# YOLOV3-DarkNet53-Quant Example
## Description
This is an example of training YOLOV3-DarkNet53-Quant with COCO2014 dataset in MindSpore.
## Requirements
- Install [MindSpore ](https://www.mindspore.cn/install/en ).
- Download the dataset COCO2014.
> Unzip the COCO2014 dataset to any path you want, the folder should include train and eval dataset as follows:
```
.
└─dataset
├─train2014
├─val2014
└─annotations
```
## Structure
```shell
.
└─yolov3_darknet53_quant
├─README.md
├─scripts
├─run_standalone_train.sh # launch standalone training(1p)
├─run_distribute_train.sh # launch distributed training(8p)
└─run_eval.sh # launch evaluating
├─src
├─__init__.py # python init file
├─config.py # parameter configuration
├─darknet.py # backbone of network
├─distributed_sampler.py # iterator of dataset
├─initializer.py # initializer of parameters
├─logger.py # log function
├─loss.py # loss function
├─lr_scheduler.py # generate learning rate
├─transforms.py # Preprocess data
├─util.py # util function
├─yolo.py # yolov3 network
├─yolo_dataset.py # create dataset for YOLOV3
├─eval.py # eval net
└─train.py # train net
```
## Running the example
### Train
#### Usage
```
# distributed training
sh run_distribute_train.sh [DATASET_PATH] [RESUME_YOLOV3] [RANK_TABLE_FILE]
# standalone training
sh run_standalone_train.sh [DATASET_PATH] [RESUME_YOLOV3]
```
#### Launch
```bash
# distributed training example(8p)
sh run_distribute_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_102400.ckpt rank_table_8p.json
# standalone training example(1p)
sh run_standalone_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_102400.ckpt
```
> About rank_table.json, You can generate it by using the [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
#### Result
Training result will be stored in the scripts path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in log.txt.
```
# distribute training result(8p)
epoch[0], iter[0], loss:483.341675, 0.31 imgs/sec, lr:0.0
epoch[0], iter[100], loss:55.690952, 3.46 imgs/sec, lr:0.0
epoch[0], iter[200], loss:54.045728, 126.54 imgs/sec, lr:0.0
epoch[0], iter[300], loss:48.771608, 133.04 imgs/sec, lr:0.0
epoch[0], iter[400], loss:48.486769, 139.69 imgs/sec, lr:0.0
epoch[0], iter[500], loss:48.649275, 143.29 imgs/sec, lr:0.0
epoch[0], iter[600], loss:44.731309, 144.03 imgs/sec, lr:0.0
epoch[1], iter[700], loss:43.037023, 136.08 imgs/sec, lr:0.0
epoch[1], iter[800], loss:41.514788, 132.94 imgs/sec, lr:0.0
…
epoch[133], iter[85700], loss:33.326716, 136.14 imgs/sec, lr:6.497331924038008e-06
epoch[133], iter[85800], loss:34.968744, 136.76 imgs/sec, lr:6.497331924038008e-06
epoch[134], iter[85900], loss:35.868543, 137.08 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86000], loss:35.740817, 139.49 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86100], loss:34.600463, 141.47 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86200], loss:36.641916, 137.91 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86300], loss:32.819769, 138.17 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86400], loss:35.603033, 142.23 imgs/sec, lr:1.6245529650404933e-06
epoch[134], iter[86500], loss:34.303755, 145.18 imgs/sec, lr:1.6245529650404933e-06
...
```
### Infer
#### Usage
```
# infer
sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [DEVICE_ID]
```
#### Launch
```bash
# infer with checkpoint
sh run_eval.sh dataset/coco2014/ checkpoint/0-131.ckpt 0
```
> checkpoint can be produced in training process.
#### Result
Inference result will be stored in the scripts path, whose folder name is "eval". Under this, you can find result like the followings in log.txt.
```
=============coco eval reulst=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.531
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.260
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.402
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.429
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558
```
