BootsofLagrangian
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Update README.md
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README.md
CHANGED
@@ -69,13 +69,13 @@ import PIL.Image
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def predict(image_path, weights='ultima_yolov9-e.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45):
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# Initialize
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device = select_device('0')
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model = DetectMultiBackend(weights=weights, device=
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stride, names, pt = model.stride, model.names, model.pt
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# Load image
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image = np.array(PIL.Image.open(image_path))
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img = letterbox(
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img = img
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(device).float()
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img /= 255.0
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@@ -88,10 +88,10 @@ def predict(image_path, weights='ultima_yolov9-e.pt', imgsz=640, conf_thres=0.1,
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# Apply NMS
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pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000)
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```
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or use `
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```bash
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python ./
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```
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# Training Infomation
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def predict(image_path, weights='ultima_yolov9-e.pt', imgsz=640, conf_thres=0.1, iou_thres=0.45):
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# Initialize
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device = select_device('0')
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model = DetectMultiBackend(weights=weights, device=device, fp16=False)
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stride, names, pt = model.stride, model.names, model.pt
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# Load image
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image = np.array(PIL.Image.open(image_path).convert("RGB"))
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img = letterbox(image, imgsz, stride=stride, auto=True)[0]
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img = img.transpose(2, 0, 1)
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(device).float()
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img /= 255.0
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# Apply NMS
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pred = non_max_suppression(pred[0][0], conf_thres, iou_thres, classes=None, max_det=1000)
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```
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or use `detect.py` in yolov9 repo.
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```bash
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python ./detect.py --source [image_path] --device 0 --img 1280 --weights './ultima_yolov9-e.pt' --name ultima_yolov9_1280_detect
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```
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# Training Infomation
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