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145
README.md
145
README.md
@ -1,23 +1,38 @@
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***2022.7.14:Optimize loss, adopt IOU aware based on smooth L1, and the AP is significantly increased by 0.7***
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# :zap:FastestDet:zap:
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[](https://zenodo.org/badge/latestdoi/508635170)
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* ***Faster! Stronger! Simpler!***
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* ***It has better single core reasoning performance and simpler feature map post-processing than Yolo-fastest***
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* ***In the ARM CPU of RK3568, the single core reasoning performance is 50% higher than Yolo-fastest***
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* ***The coco evaluation index increased by 3.8% compared with the map0.5 of Yolo-fastest***
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* ***It has better performance and simpler feature map post-processing than Yolo-fastest***
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* ***The performance is 10% higher than Yolo-fastest***
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* ***The coco evaluation index increased by 1.2% compared with the map0.5 of Yolo-fastestv2***
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* ***算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604***
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# Evaluating indicator/Benchmark
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Network|COCO mAP(0.5)|Resolution|Run Time(4xCore)|Run Time(1xCore)|FLOPs(G)|Params(M)
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Network|mAPval 0.5|mAPval 0.5:0.95|Resolution|Run Time(4xCore)|Run Time(1xCore)|Params(M)
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:---:|:---:|:---:|:---:|:---:|:---:|:---:
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[Yolo-FastestV1.1](https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ModelZoo/yolo-fastest-1.1_coco)|24.40 %|320X320|26.60 ms|75.74 ms|0.252|0.35M
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[Yolo-FastestV2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|24.10 %|352X352|23.8 ms|68.9 ms|0.212|0.25M
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FastestDet|27.8%|512X512|21.51ms|34.62ms|*|0.25M
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* ***Test platform RK3568 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)***
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[yolov5s](https://github.com/ultralytics/yolov5)|56.8%|37.4%|640X640|395.31ms|1139.16ms|7.2M
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[yolov6n](https://github.com/meituan/YOLOv6)|-|30.8%|416X416|109.24ms|445.44ms|4.3M
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[yolox-nano](https://github.com/Megvii-BaseDetection/YOLOX)|-|25.8%|416X416|76.31ms|191.16ms|0.91M
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[nanodet_m](https://github.com/RangiLyu/nanodet)|-|20.6%|320X320|49.24ms|160.35ms|0.95M
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[yolo-fastestv1.1](https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ModelZoo/yolo-fastest-1.1_coco)|24.40%|-|320X320|26.60ms|75.74ms|0.35M
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[yolo-fastestv2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|24.10%|-|352X352|23.8ms|68.9ms|0.25M
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FastestDet|25.3%|13.0%|352X352|23.51ms|70.62ms|0.24M
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* ***Test platform Radxa Rock3A RK3568 ARM Cortex-A55 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)***
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* ***CPU lock frequency 2.0GHz***
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# Improvement
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* Anchor-Free
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* Single scale detector head
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* Cross grid multiple candidate targets
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* Dynamic positive and negative sample allocation
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# Multi-platform benchmark
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Equipment|Computing backend|System|Framework|Run time(Single core)|Run time(Multi core)
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:---:|:---:|:---:|:---:|:---:|:---:
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Radxa rock3a|RK3568(arm-cpu)|Linux(aarch64)|ncnn|70.62ms|23.51ms
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Radxa rock3a|RK3568(NPU)|Linux(aarch64)|rknn|28ms|-
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Qualcomm|Snapdragon 835(arm-cpu)|Android(aarch64)|ncnn|32.34ms|16.24ms
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Intel|i7-8700(X86-cpu)|Linux(amd64)|ncnn|4.51ms|4.33ms
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# How to use
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## Dependent installation
|
||||
* PiP(Note pytorch CUDA version selection)
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@ -27,7 +42,7 @@ FastestDet|27.8%|512X512|21.51ms|34.62ms|*|0.25M
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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_AP05:0.253207_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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@ -106,36 +121,36 @@ FastestDet|27.8%|512X512|21.51ms|34.62ms|*|0.25M
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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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```
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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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VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt" # Val dataset path .txt file
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NAMES: "dataset/coco128/coco.names" # .names category label file
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MODEL:
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NC: 80 # Number of detection categories
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INPUT_WIDTH: 512 # The width of the model input image
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INPUT_HEIGHT: 512 # The height of the model input image
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TRAIN:
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LR: 0.001 # Train learn rate
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THRESH: 0.25 # ？？？？
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WARMUP: true # Trun on warm up
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BATCH_SIZE: 64 # Batch size
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END_EPOCH: 350 # Train epichs
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MILESTIONES: # Declining learning rate steps
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- 150
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- 250
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- 300
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```
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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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||||
VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt" # Val dataset path .txt file
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||||
NAMES: "dataset/coco128/coco.names" # .names category label file
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||||
MODEL:
|
||||
NC: 80 # Number of detection categories
|
||||
INPUT_WIDTH: 352 # The width of the model input image
|
||||
INPUT_HEIGHT: 352 # The height of the model input image
|
||||
TRAIN:
|
||||
LR: 0.001 # Train learn rate
|
||||
THRESH: 0.25 # ？？？？
|
||||
WARMUP: true # Trun on warm up
|
||||
BATCH_SIZE: 64 # Batch size
|
||||
END_EPOCH: 350 # Train epichs
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||||
MILESTIONES: # Declining learning rate steps
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- 150
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- 250
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- 300
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```
|
||||
### Train
|
||||
* Perform training tasks
|
||||
```
|
||||
python3 train.py --yaml configs/config.yaml
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python3 train.py --yaml configs/coco.yaml
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```
|
||||
### Evaluation
|
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* Calculate map evaluation
|
||||
```
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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_AP05:0.253207_280-epoch.pth
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```
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* COCO2017 evaluation
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```
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@ -148,32 +163,54 @@ TRAIN:
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DONE (t=30.85s).
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Accumulating evaluation results...
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DONE (t=4.97s).
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.140
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.278
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||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.128
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||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018
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||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103
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||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.232
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||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.157
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.225
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032
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||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.201
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||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.130
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.253
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.119
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.129
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.237
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.142
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.208
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.043
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236
|
||||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372
|
||||
|
||||
```
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||||
# Deploy
|
||||
## Export onnx
|
||||
* You can export .onnx by adding the --onnx option when executing test.py
|
||||
```
|
||||
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg --onnx
|
||||
```
|
||||
## Export torchscript
|
||||
* You can export .pt by adding the --torchscript option when executing test.py
|
||||
```
|
||||
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg --torchscript
|
||||
```
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||||
## NCNN
|
||||
* Waiting for update
|
||||
* Need to compile ncnn and opencv in advance and modify the path in build.sh
|
||||
```
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||||
cd example/ncnn/
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sh build.sh
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./FastestDet
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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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||||
cd example/onnx-runtime
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pip install onnx-runtime
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python3 runtime.py
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||||
```
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||||
# Citation
|
||||
* If you find this project useful in your research, please consider cite:
|
||||
```
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||||
@misc{=FastestDet,
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title={FastestDet: Ultra lightweight anchor-free real-time object detection algorithm.},
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author={xuehao.ma},
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||||
howpublished = {\url{https://github.com/dog-qiuqiu/FastestDet}},
|
||||
year={2022}
|
||||
}
|
||||
```
|
||||
```
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||||
@misc{=FastestDet,
|
||||
title={FastestDet: Ultra lightweight anchor-free real-time object detection algorithm.},
|
||||
author={xuehao.ma},
|
||||
howpublished = {\url{https://github.com/dog-qiuqiu/FastestDet}},
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||||
year={2022}
|
||||
}
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||||
```
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||||
# Reference
|
||||
* https://github.com/Tencent/ncnn
|
||||
|
||||
@ -1,18 +1,18 @@
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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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INPUT_HEIGHT: 512
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INPUT_WIDTH: 352
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INPUT_HEIGHT: 352
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TRAIN:
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LR: 0.001
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THRESH: 0.25
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WARMUP: true
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BATCH_SIZE: 64
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END_EPOCH: 350
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BATCH_SIZE: 96
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END_EPOCH: 300
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MILESTIONES:
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- 150
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- 250
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- 300
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- 100
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- 200
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- 250
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Some files were not shown because too many files have changed in this diff Show More
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Reference in New Issue
Block a user