Skip to main content

Yolov5-Lite: Minimal YoloV5 Implementation

Project description

Yolov5-Lite: Minimal YoloV5 Implementation

Yolov5-Lite

Overview

It has been simplified by editing detect.py in the yolov5 repository.

Installation

git clone https://github.com/kadirnar/yolov5-lite
cd yolov5-lite
pip install -r requirements.txt

Yolov5-Lite Prediction:

It is the edited version of the codes in the detect file.

class Yolov5:
    def __init__(self, weights, device, data):
        self.weights = weights
        self.device = device

    def load_model(self, weights, device, data):
        self.device = select_device(device)
        self.model = DetectMultiBackend(weights, device=self.device, data=data)
        
    def preprocces_img(self, img, imgsz):
        self.npy_im = numpy_img(img, imgsz)
        self.tensor_im = file_to_torch(self.npy_im, self.device)

    def detect(self):
        # Inference
        pred = self.model(self.tensor_im) # shape: torch.Size([1, 3, 640, 480])

        # NMS
        pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, max_det=1000)
        for det in pred:
            det[:, :4] = scale_coords(self.tensor_im.shape[2:], det[:, :4], self.npy_im.shape).round()
        
        self.det = det

    def show_img(self, view_img=True):
        # Write results
        for *xyxy, conf, cls in reversed(self.det):
            annotator = Annotator(self.npy_im, line_width=3, example=str(self.model.names))
            logging.info("\t+ Label: %s, Conf: %.5f" % (self.model.names[int(cls)], conf.item()))
            if view_img:  # Add bbox to image
                label = "%s %.2f" % (self.model.names[int(cls)], conf)
                annotator.box_label(xyxy, label, color=colors(int(cls), True))

        # Stream results
        im0 = annotator.result()
        if view_img:
            cv2.imshow("frame", im0)
            cv2.waitKey(0)
            cv2.destroyAllWindows()

Yolov5-Lite Run Code:

You can take the detect.py file as an example to load and visualize your yolov5 models.

weights = "yolov5s.pt"
img = "data/images/bus.jpg"
data = "data/coco128.yaml"
device = "cpu"
imgsz = 640
view_img = True


model = Yolov5(weights, device, data)
model.load_model(weights, device, data)
model.preprocces_img(img, imgsz)
model.detect()
model.show_img(view_img)

Reference:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

yolite-0.0.3.tar.gz (3.2 kB view details)

Uploaded Source

File details

Details for the file yolite-0.0.3.tar.gz.

File metadata

  • Download URL: yolite-0.0.3.tar.gz
  • Upload date:
  • Size: 3.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for yolite-0.0.3.tar.gz
Algorithm Hash digest
SHA256 04066f10b93260b79b5929e0cb5e19b49d1136c78e25611e34d94cd92a8327c0
MD5 9738f86f260af7e04c323fd5ee05685d
BLAKE2b-256 d0b99a6e890d85ffce5527c2d449ba148c1a048cee25dc6bbaf9fa53f78e9ec0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page