Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Update README.md

This commit is contained in:
Glenn Jocher 2018-12-28 19:23:35 +01:00 committed by GitHub
parent 1b629e754c
commit 7f257d30c5
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 5 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
README.md
View File

@ -24,6 +24,7 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
**Resume Training:** Run `train.py --resume` to resume training from the most recently saved checkpoint `weights/latest.pt`. **Resume Training:** Run `train.py --resume` to resume training from the most recently saved checkpoint `weights/latest.pt`.
Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.6 s/batch on a 1080 Ti (15 epochs/day)** or 0.45 s/batch on a 2080 Ti. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.6 s/batch on a 1080 Ti (15 epochs/day)** or 0.45 s/batch on a 2080 Ti.
![Alt](https://user-images.githubusercontent.com/26833433/49822374-3b27bf00-fd7d-11e8-9180-f0ac9fe2fdb4.png "coco training loss") ![Alt](https://user-images.githubusercontent.com/26833433/49822374-3b27bf00-fd7d-11e8-9180-f0ac9fe2fdb4.png "coco training loss")
## Image Augmentation ## Image Augmentation
@ -32,10 +33,10 @@ Each epoch trains on 120,000 images from the train and validate COCO sets, and t
Augmentation | Description Augmentation | Description
--- | --- --- | ---
Translation | +/- 20% (vertical and horizontal) Translation | +/- 10% (vertical and horizontal)
Rotation | +/- 5 degrees Rotation | +/- 5 degrees
Shear | +/- 3 degrees (vertical and horizontal) Shear | +/- 2 degrees (vertical and horizontal)
Scale | +/- 20% Scale | +/- 10%
Reflection | 50% probability (horizontal-only) Reflection | 50% probability (horizontal-only)
H**S**V Saturation | +/- 50% H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50% HS**V** Intensity | +/- 50%
@ -47,7 +48,7 @@ HS**V** Intensity | +/- 50%
Run `detect.py` to apply trained weights to an image and visualize results, such as `zidane.jpg` from the `data/samples` folder, shown here. Run `detect.py` to apply trained weights to an image and visualize results, such as `zidane.jpg` from the `data/samples` folder, shown here.
**YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt` **YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt`
<img src="https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg" width="800"> <img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="800">
**YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt` **YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt`
<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="800"> <img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="800">