Comments (2)
zzh8829
commented on May 12, 2024
1
For 416x416x3 images, you can train the full yolov3-tiny at about 140 images per second on Tesla M60 (comparable to GTX 1060). I didn't get a chance to test yolov3 yet, since I only been training the tiny version.
I also added some comments related to benchmark here #2
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mengzhibin
commented on May 12, 2024
thanks very much for your nice work.
For I get slow speed in pytorch version, faster version is searching by me.
now I know your condition, I will close this question.
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Related Issues (20)
- Converting a custom yolov3 model
- Increasing number of channels
- Train the model from random weights, 100 epochs DO NOT work HOT 2
- [Feature Request] Implementing YOLO v4 / v5 / .. ?
- Get negative value by calling model(dataset)
- anchors HOT 1
- model.fit() and eager_tf generates different training results HOT 2
- RTX 2080ti batch size 2..
- Invalid argument: Received a label value of 67 which is outside the valid range of [0, 1) HOT 1
- Error in colab notebook
- box caculation
- yolov3 tiny - evaluation on pretrained weights gives lower accuracy than expected HOT 1
- No detection when using GPU, but CPU works
- "Windows fatal exception: access violation" when run export_tflite.py
- Yolo loss binary_crossentropy version
- How to create tfrecord for coco dataset. HOT 1
- Windows - ERROR: No matching distribution found for tensorflow-gpu==2.1.0rc1 HOT 1
- 识别率
- oriented bounding boxes
- Detection box returns nan when running detect.py
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