Fix NAS post-processing (#19882)

Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/models/yolo-nas.md

@@ -6,6 +6,10 @@ keywords: YOLO-NAS, Deci AI, object detection, deep learning, Neural Architectur
 
 # YOLO-NAS
 
+!!! note "Important Update"
+
+    Please note that [Deci](https://www.linkedin.com/company/deciai/), the original creators of YOLO-NAS, have been acquired by NVIDIA. As a result, these models are no longer actively maintained by Deci. Ultralytics continues to support the usage of these models, but no further updates from the original team are expected.
+
 ## Overview
 
 Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced [Neural Architecture Search](https://www.ultralytics.com/glossary/neural-architecture-search-nas) technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and [accuracy](https://www.ultralytics.com/glossary/accuracy)-latency trade-offs, YOLO-NAS represents a major leap in object detection.

ultralytics/models/nas/model.py

@@ -81,6 +81,7 @@ class NAS(Model):
         self.model.pt_path = weights  # for export()
         self.model.task = "detect"  # for export()
         self.model.args = {**DEFAULT_CFG_DICT, **self.overrides}  # for export()
+        self.model.eval()
 
     def info(self, detailed: bool = False, verbose: bool = True):
         """

ultralytics/models/nas/predict.py

@@ -2,16 +2,15 @@
 
 import torch
 
-from ultralytics.engine.predictor import BasePredictor
-from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
 from ultralytics.utils import ops
 
 
-class NASPredictor(BasePredictor):
+class NASPredictor(DetectionPredictor):
     """
     Ultralytics YOLO NAS Predictor for object detection.
 
-    This class extends the `BasePredictor` from Ultralytics engine and is responsible for post-processing the
+    This class extends the `DetectionPredictor` from Ultralytics engine and is responsible for post-processing the
     raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
     scaling the bounding boxes to fit the original image dimensions.
 
@@ -38,23 +37,4 @@ class NASPredictor(BasePredictor):
         # Convert boxes from xyxy to xywh format and concatenate with class scores
         boxes = ops.xyxy2xywh(preds_in[0][0])
         preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
-
-        # Apply non-maximum suppression to filter overlapping detections
-        preds = ops.non_max_suppression(
-            preds,
-            self.args.conf,
-            self.args.iou,
-            agnostic=self.args.agnostic_nms,
-            max_det=self.args.max_det,
-            classes=self.args.classes,
-        )
-
-        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
-            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
-
-        results = []
-        for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
-            # Scale bounding boxes to match original image dimensions
-            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
-            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
-        return results
+        return super().postprocess(preds, img, orig_imgs)

ultralytics/models/nas/val.py

@@ -36,7 +36,4 @@ class NASValidator(DetectionValidator):
         """Apply Non-maximum suppression to prediction outputs."""
         boxes = ops.xyxy2xywh(preds_in[0][0])  # Convert bounding box format from xyxy to xywh
         preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)  # Concatenate boxes with scores and permute
-        return super().postprocess(
-            preds,
-            max_time_img=0.5,
-        )
+        return super().postprocess(preds)