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car-detection-bayes
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Glenn Jocher 2018-12-11 20:47:21 +01:00
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# Inference
Run `detect.py --weights` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Download official YOLOv3 weights:
Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Download official YOLOv3 weights:
- PyTorch format: https://storage.googleapis.com/ultralytics/yolov3.pt
- Darknet format: https://pjreddie.com/media/files/yolov3.weights
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "inference example")
# Testing
# Validation mAP
Run `test.py` to validate the official YOLOv3 weights `checkpoints/yolov3.weights` against the 5000 validation images. You should obtain a mAP of .581 using this repo (https://github.com/ultralytics/yolov3), compared to .579 as reported in darknet (https://arxiv.org/abs/1804.02767).
Run `test.py` to validate the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. You should obtain a .584 mAP at `--img-size 416`, or .586 at `--img-size 608` using this repo, compared to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767).
Run `test.py --weights weights/latest.pt` to validate against the latest training
oint.
Run `test.py --weights weights/latest.pt` to validate against the latest training results. Default training settings produce a 0.522 mAP at epoch 62. We are currently exploring how to improve this.
# Contact