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new file mode 100644
index 00000000..d6456956
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+++ b/models/object_detection_nanodet/LICENSE
@@ -0,0 +1,202 @@
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diff --git a/models/object_detection_nanodet/NanodetPlus.py b/models/object_detection_nanodet/NanodetPlus.py
new file mode 100644
index 00000000..84544e22
--- /dev/null
+++ b/models/object_detection_nanodet/NanodetPlus.py
@@ -0,0 +1,138 @@
+import cv2
+import numpy as np
+
+class NanoDet():
+    def __init__(self, modelPath, prob_threshold=0.35, iou_threshold=0.6):
+        with open('coco.names', 'rt') as f:
+            self.classes = f.read().rstrip('\n').split('\n')
+
+        self.num_classes = len(self.classes)
+        self.strides = (8, 16, 32, 64)
+        self.image_shape = (416, 416)
+        self.reg_max = 7
+        self.prob_threshold = prob_threshold
+        self.iou_threshold = iou_threshold
+        self.project = np.arange(self.reg_max + 1)
+        self.mean = np.array([103.53, 116.28, 123.675], dtype=np.float32).reshape(1, 1, 3)
+        self.std = np.array([57.375, 57.12, 58.395], dtype=np.float32).reshape(1, 1, 3)
+        self.net = cv2.dnn.readNet(modelPath)
+
+        self.anchors_mlvl = []
+        for i in range(len(self.strides)):
+            featmap_size = (int(self.image_shape[0] / self.strides[i]), int(self.image_shape[1] / self.strides[i]))
+            stride = self.strides[i]
+            feat_h, feat_w = featmap_size
+            shift_x = np.arange(0, feat_w) * stride
+            shift_y = np.arange(0, feat_h) * stride
+            xv, yv = np.meshgrid(shift_x, shift_y)
+            xv = xv.flatten()
+            yv = yv.flatten()
+            cx = xv + 0.5 * (stride-1)
+            cy = yv + 0.5 * (stride - 1)
+            #anchors = np.stack((cx, cy), axis=-1)
+            anchors = np.column_stack((cx, cy))
+            self.anchors_mlvl.append(anchors)
+
+    def softmax_func(self,x, axis=0):
+        x_exp = np.exp(x)
+        x_sum = np.sum(x_exp, axis=axis, keepdims=True)
+        s = x_exp / x_sum
+        return s
+
+    def pre_process(self, img):
+        img = img.astype(np.float32)
+        img = (img - self.mean) / self.std
+        blob = cv2.dnn.blobFromImage(img)
+        return blob
+
+    def infer(self, srcimg, keep_ratio=True):
+        top, left, newh, neww = 0, 0, self.image_shape[0], self.image_shape[1]
+        if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
+            hw_scale = srcimg.shape[0] / srcimg.shape[1]
+            if hw_scale > 1:
+                newh, neww = self.image_shape[0], int(self.image_shape[1] / hw_scale)
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                left = int((self.image_shape[1] - neww) * 0.5)
+                img = cv2.copyMakeBorder(img, 0, 0, left, self.image_shape[1] - neww - left, cv2.BORDER_CONSTANT,
+                                         value=0)  # add border
+            else:
+                newh, neww = int(self.image_shape[0] * hw_scale), self.image_shape[1]
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                top = int((self.image_shape[0] - newh) * 0.5)
+                img = cv2.copyMakeBorder(img, top, self.image_shape[0] - newh - top, 0, 0, cv2.BORDER_CONSTANT, value=0)
+        else:
+            img = cv2.resize(srcimg, self.image_shape, interpolation=cv2.INTER_AREA)
+
+        blob = self.pre_process(img)
+        self.net.setInput(blob)
+        outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+        det_bboxes, det_conf, det_classid = self.post_process(outs)
+        ratioh,ratiow = srcimg.shape[0]/newh,srcimg.shape[1]/neww
+
+        return left, top, ratioh, ratiow, det_bboxes, det_conf, det_classid
+
+
+    def post_process(self, preds):
+        cls_scores, bbox_preds = preds[::2], preds[1::2]
+        rescale = False
+        scale_factor = 1
+        bboxes_mlvl = []
+        scores_mlvl = []
+        for stride, cls_score, bbox_pred, anchors in zip(self.strides, cls_scores, bbox_preds, self.anchors_mlvl):
+            if cls_score.ndim==3:
+                cls_score = cls_score.squeeze(axis=0)
+            if bbox_pred.ndim==3:
+                bbox_pred = bbox_pred.squeeze(axis=0)
+            bbox_pred = self.softmax_func(bbox_pred.reshape(-1, self.reg_max + 1), axis=1)
+            bbox_pred = np.dot(bbox_pred, self.project).reshape(-1,4)
+            bbox_pred *= stride
+
+            nms_pre = 1000
+            if nms_pre > 0 and cls_score.shape[0] > nms_pre:
+                max_scores = cls_score.max(axis=1)
+                topk_inds = max_scores.argsort()[::-1][0:nms_pre]
+                anchors = anchors[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                cls_score = cls_score[topk_inds, :]
+
+            points = anchors
+            distance = bbox_pred
+            max_shape=self.image_shape
+            x1 = points[:, 0] - distance[:, 0]
+            y1 = points[:, 1] - distance[:, 1]
+            x2 = points[:, 0] + distance[:, 2]
+            y2 = points[:, 1] + distance[:, 3]
+
+            if max_shape is not None:
+                x1 = np.clip(x1, 0, max_shape[1])
+                y1 = np.clip(y1, 0, max_shape[0])
+                x2 = np.clip(x2, 0, max_shape[1])
+                y2 = np.clip(y2, 0, max_shape[0])
+
+            #bboxes = np.stack([x1, y1, x2, y2], axis=-1)
+            bboxes = np.column_stack([x1, y1, x2, y2])
+            bboxes_mlvl.append(bboxes)
+            scores_mlvl.append(cls_score)
+
+        bboxes_mlvl = np.concatenate(bboxes_mlvl, axis=0)
+        if rescale:
+            bboxes_mlvl /= scale_factor
+        scores_mlvl = np.concatenate(scores_mlvl, axis=0)
+        bboxes_wh = bboxes_mlvl.copy()
+        bboxes_wh[:, 2:4] = bboxes_wh[:, 2:4] - bboxes_wh[:, 0:2]
+        classIds = np.argmax(scores_mlvl, axis=1)
+        confidences = np.max(scores_mlvl, axis=1)
+
+        indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.prob_threshold, self.iou_threshold)
+
+        if len(indices)>0:
+            det_bboxes = bboxes_mlvl[indices[:]]
+            det_conf = confidences[indices[:]]
+            det_classid = classIds[indices[:]]
+
+        else:
+            det_bboxes = np.array([])
+            det_conf = np.array([])
+            det_classid = np.array([])
+
+        return det_bboxes.astype(np.float32), det_conf, det_classid
diff --git a/models/object_detection_nanodet/README.md b/models/object_detection_nanodet/README.md
new file mode 100644
index 00000000..c78f506e
--- /dev/null
+++ b/models/object_detection_nanodet/README.md
@@ -0,0 +1,140 @@
+# Nanodet
+
+Nanodet: NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training.
+
+#### Model metrics:
+Average Precision and Recall values observed for COCO dataset classes are showed below 
+
+<table>
+<tr><th>Average Precision </th><th>Average Recall</th></tr>
+<tr><td>
+  
+|  area  |  IoU  |  Average Precision(AP)  |
+|:-------|:------|:------------------------|
+|  all  |  0.50:0.95  |  0.304  |
+|  all  |  0.50  |  0.459  |
+|  all  |  0.75  |  0.317  |
+|  small  |  0.50:0.95  |  0.107  |
+|  medium  |  0.50:0.95  |  0.322  |
+|  large  |  0.50:0.95  |  0.478  |
+ 
+ </td><td>
+
+  area  |  IoU  |  Average Recall  |
+|:-------|:------|:----------------|
+|  all  |  0.50:0.95  |  0.278  |
+|  all  |  0.50:0.95  |  0.434  |
+|  all  |  0.50:0.95 |  0.462  |
+|  small  |  0.50:0.95  |  0.198  |
+|  medium  |  0.50:0.95  |  0.510  |
+|  large  |  0.50:0.95  |  0.702  |
+</td></tr> </table>
+
+
+## Demo
+
+Run the following command to try the demo: 
+```shell
+# Nanodet inference on image input
+python demo.py --model /path/to/model/ --input_type image --image_path /path/to/image/
+
+# Nanodet inference on video input
+python demo.py --model /path/to/model/ --input_type video 
+
+#Saving outputs 
+#Image output
+python demo.py --model /path/to/model/ --input_type image --image_path /path/to/image/ --save True
+
+#Video output
+python demo.py --model /path/to/model/ --input_type video --save True
+```
+Note: 
+- By default input_type: image
+- image result saved as "result.jpg"
+- webcam result saved as "Webcam_result.mp4"
+
+
+## Results
+
+Here are some of the sample results that were observed using the model,
+
+<p float="left">
+  <img src="./examples/results/TestResult1.png" width="450" height="450">
+  <img src="./examples/results/TestResult2.png" width="450" height="450">
+</p>
+  
+Video inference result,
+<p align="center">
+  <img src="https://github.com/Sidd1609/opencv_zoo/blob/master/models/object_detection_nanodet/examples/results/WebCamR.gif" width="650" height="450">
+</p>
+  
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+
+## Reference
+
+- Nanodet: https://zhuanlan.zhihu.com/p/306530300
+- Nanodet Plus: https://zhuanlan.zhihu.com/p/449912627
+- Nanodet weight and scripts for training: https://github.com/RangiLyu/nanodet
+
+
+#### Note:
+
+- This version of nanodet: Nanodet-m-plus-1.5x_416
+- The model was trained on COCO 2017 dataset, link to dataset: https://cocodataset.org/#download
+- Below, we have results of COCO data inference
+
+| class         | AP50   | mAP   | class          | AP50   | mAP   |
+|:--------------|:-------|:------|:---------------|:-------|:------|
+| person        | 67.5   | 41.8  | bicycle        | 35.4   | 18.8  |
+| car           | 45.0   | 25.4  | motorcycle     | 58.9   | 33.1  |
+| airplane      | 77.3   | 58.9  | bus            | 68.8   | 56.4  |
+| train         | 81.1   | 60.5  | truck          | 38.6   | 24.7  |
+| boat          | 35.5   | 16.7  | traffic light  | 30.5   | 14.0  |
+| fire hydrant  | 69.8   | 54.5  | stop sign      | 60.9   | 54.6  |
+| parking meter | 55.1   | 38.5  | bench          | 26.8   | 15.9  |
+| bird          | 38.3   | 23.6  | cat            | 82.5   | 62.1  |
+| dog           | 67.0   | 51.4  | horse          | 64.3   | 44.2  |
+| sheep         | 57.7   | 35.8  | cow            | 61.2   | 39.9  |
+| elephant      | 79.9   | 56.2  | bear           | 81.8   | 63.0  |
+| zebra         | 85.4   | 59.5  | giraffe        | 84.1   | 59.9  |
+| backpack      | 12.4   | 5.9   | umbrella       | 46.5   | 28.8  |
+| handbag       | 8.4    | 3.7   | tie            | 35.2   | 19.6  |
+| suitcase      | 38.1   | 23.8  | frisbee        | 60.7   | 43.9  |
+| skis          | 30.5   | 14.5  | snowboard      | 32.3   | 18.2  |
+| sports ball   | 37.6   | 24.5  | kite           | 51.1   | 30.4  |
+| baseball bat  | 28.9   | 13.6  | baseball glove | 40.1   | 21.6  |
+| skateboard    | 59.4   | 35.2  | surfboard      | 47.9   | 26.6  |
+| tennis racket | 55.2   | 30.5  | bottle         | 34.7   | 20.2  |
+| wine glass    | 27.8   | 16.3  | cup            | 35.5   | 23.7  |
+| fork          | 25.9   | 14.8  | knife          | 10.9   | 5.6   |
+| spoon         | 8.7    | 4.1   | bowl           | 42.8   | 29.4  |
+| banana        | 35.5   | 18.5  | apple          | 19.4   | 12.9  |
+| sandwich      | 46.7   | 33.4  | orange         | 35.2   | 25.9  |
+| broccoli      | 36.4   | 19.1  | carrot         | 30.9   | 17.8  |
+| hot dog       | 42.7   | 29.3  | pizza          | 61.0   | 44.9  |
+| donut         | 47.3   | 34.0  | cake           | 39.9   | 24.4  |
+| chair         | 28.8   | 16.1  | couch          | 60.5   | 42.6  |
+| potted plant  | 29.0   | 15.3  | bed            | 63.3   | 46.0  |
+| dining table  | 39.6   | 27.5  | toilet         | 71.3   | 55.3  |
+| tv            | 66.5   | 48.1  | laptop         | 62.6   | 46.9  |
+| mouse         | 63.5   | 44.1  | remote         | 19.8   | 10.3  |
+| keyboard      | 62.1   | 41.5  | cell phone     | 33.7   | 22.8  |
+| microwave     | 54.9   | 39.6  | oven           | 48.1   | 30.4  |
+| toaster       | 30.0   | 16.4  | sink           | 44.5   | 27.8  |
+| refrigerator  | 63.2   | 46.1  | book           | 18.4   | 7.3   |
+| clock         | 57.8   | 35.8  | vase           | 33.7   | 22.1  |
+| scissors      | 27.8   | 17.8  | teddy bear     | 54.1   | 35.4  |
+| hair drier    | 2.9    | 1.1   | toothbrush     | 13.1   | 8.2   |
+
+
+#### Contributor Details
+
+- Google Summer of Code'22
+- Contributor: Sri Siddarth Chakaravarthy
+- Github Profile: https://github.com/Sidd1609
+- Organisation: OpenCV
+- Project: Lightweight object detection models using OpenCV 
diff --git a/models/object_detection_nanodet/coco.names b/models/object_detection_nanodet/coco.names
new file mode 100644
index 00000000..16315f2b
--- /dev/null
+++ b/models/object_detection_nanodet/coco.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
\ No newline at end of file
diff --git a/models/object_detection_nanodet/demo.py b/models/object_detection_nanodet/demo.py
new file mode 100644
index 00000000..69caee69
--- /dev/null
+++ b/models/object_detection_nanodet/demo.py
@@ -0,0 +1,122 @@
+import cv2
+import numpy as np
+import argparse
+import time
+import moviepy.video.io.ImageSequenceClip
+from NanodetPlus import NanoDet
+
+backends = [cv2.dnn.DNN_BACKEND_OPENCV, cv2.dnn.DNN_BACKEND_CUDA]
+targets = [cv2.dnn.DNN_TARGET_CPU, cv2.dnn.DNN_TARGET_CUDA, cv2.dnn.DNN_TARGET_CUDA_FP16]
+help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
+help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
+
+try:
+    backends += [cv2.dnn.DNN_BACKEND_TIMVX]
+    targets += [cv2.dnn.DNN_TARGET_NPU]
+    help_msg_backends += "; {:d}: TIMVX"
+    help_msg_targets += "; {:d}: NPU"
+except:
+    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/Sidd1609/5bb321c8733110ed613ec120c7c02e41 for more information.')
+
+with open('coco.names', 'rt') as f:
+    classes = f.read().rstrip('\n').split('\n')
+
+def drawPred(frame, classId, conf, left, top, right, bottom):
+    cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 0), thickness=2)
+    #label = '%.2f' % conf
+    label =''
+    label = '%s%s' % (classes[classId], label)
+    labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+    top = max(top, labelSize[1])
+    # cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine), (255,255,255), cv.FILLED)
+    cv2.putText(frame, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), thickness=2)
+    return frame
+
+if __name__=='__main__':
+    parser = argparse.ArgumentParser(description='Nanodet inference using OpenCV an contribution by Sri Siddarth Chakaravarthy part of GSOC_2022')
+    parser.add_argument('--model', type=str, default='object_detection_nanodet-plus-m-1.5x-416.onnx', help="Path to the model")
+    parser.add_argument('--input_type', type=str, default='image', help="Input types: image or video")
+    parser.add_argument('--image_path', type=str, default='test2.jpg', help="Image path")
+    parser.add_argument('--confidence', default=0.35, type=float, help='Class confidence')
+    parser.add_argument('--nms', default=0.6, type=float, help='Enter nms IOU threshold')
+    parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
+    args = parser.parse_args()
+    model_net = NanoDet(modelPath= args.model ,prob_threshold=args.confidence, iou_threshold=args.nms)
+
+    if (args.input_type=="image"):
+        image = cv2.imread(args.image_path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        srcimg = image
+        drawimg = srcimg.copy()
+        a = time.time()
+        #image = model_net.infer(image)
+        left, top, ratioh, ratiow, det_bboxes, det_conf, det_classid = model_net.infer(image)
+        b = time.time()
+        print('Inference_Time:'+str(b-a)+' secs')
+        for i in range(det_bboxes.shape[0]):
+            xmin, ymin, xmax, ymax = max(int((det_bboxes[i,0] - left) * ratiow), 0), max(int((det_bboxes[i,1] - top) * ratioh), 0), min(
+                int((det_bboxes[i,2] - left) * ratiow), srcimg.shape[1]), min(int((det_bboxes[i,3] - top) * ratioh), srcimg.shape[0])
+            frame = drawPred(drawimg, det_classid[i], det_conf[i], xmin, ymin, xmax, ymax)
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        cv2.namedWindow(args.image_path, cv2.WINDOW_AUTOSIZE)
+        cv2.imshow(args.image_path, frame)
+        cv2.waitKey(0)
+
+        if args.save:
+            print('Resutls saved to result.jpg\n')
+            cv2.imwrite('result.jpg', frame)
+
+    else:
+        print("Press 1 to stop video capture")
+        cap = cv2.VideoCapture(0)
+        tm = cv2.TickMeter()
+        total_frames = 0
+        frame_list = []
+        Video_save = False
+        if(args.save):
+            Video_save = True
+
+        while cv2.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            frame = cv2.flip(frame, 1)
+            srcimg = frame
+            drawimg = srcimg.copy()
+            #frame = cv2.resize(frame, [args.width, args.height])
+            # Inference
+            tm.start()
+            left, top, ratioh, ratiow, det_bboxes, det_conf, det_classid = model_net.infer(frame)
+            tm.stop()
+
+            for i in range(det_bboxes.shape[0]):
+                xmin, ymin, xmax, ymax = max(int((det_bboxes[i,0] - left) * ratiow), 0), max(int((det_bboxes[i,1] - top) * ratioh), 0), min(
+                    int((det_bboxes[i,2] - left) * ratiow), srcimg.shape[1]), min(int((det_bboxes[i,3] - top) * ratioh), srcimg.shape[0])
+                image = drawPred(drawimg, det_classid[i], det_conf[i], xmin, ymin, xmax, ymax)
+
+            total_frames += 1
+            fps=tm.getFPS()
+
+            if fps > 0:
+                fps_label = "FPS: %.2f" % fps
+                cv2.putText(image, fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+            cv2.imshow("output", image)
+
+            if cv2.waitKey(1) > -1:
+                print("Stream terminated")
+                break
+
+            if(args.save):
+                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+                frame_list.append(image)
+
+        if(Video_save):
+            clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(frame_list, fps=fps)
+            clip.write_videofile('Webcam_result.mp4')
+
+        print("Total frames: " + str(total_frames))
diff --git a/models/object_detection_nanodet/examples/results/TestResult1.png b/models/object_detection_nanodet/examples/results/TestResult1.png
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diff --git a/models/object_detection_nanodet/examples/test_data/test1.jpg b/models/object_detection_nanodet/examples/test_data/test1.jpg
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diff --git a/models/object_detection_nanodet/examples/test_data/test2.jpg b/models/object_detection_nanodet/examples/test_data/test2.jpg
new file mode 100644
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diff --git a/models/object_detection_nanodet/object_detection_nanodet-plus-m-1.5x-416.onnx b/models/object_detection_nanodet/object_detection_nanodet-plus-m-1.5x-416.onnx
new file mode 100644
index 00000000..066cb860
--- /dev/null
+++ b/models/object_detection_nanodet/object_detection_nanodet-plus-m-1.5x-416.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b82da9944b88577175ee23a459dce2e26e6e4be573def65b1055dc2d9720186
+size 3800954
diff --git a/models/object_detection_yolox/LICENSE b/models/object_detection_yolox/LICENSE
new file mode 100644
index 00000000..1d4dc763
--- /dev/null
+++ b/models/object_detection_yolox/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
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+
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diff --git a/models/object_detection_yolox/README.md b/models/object_detection_yolox/README.md
new file mode 100644
index 00000000..c52507fc
--- /dev/null
+++ b/models/object_detection_yolox/README.md
@@ -0,0 +1,264 @@
+# YOLOX
+
+Nanodet: YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.
+
+Key features of the YOLOX object detector
+- **Anchor-free detectors** significantly reduce the number of design parameters
+- **A decoupled head for classification, regression, and localization** improves the convergence speed
+- **SimOTA advanced label assignment strategy** reduces training time and avoids additional solver hyperparameters
+- **Strong data augmentations like MixUp and Mosiac** to boost YOLOX performance
+
+#### Model metrics:
+Average Precision and Recall values observed for COCO dataset classes are showed below 
+
+
+##### YOLOX_S:
+Average forward time: 5.53 ms, Average NMS time: 1.71 ms, Average inference time: 7.25 ms
+<table>
+<tr><th>Average Precision </th><th>Average Recall</th></tr>
+<tr><td>
+
+|  area  |  IoU  |  Average Precision(AP)  |
+|:-------|:------|:------------------------|
+|  all  |  0.50:0.95  |  0.405  |
+|  all  |  0.50  |  0.593  |
+|  all  |  0.75  |  0.437  |
+|  small  |  0.50:0.95  |  0.232  |
+|  medium  |  0.50:0.95  |  0.448  |
+|  large  |  0.50:0.95  |  0.541  |
+
+ </td><td>
+
+  area  |  IoU  |  Average Recall(AR)  |
+|:-------|:------|:----------------|
+|  all  |  0.50:0.95  |  0.326  |
+|  all  |  0.50:0.95  |  0.531  |
+|  all  |  0.50:0.95 |  0.574  |
+|  small  |  0.50:0.95  |  0.365  |
+|  medium  |  0.50:0.95  |  0.634  |
+|  large  |  0.50:0.95  |  0.724  |
+</td></tr> </table>
+
+
+##### YOLOX_tiny:
+Average forward time: 2.07 ms, Average NMS time: 1.71 ms, Average inference time: 3.79 ms
+<table>
+<tr><th>Average Precision </th><th>Average Recall</th></tr>
+<tr><td>
+
+|  area  |  IoU  |  Average Precision(AP)  |
+|:-------|:------|:------------------------|
+|  all  |  0.50:0.95  |  0.328  |
+|  all  |  0.50  |  0.504  |
+|  all  |  0.75  |  0.346  |
+|  small  |  0.50:0.95  |  0.139  |
+|  medium  |  0.50:0.95  |  0.360  |
+|  large  |  0.50:0.95  |  0.501  |
+
+ </td><td>
+
+  area  |  IoU  |  Average Recall(AR)  |
+|:-------|:------|:----------------|
+|  all  |  0.50:0.95  |  0.283  |
+|  all  |  0.50:0.95  |  0.450  |
+|  all  |  0.50:0.95 |  0.485  |
+|  small  |  0.50:0.95  |  0.226  |
+|  medium  |  0.50:0.95  |  0.550  |
+|  large  |  0.50:0.95  |  0.687  |
+</td></tr> </table>
+
+
+##### YOLOX_nano:
+Average forward time: 1.68 ms, Average NMS time: 1.64 ms, Average inference time: 3.31 ms
+<table>
+<tr><th>Average Precision </th><th>Average Recall</th></tr>
+<tr><td>
+
+|  area  |  IoU  |  Average Precision(AP)  |
+|:-------|:------|:------------------------|
+|  all  |  0.50:0.95  |  0.258  |
+|  all  |  0.50  |  0.414  |
+|  all  |  0.75  |  0.268  |
+|  small  |  0.50:0.95  |  0.082  |
+|  medium  |  0.50:0.95  |  0.275  |
+|  large  |  0.50:0.95  |  0.410  |
+
+ </td><td>
+
+  area  |  IoU  |  Average Recall(AR)  |
+|:-------|:------|:----------------|
+|  all  |  0.50:0.95  |  0.241  |
+|  all  |  0.50:0.95  |  0.384  |
+|  all  |  0.50:0.95 |  0.420  |
+|  small  |  0.50:0.95  |  0.157  |
+|  medium  |  0.50:0.95  |  0.473  |
+|  large  |  0.50:0.95  |  0.631  |
+</td></tr> </table>
+
+
+## Demo
+
+Run the following command to try the demo: 
+```shell
+# Nanodet inference on image input
+python demo.py --model /path/to/model/ --input_type image --image_path /path/to/image/
+
+# Nanodet inference on video input
+python demo.py --model /path/to/model/ --input_type video 
+
+#Saving outputs 
+#Image output
+python demo.py --model /path/to/model/ --input_type image --image_path /path/to/image/ --save True
+
+#Video output
+python demo.py --model /path/to/model/ --input_type video --save True
+
+other parameters 
+--confidence: Confidence values of the predictions (default: 0.5)
+--nms: NMS threshold value for predictions (default: 0.5)
+--obj: Object threshold value (default: 0.5)
+```
+Note: 
+- By default input_type: image
+- image result saved as "result.jpg"
+- webcam result saved as "Webcam_result.mp4"
+
+
+## Results
+
+Here are some of the sample results that were observed using the model (**yolox_s.onnx**),
+
+<p float="left">
+  <img src="./examples/results/result1.jpg" width="450" height="450">
+  <img src="./examples/results/result2.jpg" width="450" height="450">
+</p>
+  
+Video inference result,
+<p align="center">
+  <img src="./examples/results/WebcamR.gif" width="650" height="450">
+</p>
+  
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+
+## Reference
+
+- YOLOX article: https://arxiv.org/abs/2107.08430
+- YOLOX weight and scripts for training: https://github.com/Megvii-BaseDetection/YOLOX
+- YOLOX blog: https://arshren.medium.com/yolox-new-improved-yolo-d430c0e4cf20
+- YOLOX-lite: https://github.com/TexasInstruments/edgeai-yolox
+
+
+#### Note:
+
+- In this repo we have used the following versions of YOLOX: YOLOX_S, YOLOX_tiny, YOLOX_nano
+- The model was trained on COCO 2017 dataset, link to dataset: https://cocodataset.org/#download
+- Below, we have per class AP results on COCO dataset for the models YOLOX_S, YOLOX_tiny, YOLOX_nano respectively
+
+##### YOLOX_S
+| class         | AP     | class        | AP     | class          | AP     |
+|:--------------|:-------|:-------------|:-------|:---------------|:-------|
+| person        | 54.109 | bicycle      | 31.580 | car            | 40.447 |
+| motorcycle    | 43.477 | airplane     | 66.070 | bus            | 64.183 |
+| train         | 64.483 | truck        | 35.110 | boat           | 24.681 |
+| traffic light | 25.068 | fire hydrant | 64.382 | stop sign      | 65.333 |
+| parking meter | 48.439 | bench        | 22.653 | bird           | 33.324 |
+| cat           | 66.394 | dog          | 60.096 | horse          | 58.080 |
+| sheep         | 49.456 | cow          | 53.596 | elephant       | 65.574 |
+| bear          | 70.541 | zebra        | 66.461 | giraffe        | 66.780 |
+| backpack      | 13.095 | umbrella     | 41.614 | handbag        | 12.865 |
+| tie           | 29.453 | suitcase     | 39.089 | frisbee        | 61.712 |
+| skis          | 21.623 | snowboard    | 31.326 | sports ball    | 39.820 |
+| kite          | 41.410 | baseball bat | 27.311 | baseball glove | 36.661 |
+| skateboard    | 49.374 | surfboard    | 35.524 | tennis racket  | 45.569 |
+| bottle        | 37.270 | wine glass   | 33.088 | cup            | 39.835 |
+| fork          | 31.620 | knife        | 15.265 | spoon          | 14.918 |
+| bowl          | 43.251 | banana       | 27.904 | apple          | 17.630 |
+| sandwich      | 32.789 | orange       | 29.388 | broccoli       | 23.187 |
+| carrot        | 23.114 | hot dog      | 33.716 | pizza          | 52.541 |
+| donut         | 47.980 | cake         | 36.160 | chair          | 29.707 |
+| couch         | 46.175 | potted plant | 24.781 | bed            | 44.323 |
+| dining table  | 30.022 | toilet       | 64.237 | tv             | 57.301 |
+| laptop        | 58.362 | mouse        | 57.774 | remote         | 24.271 |
+| keyboard      | 48.020 | cell phone   | 32.376 | microwave      | 57.220 |
+| oven          | 36.168 | toaster      | 28.735 | sink           | 38.159 |
+| refrigerator  | 52.876 | book         | 15.030 | clock          | 48.622 |
+| vase          | 37.013 | scissors     | 26.307 | teddy bear     | 45.676 |
+| hair drier    | 7.255  | toothbrush   | 19.374 |                |        |
+
+
+##### YOLOX_tiny
+| class         | AP     | class        | AP     | class          | AP     |
+|:--------------|:-------|:-------------|:-------|:---------------|:-------|
+| person        | 45.685 | bicycle      | 22.797 | car            | 29.265 |
+| motorcycle    | 37.980 | airplane     | 59.446 | bus            | 54.878 |
+| train         | 62.459 | truck        | 26.850 | boat           | 16.724 |
+| traffic light | 17.527 | fire hydrant | 55.155 | stop sign      | 57.120 |
+| parking meter | 37.755 | bench        | 17.656 | bird           | 24.382 |
+| cat           | 55.792 | dog          | 50.964 | horse          | 49.806 |
+| sheep         | 39.894 | cow          | 42.855 | elephant       | 58.863 |
+| bear          | 62.345 | zebra        | 58.389 | giraffe        | 62.362 |
+| backpack      | 8.131  | umbrella     | 33.650 | handbag        | 7.777  |
+| tie           | 21.907 | suitcase     | 25.593 | frisbee        | 48.975 |
+| skis          | 16.941 | snowboard    | 19.409 | sports ball    | 30.718 |
+| kite          | 33.956 | baseball bat | 17.912 | baseball glove | 28.793 |
+| skateboard    | 38.253 | surfboard    | 28.329 | tennis racket  | 33.240 |
+| bottle        | 23.872 | wine glass   | 20.386 | cup            | 26.962 |
+| fork          | 21.025 | knife        | 8.434  | spoon          | 6.513  |
+| bowl          | 34.706 | banana       | 24.050 | apple          | 12.745 |
+| sandwich      | 28.046 | orange       | 24.216 | broccoli       | 18.579 |
+| carrot        | 16.283 | hot dog      | 30.058 | pizza          | 44.371 |
+| donut         | 35.957 | cake         | 29.765 | chair          | 22.070 |
+| couch         | 41.221 | potted plant | 19.856 | bed            | 44.173 |
+| dining table  | 29.000 | toilet       | 60.369 | tv             | 49.868 |
+| laptop        | 48.858 | mouse        | 47.843 | remote         | 14.349 |
+| keyboard      | 42.412 | cell phone   | 23.536 | microwave      | 51.839 |
+| oven          | 32.384 | toaster      | 24.209 | sink           | 32.607 |
+| refrigerator  | 50.156 | book         | 9.534  | clock          | 41.661 |
+| vase          | 25.548 | scissors     | 17.612 | teddy bear     | 39.375 |
+| hair drier    | 0.000  | toothbrush   | 9.933  |                |        |
+
+
+##### YOLOX_nano
+| class         | AP     | class        | AP     | class          | AP     |
+|:--------------|:-------|:-------------|:-------|:---------------|:-------|
+| person        | 38.444 | bicycle      | 16.922 | car            | 21.708 |
+| motorcycle    | 30.753 | airplane     | 47.573 | bus            | 49.651 |
+| train         | 55.302 | truck        | 20.294 | boat           | 11.919 |
+| traffic light | 12.026 | fire hydrant | 48.798 | stop sign      | 52.446 |
+| parking meter | 33.439 | bench        | 13.565 | bird           | 16.520 |
+| cat           | 42.603 | dog          | 43.831 | horse          | 37.338 |
+| sheep         | 27.807 | cow          | 33.155 | elephant       | 52.374 |
+| bear          | 49.737 | zebra        | 52.259 | giraffe        | 56.445 |
+| backpack      | 5.456  | umbrella     | 25.288 | handbag        | 2.802  |
+| tie           | 17.110 | suitcase     | 17.757 | frisbee        | 40.878 |
+| skis          | 13.245 | snowboard    | 11.443 | sports ball    | 22.310 |
+| kite          | 28.107 | baseball bat | 10.295 | baseball glove | 20.294 |
+| skateboard    | 28.285 | surfboard    | 19.142 | tennis racket  | 25.253 |
+| bottle        | 15.064 | wine glass   | 13.412 | cup            | 19.357 |
+| fork          | 13.384 | knife        | 4.276  | spoon          | 3.460  |
+| bowl          | 26.615 | banana       | 18.067 | apple          | 9.672  |
+| sandwich      | 22.817 | orange       | 23.574 | broccoli       | 14.710 |
+| carrot        | 10.180 | hot dog      | 18.646 | pizza          | 38.244 |
+| donut         | 24.204 | cake         | 21.330 | chair          | 14.644 |
+| couch         | 33.018 | potted plant | 13.252 | bed            | 38.034 |
+| dining table  | 24.287 | toilet       | 52.986 | tv             | 44.978 |
+| laptop        | 44.130 | mouse        | 35.173 | remote         | 7.349  |
+| keyboard      | 33.903 | cell phone   | 19.140 | microwave      | 38.800 |
+| oven          | 25.890 | toaster      | 10.665 | sink           | 23.293 |
+| refrigerator  | 42.697 | book         | 6.942  | clock          | 35.254 |
+| vase          | 18.742 | scissors     | 11.866 | teddy bear     | 30.907 |
+| hair drier    | 0.000  | toothbrush   | 7.284  |                |        |
+
+
+#### Contributor Details
+
+- Google Summer of Code'22
+- Contributor: Sri Siddarth Chakaravarthy
+- Github Profile: https://github.com/Sidd1609
+- Organisation: OpenCV
+- Project: Lightweight object detection models using OpenCV 
+
diff --git a/models/object_detection_yolox/YoloX.py b/models/object_detection_yolox/YoloX.py
new file mode 100644
index 00000000..7d299a15
--- /dev/null
+++ b/models/object_detection_yolox/YoloX.py
@@ -0,0 +1,125 @@
+import cv2
+import numpy as np
+
+class YoloX():
+    def __init__(self, modelPath, p6=False, confThreshold=0.35, nmsThreshold=0.5, objThreshold=0.5):
+        with open('coco.names', 'rt') as f:
+            self.classes = f.read().rstrip('\n').split('\n')
+        self.num_classes = len(self.classes)
+        self.net = cv2.dnn.readNet(modelPath)
+        self.input_size = (640, 640)
+        #self.ratio = min(self.input_size[0] / image.shape[0], self.input_size[1] / image.shape[1])
+        self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3)
+        self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3)
+        if not p6:
+            self.strides = [8, 16, 32]
+        else:
+            self.strides = [8, 16, 32, 64]
+        self.confThreshold = confThreshold
+        self.nmsThreshold = nmsThreshold
+        self.objThreshold = objThreshold
+
+    def preprocess(self, image):
+        if len(image.shape) == 3:
+            padded_img = np.ones((self.input_size[0], self.input_size[1], 3)) * 114.0
+        else:
+            padded_img = np.ones(self.input_size) * 114.0
+        img = np.array(image)
+        ratio = min(self.input_size[0] / img.shape[0], self.input_size[1] / img.shape[1])
+        resized_img = cv2.resize(
+            img, (int(img.shape[1] * ratio), int(img.shape[0] * ratio)), interpolation=cv2.INTER_LINEAR
+        ).astype(np.float32)
+        padded_img[: int(img.shape[0] * ratio), : int(img.shape[1] * ratio)] = resized_img
+        image = padded_img
+
+        image = image.astype(np.float32)
+        image = image[:, :, ::-1]
+        return image, ratio
+
+    def infer(self, srcimg):
+        img, ratio = self.preprocess(srcimg)
+        blob = cv2.dnn.blobFromImage(img)
+        self.net.setInput(blob)
+        outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+        predictions = self.postprocess(outs[0], ratio)
+
+        return predictions
+
+
+    def postprocess(self, outputs, ratio):
+        grids = []
+        expanded_strides = []
+        hsizes = [self.input_size[0] // stride for stride in self.strides]
+        wsizes = [self.input_size[1] // stride for stride in self.strides]
+
+        for hsize, wsize, stride in zip(hsizes, wsizes, self.strides):
+            xv, yv = np.meshgrid(np.arange(hsize), np.arange(wsize))
+            grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+            grids.append(grid)
+            shape = grid.shape[:2]
+            expanded_strides.append(np.full((*shape, 1), stride))
+
+        grids = np.concatenate(grids, 1)
+        expanded_strides = np.concatenate(expanded_strides, 1)
+        outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
+        outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
+
+        predictions = outputs[0]
+
+        boxes = predictions[:, :4]
+        scores = predictions[:, 4:5] * predictions[:, 5:]
+
+        boxes_xyxy = np.ones_like(boxes)
+        boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
+        boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
+        boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
+        boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
+        boxes_xyxy /= ratio
+
+        final_dets = []
+        num_classes = scores.shape[1]
+        for cls_ind in range(num_classes):
+            cls_scores = scores[:, cls_ind]
+            valid_score_mask = cls_scores > self.confThreshold
+
+            if valid_score_mask.sum() == 0:
+                continue
+
+            else:
+                valid_scores = cls_scores[valid_score_mask]
+                valid_boxes = boxes_xyxy[valid_score_mask]
+
+                keep = []
+                x1 = valid_boxes[:, 0]
+                y1 = valid_boxes[:, 1]
+                x2 = valid_boxes[:, 2]
+                y2 = valid_boxes[:, 3]
+
+                areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+                order = valid_scores.argsort()[::-1]
+
+                while order.size > 0:
+                    i = order[0]
+                    keep.append(i)
+                    xx1 = np.maximum(x1[i], x1[order[1:]])
+                    yy1 = np.maximum(y1[i], y1[order[1:]])
+                    xx2 = np.minimum(x2[i], x2[order[1:]])
+                    yy2 = np.minimum(y2[i], y2[order[1:]])
+
+                    w = np.maximum(0.0, xx2 - xx1 + 1)
+                    h = np.maximum(0.0, yy2 - yy1 + 1)
+                    inter = w * h
+                    ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+                    inds = np.where(ovr <= self.nmsThreshold)[0]
+                    order = order[inds + 1]
+                    if len(keep) > 0:
+                        cls_inds = np.ones((len(keep), 1)) * cls_ind
+                        dets = np.concatenate([valid_boxes[keep], valid_scores[keep, None], cls_inds], 1)
+                        final_dets.append(dets)
+
+        if len(final_dets) == 0:
+            return None
+
+        res_dets = np.concatenate(final_dets, 0)
+        return res_dets
diff --git a/models/object_detection_yolox/coco.names b/models/object_detection_yolox/coco.names
new file mode 100644
index 00000000..16315f2b
--- /dev/null
+++ b/models/object_detection_yolox/coco.names
@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
\ No newline at end of file
diff --git a/models/object_detection_yolox/demo.py b/models/object_detection_yolox/demo.py
new file mode 100644
index 00000000..566c0bf3
--- /dev/null
+++ b/models/object_detection_yolox/demo.py
@@ -0,0 +1,132 @@
+import cv2
+import numpy as np
+import argparse
+import time
+import moviepy.video.io.ImageSequenceClip
+from YoloX import YoloX
+
+backends = [cv2.dnn.DNN_BACKEND_OPENCV, cv2.dnn.DNN_BACKEND_CUDA]
+targets = [cv2.dnn.DNN_TARGET_CPU, cv2.dnn.DNN_TARGET_CUDA, cv2.dnn.DNN_TARGET_CUDA_FP16]
+help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
+help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
+
+try:
+    backends += [cv2.dnn.DNN_BACKEND_TIMVX]
+    targets += [cv2.dnn.DNN_TARGET_NPU]
+    help_msg_backends += "; {:d}: TIMVX"
+    help_msg_targets += "; {:d}: NPU"
+except:
+    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/Sidd1609/5bb321c8733110ed613ec120c7c02e41 for more information.')
+
+with open('coco.names', 'rt') as f:
+    classes = f.read().rstrip('\n').split('\n')
+
+
+def vis(dets, res_img):
+    if dets is not None:
+        final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
+        for i in range(len(final_boxes)):
+            box = final_boxes[i]
+            cls_id = int(final_cls_inds[i])
+            score = final_scores[i]
+            if score < args.confidence:
+                continue
+
+            x0 = int(box[0])
+            y0 = int(box[1])
+            x1 = int(box[2])
+            y1 = int(box[3])
+
+            text = '{}:{:.1f}%'.format(classes[cls_id], score * 100)
+            font = cv2.FONT_HERSHEY_SIMPLEX
+            txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
+            cv2.rectangle(res_img, (x0, y0), (x1, y1), (0, 0, 255), 2)
+            cv2.rectangle(res_img, (x0, y0 + 1), (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), (255, 255, 255), -1)
+            cv2.putText(res_img, text, (x0, y0 + txt_size[1]), font, 0.4, (0, 255, 0), thickness=1)
+
+    else:
+        print("No detections")
+
+    return res_img
+
+if __name__=='__main__':
+    parser = argparse.ArgumentParser(description='Nanodet inference using OpenCV an contribution by Sri Siddarth Chakaravarthy part of GSOC_2022')
+    parser.add_argument('--model', type=str, default='yolox_s.onnx', help="Path to the model")
+    parser.add_argument('--input_type', type=str, default='image', help="Input types: image or video")
+    parser.add_argument('--image_path', type=str, default='test2.jpg', help="Image path")
+    parser.add_argument('--video_path', type=str, default='sample.mp4', help="Video path")
+    parser.add_argument('--confidence', default=0.5, type=float, help='Class confidence')
+    parser.add_argument('--nms', default=0.5, type=float, help='Enter nms IOU threshold')
+    parser.add_argument('--obj', default=0.5, type=float, help='Enter object threshold')
+    parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This option is invalid when using camera.')
+    args = parser.parse_args()
+
+    model_net = YoloX(modelPath= args.model, confThreshold=args.confidence, nmsThreshold=args.nms, objThreshold=args.obj)
+    
+    if (args.input_type=="image"):
+        srcimg = cv2.imread(args.image_path)
+
+        image = srcimg
+        a = time.time()
+        preds = model_net.infer(srcimg)
+        b = time.time()
+        print('Inference_Time:'+str(b-a)+' secs')
+
+        srcimg = vis(preds, image)
+
+        cv2.namedWindow(args.image_path, cv2.WINDOW_AUTOSIZE)
+        cv2.imshow(args.image_path, srcimg)
+        cv2.waitKey(0)
+
+        if args.save:
+            print('Resutls saved to result.jpg\n')
+            cv2.imwrite('result.jpg', image)
+
+    else:
+        print("Press 1 to stop video capture")
+        cap = cv2.VideoCapture(0)
+        tm = cv2.TickMeter()
+        total_frames = 0
+        frame_list = []
+        Video_save = False
+        if(args.save):
+            Video_save = True
+
+        while cv2.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            frame = cv2.flip(frame, 1)
+            srcimg = frame
+
+            # Inference
+            tm.start()
+            preds = model_net.infer(srcimg)
+            tm.stop()
+
+            srcimg = vis(preds, srcimg)
+
+            total_frames += 1
+            fps=tm.getFPS()
+
+            if fps > 0:
+                fps_label = "FPS: %.2f" % fps
+                cv2.putText(srcimg, fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+            cv2.imshow("output", srcimg)
+
+            if cv2.waitKey(1) > -1:
+                print("Stream terminated")
+                break
+
+            if(args.save):
+                srcimg = cv2.cvtColor(srcimg, cv2.COLOR_BGR2RGB)
+                frame_list.append(srcimg)
+
+        if(Video_save):
+            clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(frame_list, fps=fps)
+            clip.write_videofile('Webcam_result.mp4')
+
+        print("Total frames: " + str(total_frames))
diff --git a/models/object_detection_yolox/examples/results/WebcamR.gif b/models/object_detection_yolox/examples/results/WebcamR.gif
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diff --git a/models/object_detection_yolox/yolox_nano.onnx b/models/object_detection_yolox/yolox_nano.onnx
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diff --git a/models/object_detection_yolox/yolox_s.onnx b/models/object_detection_yolox/yolox_s.onnx
new file mode 100644
index 00000000..42d1fb00
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