added yolonas coco model inference

README.md

@@ -191,7 +191,7 @@ You can Inference your **YOLO-NAS** model with **Single Command Line**
 
   `-n`, `--num`: Number of classes the model trained on <br>
   `-m`, `--model`: Model type (choices: `yolo_nas_s`, `yolo_nas_m`, `yolo_nas_l`) <br>
-  `-w`, `--weight`: path to trained model weight <br>
+  `-w`, `--weight`: path to trained model weight, for COCO model: `coco` <br>
   `-s`, `--source`: video path/cam-id/RTSP <br>
   `-c`, `--conf`: model prediction confidence (0<conf<1) <br>
   `--save`: to save video <br>
@@ -200,7 +200,13 @@ You can Inference your **YOLO-NAS** model with **Single Command Line**
 </details>
 
 **Example:**
+```bash
+# For COCO YOLO-NAS Model
+python3 inference.py --model yolo_nas_s --weight coco --source 0                              # Camera
+python3 inference.py --model yolo_nas_m --weight coco --source /test/video.mp4 --conf 0.66    # video
 ```
+
+```bash
 python3 inference.py --num 3 --model yolo_nas_m --weight /runs/train4/ckpt_best.pth --source /test/video.mp4 --conf 0.66           # video
                                                                                     --source /test/sample.jpg --conf 0.5 --save    # Image save
                                                                                     --source /test/video.mp4 --conf 0.75 --hide    # to save and hide video window

inference.py

@@ -9,7 +9,7 @@
 
 
 ap = argparse.ArgumentParser()
-ap.add_argument("-n", "--num", type=int, required=True,
+ap.add_argument("-n", "--num", type=int, required=False,
                 help="number of classes the model trained on")
 ap.add_argument("-m", "--model", type=str, default='yolo_nas_s',
                 choices=['yolo_nas_s', 'yolo_nas_m', 'yolo_nas_l'],
@@ -52,12 +52,17 @@ def get_bbox(img):
     return labels
 
 
+# Load COCO YOLO-NAS Model
+if args["weight"] == "coco":
+    model = models.get(args['model'], pretrained_weights="coco")
 # Load YOLO-NAS Model
-model = models.get(
-    args['model'],
-    num_classes=args['num'], 
-    checkpoint_path=args["weight"]
-)
+else:
+    model = models.get(
+        args['model'],
+        num_classes=args['num'], 
+        checkpoint_path=args["weight"]
+    )
+
 model = model.to("cuda" if torch.cuda.is_available() else "cpu")
 class_names = model.predict(np.zeros((1,1,3)), conf=args['conf'])._images_prediction_lst[0].class_names
 print('Class Names: ', class_names)