YOLOv6
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How to infer yolov6 model for single image
i need a python script which take single image and do the prediction on it and return the results. can someone help on this
try using infer.py and pass your image path in source argument.
for eg.
python tools/infer.py --weights <saved_model_path> --source <img path>
@Rjshrivastav that is one way of doing it, I need script which take a single image and return the bounding boxes and class labels.
@Rjshrivastav that is one way of doing it, I need script which take a single image and return the bounding boxes and class labels.
Hi, you can add --save-txt option to save predicted scores and labels of yolo format.
@MTChengMeng do we have a predict function which takes image as input and gives the output as bboxes and labels. I have currently trained the model and want function to integrate with my codebase.
A function would help instead of invoking like this 'python tools/infer.py --weights <saved_model_path> --source '
@mallapraveen you can useyolov6/core/inferer.py
that contains all the necessary functions.
@Rjshrivastav 这是一种方法,我需要一个脚本来获取单个图像并返回边界框和类标签。
改写一下infer的代码。 def infer(self, conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf): ''' Model Inference and results visualization '''
for img_path in tqdm(self.img_paths):
img, img_src = self.precess_image(img_path, self.img_size, self.stride, self.half)
img = img.to(self.device)
if len(img.shape) == 3:
img = img[None]
# expand for batch dim
pred_results = self.model(img)
det = non_max_suppression(pred_results, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)[0]
if len(det):
det[:, :4] = self.rescale(img.shape[2:], det[:, :4], img_src.shape).round()
return img_src, det
else:
# return img_src,torch.tensor(np.zeros((1,6)))
return img_src,np.zeros((1,6))
@SongHfei Thanks