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wang-xinyu 2020-07-06 19:40:28 +08:00
parent fc935a130c
commit 8ebe060186
5 changed files with 5 additions and 5 deletions
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2
README.md
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@ -14,7 +14,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
- `28 May 2020`. arcface LResNet50E-IR model from [deepinsight/insightface](https://github.com/deepinsight/insightface) implemented. We got 333fps on GTX1080.
- `2 June 2020`. yolov3 and yolov3-spp migrated to TensorRT 7 API. The new yolov3 is using pytorch implementation [ultralytics/yolov3](https://github.com/ultralytics/yolov3), the yolov3 in branch `trt4` was using pytorch implementation [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3).
- `23 June 2020`. Update yolov5-s model according to [ultralytics/yolov5](https://github.com/ultralytics/yolov5)'s PANet updates on 22 June 2020.
- `6 July 2020`. Add yolov3-tiny, and got 333fps GTX1080.
- `6 July 2020`. Add yolov3-tiny, and got 333fps on GTX1080.
## Tutorials

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yolov3-spp/gen_wts.py
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@ -5,7 +5,7 @@ from utils.utils import *
model = Darknet('cfg/yolov3-spp.cfg', (416, 416))
weights = sys.argv[1]
dev = '1'
dev = '0'
device = torch_utils.select_device(dev)
model.load_state_dict(torch.load(weights, map_location=device)['model'])

2
yolov3-tiny/gen_wts.py
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@ -5,7 +5,7 @@ from utils.utils import *
model = Darknet('cfg/yolov3-tiny.cfg', (608, 608))
weights = sys.argv[1]
dev = '0'
device = torch_utils.select_device('0')
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format

2
yolov3/gen_wts.py
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@ -5,7 +5,7 @@ from utils.utils import *
model = Darknet('cfg/yolov3.cfg', (608, 608))
weights = sys.argv[1]
dev = '0'
device = torch_utils.select_device('0')
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format

2
yolov4/gen_wts.py
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@ -5,7 +5,7 @@ from utils.utils import *
model = Darknet('cfg/yolov4.cfg', (608, 608))
weights = sys.argv[1]
dev = '0'
device = torch_utils.select_device('0')
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format