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README.md
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README.md
@ -31,7 +31,7 @@ AMD|R5-5600(X86-cpu)|Linux(amd64)|ncnn|2.16ms|1.73ms
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## Test
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* Picture test
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```
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python3 test.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg
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python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg
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```
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<div align=center>
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<img src="https://github.com/dog-qiuqiu/FastestDet/blob/main/result.png"> />
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@ -110,7 +110,7 @@ AMD|R5-5600(X86-cpu)|Linux(amd64)|ncnn|2.16ms|1.73ms
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```
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### Build the training .yaml configuration file
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* Reference./configs/config.yaml
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* Reference./configs/coco.yaml
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```
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DATASET:
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TRAIN: "/home/qiuqiu/Desktop/coco2017/train2017.txt" # Train dataset path .txt file
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@ -134,12 +134,12 @@ TRAIN:
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### Train
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* Perform training tasks
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```
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python3 train.py --yaml configs/config.yaml
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python3 train.py --yaml configs/coco.yaml
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```
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### Evaluation
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* Calculate map evaluation
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```
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python3 eval.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth
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python3 eval.py --yaml configs/coco.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth
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```
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* COCO2017 evaluation
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```
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@ -170,7 +170,7 @@ TRAIN:
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## Export onnx
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* You can export .onnx by adding the --onnx option when executing test.py
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```
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python3 test.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg --onnx
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python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg --onnx
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```
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## onnx-runtime
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* You can learn about the pre and post-processing methods of FastestDet in this Sample
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@ -1,7 +1,7 @@
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DATASET:
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TRAIN: "/home/qiuqiu/Desktop/coco2017/train2017.txt"
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VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt"
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NAMES: "dataset/coco128/coco.names"
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NAMES: "configs/coco.names"
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MODEL:
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NC: 80
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INPUT_WIDTH: 512
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@ -1 +0,0 @@
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# pytorch-detector
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BIN
dataset/coco128/.DS_Store
vendored
BIN
dataset/coco128/.DS_Store
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Reference in New Issue
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