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car-detection-bayes
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Merge remote-tracking branch 'origin/master'

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Glenn Jocher 2019-08-02 01:33:33 +02:00
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README.md
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@ -47,16 +47,12 @@ Our Jupyter [notebook](https://colab.research.google.com/github/ultralytics/yolo
# Training
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
Each epoch trains on 117,263 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 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day)**.
Here we see training results from `coco_1img.data`, `coco_10img.data` and `coco_100img.data`, 3 example files available in the `data/` folder, which train and test on the first 1, 10 and 100 images of the coco2014 trainval dataset.
`from utils import utils; utils.plot_results()`
![results](https://user-images.githubusercontent.com/26833433/56207787-ec9e7000-604f-11e9-94dd-e1fcc374270f.png)
**Plot Training:** `from utils import utils; utils.plot_results()` plots training results from `coco_16img.data`, `coco_64img.data`, 2 example datasets available in the `data/` folder, which train and test on the first 16 and 64 images of the COCO2014-trainval dataset.
![results](https://user-images.githubusercontent.com/26833433/62325526-1fa82a80-b4ac-11e9-958e-2a263bf15ab0.png)
## Image Augmentation

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examples.ipynb
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@ -46,11 +46,8 @@
"import glob\n",
"import torch\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from IPython.display import Image \n",
"from IPython.display import clear_output\n",
"from IPython.display import Image, clear_output\n",
"print('PyTorch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
],
"execution_count": 39,