Update README.md

README.md

@@ -13,7 +13,7 @@ datasets:
 ### Model Description
 [YOLOX](https://arxiv.org/abs/2107.08430) is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported.
 
-[YOLOXDetect-Pip](https://github.com/kadirnar/yolov6-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use.
+[YOLOXDetect-Pip](https://github.com/kadirnar/yolox-pip/): This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use.
 
 [Paper Repo]: Implementation of paper - [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
 
@@ -22,25 +22,20 @@ datasets:
 pip install yoloxdetect
 ```
 
-### Yolov6 Inference
+### Yolox Inference
 ```python
-from yoloxdetect import YoloxDetect
+from yoloxdetect import YoloxDetector
 from yolox.data.datasets import COCO_CLASSES
-
-model = YoloxDetect(
+model = YoloxDetector(
     model_path = "kadirnar/yolox_s-v0.1.1",
     config_path = "configs.yolox_s",
     device = "cuda:0",
-    classes = COCO_CLASSES,
-    confidence_threshold = 0.25,
-    nms_threshold = 0.45,
 )
 model.classes = COCO_CLASSES
 model.conf = 0.25
 model.iou = 0.45
 model.show = False
 model.save = True
-
 pred = model.predict(image='data/images', img_size=640)
 ```