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 2018-09-01 13:18:53 +02:00 committed by GitHub
parent 7fa8dd9257
commit b76ba51012
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -19,7 +19,7 @@ Python 3.6 or later with the following `pip3 install -U -r requirements.txt` pac
# Training
Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full augmentation, or ~15 epochs/day without input image augmentation. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (coming soon)
Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full image augmentation, or ~15 epochs/day with no augmentation. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (coming soon)
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "training loss")
# Inference