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

This commit is contained in:
Glenn Jocher 2020-06-27 09:09:07 -07:00
parent 9b9715668c e1fb453079
commit 7f953b2106
4 changed files with 14 additions and 3 deletions
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4
.github/workflows/greetings.yml vendored
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@ -16,7 +16,7 @@ jobs:
<a href="https://apps.apple.com/app/id1452689527" target="_blank">
<img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="800"></a>
<img src="https://user-images.githubusercontent.com/26833433/84200349-729f2680-aa5b-11ea-8f9a-604c9e01a658.png" width="800">
<img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="800">
To continue with this repo, please visit our [Custom Training Tutorial](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) to get started, and see our [Google Colab Notebook](https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb), [Docker Image](https://hub.docker.com/r/ultralytics/yolov3), and [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) for example environments.
@ -27,4 +27,4 @@ jobs:
- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
For more information please visit https://www.ultralytics.com.
For more information please visit https://www.ultralytics.com.

2
models.py
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@ -438,7 +438,7 @@ def convert(cfg='cfg/yolov3-spp.cfg', weights='weights/yolov3-spp.weights'):
target = weights.rsplit('.', 1)[0] + '.pt'
torch.save(chkpt, target)
print("Success: converted '%s' to 's%'" % (weights, target))
print("Success: converted '%s' to '%s'" % (weights, target))
else:
print('Error: extension not supported.')

1
requirements.txt
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@ -3,6 +3,7 @@
numpy == 1.17
opencv-python >= 4.1
torch >= 1.5
torchvision
matplotlib
pycocotools
tqdm

10
train.py
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@ -143,6 +143,15 @@ def train(hyp):
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights)
if opt.freeze_layers:
output_layer_indices = [idx - 1 for idx, module in enumerate(model.module_list) if isinstance(module, YOLOLayer)]
freeze_layer_indices = [x for x in range(len(model.module_list)) if
(x not in output_layer_indices) and
(x - 1 not in output_layer_indices)]
for idx in freeze_layer_indices:
for parameter in model.module_list[idx].parameters():
parameter.requires_grad_(False)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
@ -394,6 +403,7 @@ if __name__ == '__main__':
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--freeze-layers', action='store_true', help='Freeze non-output layers')
opt = parser.parse_args()
opt.weights = last if opt.resume else opt.weights
check_git_status()