You only look once (YOLO) is a state-of-the-art, real-time object detection system. YOLOv3 is extremely fast and accurate.
- Download the dataset COCO2014.
Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.
YOLOv3 use a totally different approach. It apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.
> Unzip the COCO2014 dataset to any path you want, the folder should include train and eval dataset as follows:
YOLOv3 uses a few tricks to improve training and increase performance, including: multi-scale predictions, a better backbone classifier, and more. The full details are in the paper!
[Paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf): YOLOv3: An Incremental Improvement. Joseph Redmon, Ali Farhadi,
University of Washington
# [Model Architecture](#contents)
YOLOv3 use DarkNet53 for performing feature extraction, which is a hybrid approach between the network used in YOLOv2, Darknet-19, and that newfangled residual network stuff. DarkNet53 uses successive 3 × 3 and 1 × 1 convolutional layers and has some shortcut connections as well and is significantly larger. It has 53 convolutional layers.
- Note：Data will be processed in yolo_dataset.py, and unzip files before uses it.
# [Environment Requirements](#contents)
- Hardware（Ascend）
- Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
The model checkpoint will be saved in outputs directory.
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
### Distributed Training
#### Result
```
sh run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
```
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.
The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log.txt`. The loss value will be achieved as follows:
sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
```
> 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.
The above python command will run in the background. You can view the results through the file "log.txt". The mAP of the test dataset will be as follows:
```
# log.txt
=============coco eval reulst=========
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528
...
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@@ -131,3 +283,51 @@ Inference result will be stored in the scripts path, whose folder name is "eval"
