yolov7
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WongKinYiu
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
My training is also very slow. I set paste_in to 0 and lowered workers down to 4, which seems to be my sweet spot as lower or higher than 4...
How to add the number of each tag to the output image using cv2.putText. For example, the recognition picture has four cars and two people. Then add (Car: 4 Person:...
HI. How can I count the number of individuals in each category in the image and output it in the result image.
Now we can export concat all anchors outputs in flag `--grid` ! If we use `--grid` only, we will export an onnx with one concat output. If we use `--grid`...
While running YOLOv7ONNXandTRT.ipynb, if I use the end2end flag while exporting, the output shape is (5,7). But when I try to export without end2end, the output shape comes out to...
iam trying to train a yolov7-tiny module on a custom dataset, iam training on kaggle which offers a free gpu, pytorch allocated more than 90% of the available memory which...
Hi, Is there any way to reduce the output layer dimension? Any explanation of what these 5 parameters are after inference: Output Shape (1, 3, 20, 20, 10)
daknet has a yolov7-tiny model.so this project can load yolov7-tiny.weights to inference?
建堯博士您好，關於在detect head的 fuse layer有試跑過一次有跳出問題，因此針對部分有重新實作了一次。 請您確認一下是否正確。 ``` def fuse_conv_and_ia(conv, ia): fusedconv = nn.Conv2d(conv.in_channels, conv.out_channels, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding, groups=conv.groups, bias=True).requires_grad_(False).to(conv.weight.device) # Prepare filters c1, c2, _, _ = conv.weight.shape c1_, c2_, _,...
训练问题
yaml格式如下： train: F:\stroller_dection\images_transfer\stroller_model_construction1\train2/images val: F:\stroller_dection\images_transfer\stroller_model_construction1\val2/images nc: 1 names: [ 'stroller' ] 运行问题如下： Traceback (most recent call last): File "F:\object_dection\stroller_detection\yolov7_train_2\utils\google_utils.py", line 26, in attempt_download assets = [x['name'] for x in response['assets']]...
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Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors