0s)
 
+                # preds[0].shape
+                # torch.Size([1, 43, 6300])
+
+                # preds[1][0][0].shape
+                # torch.Size([1, 71, 60, 80])
+                # preds[1][0][1].shape
+                # torch.Size([1, 71, 30, 40])
+                # preds[1][0][2].shape
+                # torch.Size([1, 71, 15, 20])
+
+                # preds[1][1][0].shape
+                # torch.Size([32, 6300])
+                
+                # preds[1][2][0].shape
+                # torch.Size([32, 120, 160])
+                
                 # Inference
                 with profilers[1]:
                     preds = self.inference(im, *args, **kwargs)
diff --git a/ultralytics/engine/validator.py b/ultralytics/engine/validator.py
index 1b851ee14405986fbda9a6fff57f64be3d66ca1a..f0b276cf87fad9f56dff56963b10723527a18d2d 100644
--- a/ultralytics/engine/validator.py
+++ b/ultralytics/engine/validator.py
@@ -177,7 +177,7 @@ class BaseValidator:
             self.batch_i = batch_i
             # Preprocess
             with dt[0]:
-                batch = self.preprocess(batch)
+                batch = self.preprocess(batch) # preprocessing the validation data, models hasn't done anything yet
 
             # Inference
             with dt[1]:
@@ -190,7 +190,8 @@ class BaseValidator:
 
             # Postprocess
             with dt[3]:
-                preds = self.postprocess(preds)
+                preds = self.postprocess(preds) # in validation, preds does not contain the correct
+                # length of 39 per box, 4*coords, growth_val, 6*conf per each class, class (from 0-5, owing to 6 classes), 32*masks 
 
             self.update_metrics(preds, batch)
             if self.args.plots and batch_i < 3:
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diff --git a/ultralytics/models/yolo/detect/val.py b/ultralytics/models/yolo/detect/val.py
index 20fbee9b175dc7ace5cd10efed786d60d94264ff..4ea34a88a3ff4202b650bec8dd7f601e5bd20cc1 100644
--- a/ultralytics/models/yolo/detect/val.py
+++ b/ultralytics/models/yolo/detect/val.py
@@ -51,7 +51,7 @@ class DetectionValidator(BaseValidator):
         """Preprocesses batch of images for YOLO training."""
         batch["img"] = batch["img"].to(self.device, non_blocking=True)
         batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
-        for k in ["batch_idx", "cls", "bboxes"]:
+        for k in ["batch_idx", "cls", "bboxes", "growth_stage"]: # also move growth stage to 'device'
             batch[k] = batch[k].to(self.device)
 
         if self.args.save_hybrid:
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diff --git a/ultralytics/models/yolo/segment/predict.py b/ultralytics/models/yolo/segment/predict.py
index 0edddac048773b25389d7583707137645a8cae76..c919b56dacc10ee4d85b6b0fde37f26fadce9cda 100644
--- a/ultralytics/models/yolo/segment/predict.py
+++ b/ultralytics/models/yolo/segment/predict.py
@@ -28,8 +28,8 @@ class SegmentationPredictor(DetectionPredictor):
     def postprocess(self, preds, img, orig_imgs):
         """Applies non-max suppression and processes detections for each image in an input batch."""
         # tuple if PyTorch model or array if exported
-        protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]
-        return super().postprocess(preds[0], img, orig_imgs, protos=protos)
+        protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1] # preds[1][-1].shape :-> torch.Size([1, 32, 120, 160])
+        return super().postprocess(preds[0], img, orig_imgs, protos=protos) # preds[0].shape :-> torch.Size([1, 43, 6300])
 
     def construct_results(self, preds, img, orig_imgs, protos):
         """
diff --git a/ultralytics/models/yolo/segment/val.py b/ultralytics/models/yolo/segment/val.py
index b6844cd2333fc42bb3d00de376ca3e1e7de7f6e5..21232e42a05e7022b20772097eaa30fddb8ac209 100644
--- a/ultralytics/models/yolo/segment/val.py
+++ b/ultralytics/models/yolo/segment/val.py
@@ -84,7 +84,9 @@ class SegmentationValidator(DetectionValidator):
     def _prepare_pred(self, pred, pbatch, proto):
         """Prepares a batch for training or inference by processing images and targets."""
         predn = super()._prepare_pred(pred, pbatch)
-        pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=pbatch["imgsz"])
+        pred_masks = self.process(proto, pred[:, 7:], pred[:, :4], shape=pbatch["imgsz"]) # changed the '6' to '7' 
+        # since start index of masks is one position later due to the growth stage
+        # 4*coords, growth_val, conf, class, 32*mask values 
         return predn, pred_masks
 
     def update_metrics(self, preds, batch):
@@ -115,17 +117,17 @@ class SegmentationValidator(DetectionValidator):
             gt_masks = pbatch.pop("masks")
             # Predictions
             if self.args.single_cls:
-                pred[:, 5] = 0
-            predn, pred_masks = self._prepare_pred(pred, pbatch, proto)
-            stat["conf"] = predn[:, 4]
-            stat["pred_cls"] = predn[:, 5]
+                pred[:, 6] = 0 # changed to 6 since class is at index 6 now due to gs
+            predn, pred_masks = self._prepare_pred(pred, pbatch, proto) # corrected indexing within this function to accomodate the additional growth stage
+            stat["conf"] = predn[:, 5] # conf is at '5' now. '4' previuosly
+            stat["pred_cls"] = predn[:, 6] # conf is at '6' now. 5 previously
 
             # Evaluate
             if nl:
-                stat["tp"] = self._process_batch(predn, bbox, cls)
+                stat["tp"] = self._process_batch(predn, bbox, cls) # corrected too for the 'gs'
                 stat["tp_m"] = self._process_batch(
                     predn, bbox, cls, pred_masks, gt_masks, self.args.overlap_mask, masks=True
-                )
+                ) # this function has been corrected too
             if self.args.plots:
                 self.confusion_matrix.process_batch(predn, bbox, cls)
 
@@ -205,12 +207,13 @@ class SegmentationValidator(DetectionValidator):
         else:  # boxes
             iou = box_iou(gt_bboxes, detections[:, :4])
 
-        return self.match_predictions(detections[:, 5], gt_cls, iou)
+        return self.match_predictions(detections[:, 6], gt_cls, iou) 
+        # changed to '6' since class now lies at index '6'. was previously '5'
 
     def plot_val_samples(self, batch, ni):
         """Plots validation samples with bounding box labels."""
         plot_images(
-            batch["img"],
+            batch["img"], # change to batch["img"][:,  :3, :, :, ]
             batch["batch_idx"],
             batch["cls"].squeeze(-1),
             batch["bboxes"],
@@ -224,7 +227,7 @@ class SegmentationValidator(DetectionValidator):
     def plot_predictions(self, batch, preds, ni):
         """Plots batch predictions with masks and bounding boxes."""
         plot_images(
-            batch["img"],
+            batch["img"], # change to batch["img"][:,  :3, :, :, ]
             *output_to_target(preds[0], max_det=15),  # not set to self.args.max_det due to slow plotting speed
             torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
             paths=batch["im_file"],
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diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index b17251c6d4b57915521ab71a74d88dea11f487fa..01acf997dabeab97aed8c3c9ab2e060c62b9f9fe 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -532,7 +532,7 @@ class AutoBackend(nn.Module):
 
         self.__dict__.update(locals())  # assign all variables to self
 
-    def forward(self, im, augment=False, visualize=False, embed=None):
+    def forward(self, im, augment=False, visualize=False, embed=None): # used during both inference and validation , preds = model(batch["img"], augment=augment) in engine/validator.py calls this
         """
         Runs inference on the YOLOv8 MultiBackend model.
 
@@ -552,7 +552,7 @@ class AutoBackend(nn.Module):
             im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
 
         # PyTorch
-        if self.pt or self.nn_module:
+        if self.pt or self.nn_module: # this if statement is executed during validation (on the current config, local + cpu only)
             y = self.model(im, augment=augment, visualize=visualize, embed=embed)
 
         # TorchScript
@@ -740,7 +740,12 @@ class AutoBackend(nn.Module):
             if len(self.names) == 999 and (self.task == "segment" or len(y) == 2):  # segments and names not defined
                 nc = y[0].shape[1] - y[1].shape[1] - 4  # y = (1, 32, 160, 160), (1, 116, 8400)
                 self.names = {i: f"class{i}" for i in range(nc)}
-            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
+            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] # this is executed during validation
+            # len(y)
+            # 2
+            # y[0].shape
+            # torch.Size([5, 43, 7056])
+            
         else:
             return self.from_numpy(y)
 
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diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 96a366494c228db4238c14487aeea10fc82720d2..2d7afd915d5046e4448b6ac7d4d14beeedca6731 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -140,7 +140,7 @@ class BaseModel(torch.nn.Module):
         if isinstance(x, dict):  # for cases of training and validating while training.
             temp_loss =  self.loss(x, *args, **kwargs)
             return temp_loss
-        return self.predict(x, *args, **kwargs)
+        return self.predict(x, *args, **kwargs) # called by validation too
 
     def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
         """
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diff --git a/ultralytics/utils/loss.py b/ultralytics/utils/loss.py
index dfec5ea2ec8f16193f2b9bae469bcce8b01cb7ec..3c2ac29acbb3aa34c7e5cce5247e4434b9e06ea6 100644
--- a/ultralytics/utils/loss.py
+++ b/ultralytics/utils/loss.py
@@ -282,6 +282,23 @@ class v8SegmentationLoss(v8DetectionLoss):
                     (self.reg_max * 4, 1,  self.nc), 1 # the 1 in (self.reg_max * 4, 1,  self.nc) for extra regression scalar value
         )
 
+        # for the bounding boxes
+        # preds[0][0].shape
+        # torch.Size([16, 71, 40, 40])
+        # preds[0][1].shape
+        # torch.Size([16, 71, 20, 20])
+        # preds[0][2].shape
+        # torch.Size([16, 71, 10, 10])
+
+        # shapes of pred_distri, pred_growth_val, pred_scores
+        #      [16, 64, 2100],  [16, 1, 2100],    [16, 6, 2100]
+        # it may seem odd predictinng 64 values for one box's coordiniates
+        # but yolo predict something like a histogram i.e a distribution instead of a single value
+        # 16 values for one coordinate, so 4 coordinates * 16 per coordinate = 64 values
+
+        # pred_masks.shape
+        # torch.Size([16, 2100, 32])
+
         # B, grids, ..
         pred_scores = pred_scores.permute(0, 2, 1).contiguous()
         pred_distri = pred_distri.permute(0, 2, 1).contiguous()
@@ -295,7 +312,13 @@ class v8SegmentationLoss(v8DetectionLoss):
         # Targets
         try:
             batch_idx = batch["batch_idx"].view(-1, 1)
-            targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["growth_stage"].view(-1, 1), batch["bboxes"]), 1) # to accomodate the additional growth_stage
+
+            batch_idx = batch_idx.to(self.device)
+            batch_cls = batch["cls"].view(-1, 1).to(self.device)
+            batch_growth_stage = batch["growth_stage"].view(-1, 1).to(self.device)
+            batch_bboxes = batch["bboxes"].to(self.device)
+
+            targets = torch.cat((batch_idx, batch_cls, batch_growth_stage, batch_bboxes), 1) # to accomodate the additional growth_stage
             targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
             gt_labels, gt_growth, gt_bboxes = targets.split((1, 1, 4), 2)  # cls, xyxy
             mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
@@ -327,7 +350,7 @@ class v8SegmentationLoss(v8DetectionLoss):
         # Cls loss
         # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
         loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
-        loss[4] = self.mse(pred_growth_val, target_growth)
+        loss[4] = self.mse(pred_growth_val, target_growth.to(dtype))
 
         if fg_mask.sum():
             # Bbox loss
@@ -357,7 +380,7 @@ class v8SegmentationLoss(v8DetectionLoss):
         loss[1] *= self.hyp.box  # seg gain
         loss[2] *= self.hyp.cls  # cls gain
         loss[3] *= self.hyp.dfl  # dfl gain
-        loss[4] *= self.hyp.cls # growth stage regression loss
+        loss[4] *= self.hyp.cls * 0.5 # growth stage regression loss
         return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
 
     @staticmethod
diff --git a/ultralytics/utils/metrics.py b/ultralytics/utils/metrics.py
index 43e73304b1db8daef56e7d40b8f8b1773fa83011..86a8781fc5f195327ff8b8d2680d803c61b87b11 100644
--- a/ultralytics/utils/metrics.py
+++ b/ultralytics/utils/metrics.py
@@ -336,8 +336,8 @@ class ConfusionMatrix:
         """
         if gt_cls.shape[0] == 0:  # Check if labels is empty
             if detections is not None:
-                detections = detections[detections[:, 4] > self.conf]
-                detection_classes = detections[:, 5].int()
+                detections = detections[detections[:, 5] > self.conf] # changed to '5', was prevously '4'
+                detection_classes = detections[:, 6].int() # changed to '6', was previously '5'. to accomodate for the new gs
                 for dc in detection_classes:
                     self.matrix[dc, self.nc] += 1  # false positives
             return
@@ -347,10 +347,11 @@ class ConfusionMatrix:
                 self.matrix[self.nc, gc] += 1  # background FN
             return
 
-        detections = detections[detections[:, 4] > self.conf]
+        detections = detections[detections[:, 5] > self.conf] # changed to '5', was prevously '4'
         gt_classes = gt_cls.int()
-        detection_classes = detections[:, 5].int()
-        is_obb = detections.shape[1] == 7 and gt_bboxes.shape[1] == 5  # with additional `angle` dimension
+        detection_classes = detections[:, 6].int() # changed to '6', was previously '5'
+        is_obb = detections.shape[1] == 8 and gt_bboxes.shape[1] == 6  # with additional `angle` dimension 
+        # 7 and 5 changed to 8 and 6 in the above line
         iou = (
             batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))
             if is_obb
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index e37e8834ed953d0a3eeb8da1845049486fc00a4d..e96bd752a7ae5f6c49016f81aa6bfe5e786671d7 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -179,7 +179,7 @@ def nms_rotated(boxes, scores, threshold=0.45, use_triu=True):
 
 
 def non_max_suppression(
-    prediction,
+    prediction, 
     conf_thres=0.25,
     iou_thres=0.45,
     classes=None,
@@ -236,13 +236,13 @@ def non_max_suppression(
         prediction = prediction[0]  # select only inference output
     if classes is not None:
         classes = torch.tensor(classes, device=prediction.device)
-
-    if prediction.shape[-1] == 6 or end2end:  # end-to-end model (BNC, i.e. 1,300,6)
-        output = [pred[pred[:, 4] > conf_thres][:max_det] for pred in prediction]
+    # prediction shape [1, 43, 6300], the 43 is as follows. 4 coords, growth val, 6 pseudo probs, 32 masks 
+    if prediction.shape[-1] == 7 or end2end:  # end-to-end model (BNC, i.e. 1,300,6) # changed '6' to '7' since a box is now represented by 7 values
+        output = [pred[pred[:, 5] > conf_thres][:max_det] for pred in prediction] # changed '4' to '5' since confidence is one position ahead, growth_val is at index 4
         if classes is not None:
-            output = [pred[(pred[:, 5:6] == classes).any(1)] for pred in output]
+            output = [pred[(pred[:, 6:7] == classes).any(1)] for pred in output] # changed '5:6' to '6:7', class is one position ahead
         return output
-
+    # preds.shape = [1, 43, 6300], one vector of length 43 == on bbox + it's mask. 43 = 4*coords + growth_val + 6*classes pseudo probs, 32*masks 
     bs = prediction.shape[0]  # batch size (BCN, i.e. 1,84,6300)
     nc = nc or (prediction.shape[1] - 4)  # number of classes
     nm = prediction.shape[1] - nc - 4 - 1 # number of masks # -1 is done since an extra channel is for the growth stage
@@ -263,7 +263,7 @@ def non_max_suppression(
             prediction = torch.cat((xywh2xyxy(prediction[..., :4]), prediction[..., 4:]), dim=-1)  # xywh to xyxy
 
     t = time.time()
-    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
+    output = [torch.zeros((0, 7 + nm), device=prediction.device)] * bs # '6' changed to '7' for the additional scalar growth stage value
     for xi, x in enumerate(prediction):  # image index, image inference
         # Apply constraints
         # x[((x[:, 2:4] < min_wh) | (x[:, 2:4] > max_wh)).any(1), 4] = 0  # width-height
@@ -273,6 +273,7 @@ def non_max_suppression(
         if labels and len(labels[xi]) and not rotated: # since this code wasn't executed while testing, it won't be changed
             # it could be possible that configuration might change and this might be executed in which case byzantine faults might occur
             # or the code could crash too
+            assert 0, 'Code point reached not executed in normal code flow while debugging locally'
             lb = labels[xi]
             v = torch.zeros((len(lb), nc + nm + 4), device=x.device)
             v[:, :4] = xywh2xyxy(lb[:, 1:5])  # box
@@ -288,7 +289,26 @@ def non_max_suppression(
 
         if multi_label: # False in 'normal' execution
             i, j = torch.where(cls > conf_thres)
-            x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float(), mask[i]), 1)
+            """ As seen below, torch.where returns two tensors which tell the
+            positions in cls (a 2D array) which are greater than confidence threshold (a scalar)
+            >>> arr1 = torch.randn(3, 2)
+            >>> arr2 = torch.ones(3,2) * 0.5
+            >>> arr1
+            tensor([[ 1.4419,  1.5131],
+                    [-0.0594,  0.6871],
+                    [-1.1018,  0.2445]])
+            >>> arr2
+            tensor([[0.5000, 0.5000],
+                    [0.5000, 0.5000],
+                    [0.5000, 0.5000]])
+            >>> torch.where (arr1 > arr2)
+            (tensor([0, 0, 1]), tensor([0, 1, 1]))
+            """ # The line below can increase the number of boxes since a box can belong 
+            #to more than one classes at the same time (assumed by the line below), 
+            # boxes can increase by a factor of 'nc' at max
+            # '4' changed to '5' since the class indices start one step later due to gs
+            x = torch.cat((box[i], x[i, 4, None], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
+            # 4 box coords ^^^
         else:  # best class only
             conf, j = cls.max(1, keepdim=True)
             x = torch.cat((box, gr_val, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] # adding the growth val
@@ -297,7 +317,7 @@ def non_max_suppression(
 
         # Filter by class
         if classes is not None:
-            x = x[(x[:, 5:6] == classes).any(1)]
+            x = x[(x[:, 6:7] == classes).any(1)] # changed from '5:6' to '6:7'
 
         # Check shape
         n = x.shape[0]  # number of boxes
@@ -312,6 +332,7 @@ def non_max_suppression(
         # index 5 is taken by growth value
         scores = x[:, 5]  # scores # '4' changed to 5 since conf score is at index 5 now
         if rotated:
+            assert 0, 'Code point reached not executed in normal code flow while debugging locally'
             boxes = torch.cat((x[:, :2] + c, x[:, 2:4], x[:, -1:]), dim=-1)  # xywhr
             i = nms_rotated(boxes, scores, iou_thres)
         else:
diff --git a/ultralytics/utils/plotting.py b/ultralytics/utils/plotting.py
index 12fb6b583903061fbea3e637459fcbc5424eb3db..3384b699f88e1fd12a2a41eaa35c36fe2c51a7d5 100644
--- a/ultralytics/utils/plotting.py
+++ b/ultralytics/utils/plotting.py
@@ -420,10 +420,10 @@ class Annotator:
 
         im_gpu = im_gpu.flip(dims=[0])  # flip channel
         im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
-        im_gpu = im_gpu * inv_alpha_masks[-1] + mcs
+        im_gpu = im_gpu[:, :, :3] * inv_alpha_masks[-1] + mcs # the image contains 4 channels and we only want the rgb channels, not the .tiff channel
         im_mask = im_gpu * 255
         im_mask_np = im_mask.byte().cpu().numpy()
-        self.im[:] = im_mask_np if retina_masks else ops.scale_image(im_mask_np, self.im.shape)
+        self.im[:, :, :3] = im_mask_np if retina_masks else ops.scale_image(im_mask_np, self.im.shape) # the image contains 4 channels and we only want the rgb channels, not the .tiff channel
         if self.pil:
             # Convert im back to PIL and update draw
             self.fromarray(self.im)
@@ -544,7 +544,7 @@ class Annotator:
 
     def save(self, filename="image.jpg"):
         """Save the annotated image to 'filename'."""
-        cv2.imwrite(filename, np.asarray(self.im))
+        cv2.imwrite(filename, np.asarray(self.im[:, :, :3])) # the image contains 4 channels and we only want the rgb channels, not the .tiff channel
 
     @staticmethod
     def get_bbox_dimension(bbox=None):
@@ -981,7 +981,7 @@ def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False,
         Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0)  # save RGB
     return crop
 
-
+# images, [batch_id, class_id, x, y, w, h, conf, growth_stage], ........
 @threaded
 def plot_images(
     images: Union[torch.Tensor, np.ndarray],
@@ -989,6 +989,7 @@ def plot_images(
     cls: Union[torch.Tensor, np.ndarray],
     bboxes: Union[torch.Tensor, np.ndarray] = np.zeros(0, dtype=np.float32),
     confs: Optional[Union[torch.Tensor, np.ndarray]] = None,
+    # growth_stage: Union[torch.Tensor, np.ndarray],
     masks: Union[torch.Tensor, np.ndarray] = np.zeros(0, dtype=np.uint8),
     kpts: Union[torch.Tensor, np.ndarray] = np.zeros((0, 51), dtype=np.float32),
     paths: Optional[List[str]] = None,
@@ -1279,15 +1280,16 @@ def plot_tune_results(csv_file="tune_results.csv"):
 
 
 def output_to_target(output, max_det=300):
-    """Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
+    """Convert model output to target format [batch_id, class_id, x, y, w, h, conf, growth_stage] for plotting."""
     targets = []
     for i, o in enumerate(output):
-        box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
+        box, growth_stage, conf, cls = o[:max_det, :7].cpu().split((4, 1, 1, 1), 1) # was previously box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1), changed for the additional growth stage
         j = torch.full((conf.shape[0], 1), i)
-        targets.append(torch.cat((j, cls, ops.xyxy2xywh(box), conf), 1))
+        targets.append(torch.cat((j, cls, ops.xyxy2xywh(box), conf, growth_stage), 1))
     targets = torch.cat(targets, 0).numpy()
-    return targets[:, 0], targets[:, 1], targets[:, 2:-1], targets[:, -1]
-
+    return targets[:, 0], targets[:, 1], targets[:, 2:-2], targets[:, -2] # this works too if you don't want the growth stage in the output plot
+    # return targets[:, 0], targets[:, 1], targets[:, 2:-2], targets[:, -2], targets[:, -1]
+    # batch ids ^^^       ^^^ class_ids   ^^^ bboxes        ^^^ confs       ^^^ growth_stages
 
 def output_to_rotated_target(output, max_det=300):
     """Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""