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-# pytorch-detector
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+# :zap:FastestDet:zap:
+* ***Faster! Stronger! Simpler!***
+* ***It has better single core reasoning performance and simpler feature map post-processing than Yolo-fastest***
+* ***In the ARM CPU of RK3568, the single core reasoning performance is 50% higher than Yolo-fastest***
+* ***The coco evaluation index increased by 3.8% compared with the map0.5 of Yolo-fastest***
+* ***算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604***
+# Evaluating indicator/Benchmark
+Network|COCO mAP(0.5)|Resolution|Run Time(4xCore)|Run Time(1xCore)|FLOPs(G)|Params(M)
+:---:|:---:|:---:|:---:|:---:|:---:|:---:
+[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
+[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
+FastestDet|27.8%|512X512|21.51ms|34.62ms|*|0.25M
+
+* ***Test platform RK3568 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)***
+# Improvement
+* Anchor-Free
+* Single scale detector head
+* Cross grid multiple candidate targets
+* Dynamic positive and negative sample allocation
+# How to use
+## Dependent installation
+  * PIP
+  ```
+  pip3 install -r requirements.txt
+  ```
+## Test
+* Picture test
+  ```
+  python3 test.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.248723_280-epoch.pth --img data/3.jpg
+  ```
+<div align=center>
+<img src="https://github.com/dog-qiuqiu/Yolo-FastestV2/blob/main/img/000139_result.png"> />
+</div>
+
+## How to train
+### Building data sets(The dataset is constructed in the same way as darknet yolo)
+* The format of the data set is the same as that of Darknet Yolo, Each image corresponds to a .txt label file. The label format is also based on Darknet Yolo's data set label format: "category cx cy wh", where category is the category subscript, cx, cy are the coordinates of the center point of the normalized label box, and w, h are the normalized label box The width and height, .txt label file content example as follows:
+  ```
+  11 0.344192634561 0.611 0.416430594901 0.262
+  14 0.509915014164 0.51 0.974504249292 0.972
+  ```
+* The image and its corresponding label file have the same name and are stored in the same directory. The data file structure is as follows:
+  ```
+  .
+  ├── train
+  │   ├── 000001.jpg
+  │   ├── 000001.txt
+  │   ├── 000002.jpg
+  │   ├── 000002.txt
+  │   ├── 000003.jpg
+  │   └── 000003.txt
+  └── val
+      ├── 000043.jpg
+      ├── 000043.txt
+      ├── 000057.jpg
+      ├── 000057.txt
+      ├── 000070.jpg
+      └── 000070.txt
+  ```
+* Generate a dataset path .txt file, the example content is as follows：
+  
+  train.txt
+  ```
+  /home/qiuqiu/Desktop/dataset/train/000001.jpg
+  /home/qiuqiu/Desktop/dataset/train/000002.jpg
+  /home/qiuqiu/Desktop/dataset/train/000003.jpg
+  ```
+  val.txt
+  ```
+  /home/qiuqiu/Desktop/dataset/val/000070.jpg
+  /home/qiuqiu/Desktop/dataset/val/000043.jpg
+  /home/qiuqiu/Desktop/dataset/val/000057.jpg
+  ```
+* Generate the .names category label file, the sample content is as follows:
+ 
+  category.names
+  ```
+  person
+  bicycle
+  car
+  motorbike
+  ...
+  
+  ```
+* The directory structure of the finally constructed training data set is as follows:
+  ```
+  .
+  ├── category.names        # .names category label file
+  ├── train                 # train dataset
+  │   ├── 000001.jpg
+  │   ├── 000001.txt
+  │   ├── 000002.jpg
+  │   ├── 000002.txt
+  │   ├── 000003.jpg
+  │   └── 000003.txt
+  ├── train.txt              # train dataset path .txt file
+  ├── val                    # val dataset
+  │   ├── 000043.jpg
+  │   ├── 000043.txt
+  │   ├── 000057.jpg
+  │   ├── 000057.txt
+  │   ├── 000070.jpg
+  │   └── 000070.txt
+  └── val.txt                # val dataset path .txt file
+
+  ```
+### Build the training .yaml configuration file
+* Reference./configs/config.yaml
+```
+DATASET:
+  TRAIN: "/home/qiuqiu/Desktop/coco2017/train2017.txt"  # Train dataset path .txt file
+  VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt"      # Val dataset path .txt file 
+  NAMES: "dataset/coco128/coco.names"                   # .names category label file
+MODEL:
+  NC: 80                                                # Number of detection categories
+  INPUT_WIDTH: 512                                      # The width of the model input image
+  INPUT_HEIGHT: 512                                     # 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
+  MILESTIONES:                                          # Declining learning rate steps
+    - 150
+    - 250
+    - 300
+```
+### Train
+* Perform training tasks
+  ```
+  python3 train.py --yaml configs/config.yaml
+  ```
+### Evaluation
+* Calculate map evaluation
+  ```
+  python3 eval.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.248723_280-epoch.pth
+  ```
+# Deploy
+## NCNN
+* Waiting for update
+# Reference
+* https://github.com/Tencent/ncnn
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