# Flask REST API
[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
## Requirements
[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
```shell
$ pip install Flask
```
## Run
After Flask installation run:
```shell
$ python3 restapi.py --port 5000
```
Then use [curl](https://curl.se/) to perform a request:
```shell
$ curl -X POST -F [email protected] 'http://localhost:5000/v1/object-detection/yolov5s'`
```
The model inference results are returned as a JSON response:
```json
[
{
"class": 0,
"confidence": 0.8900438547,
"height": 0.9318675399,
"name": "person",
"width": 0.3264600933,
"xcenter": 0.7438579798,
"ycenter": 0.5207948685
},
{
"class": 0,
"confidence": 0.8440024257,
"height": 0.7155083418,
"name": "person",
"width": 0.6546785235,
"xcenter": 0.427829951,
"ycenter": 0.6334488392
},
{
"class": 27,
"confidence": 0.3771208823,
"height": 0.3902671337,
"name": "tie",
"width": 0.0696444362,
"xcenter": 0.3675483763,
"ycenter": 0.7991207838
},
{
"class": 27,
"confidence": 0.3527112305,
"height": 0.1540903747,
"name": "tie",
"width": 0.0336618312,
"xcenter": 0.7814827561,
"ycenter": 0.5065554976
}
]
```
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
Yolov5水果分类识别+pyqt交互式界面
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Yolov5水果分类识别+pyqt交互式界面 （102个子文件） 
Dockerfile 821B .DS_Store 6KB .DS_Store 6KB .gitignore 12B YoloToONNX.ipynb 4KB README.md 2KB README.md 1KB datasets.py 44KB train.py 33KB general.py 28KB plots.py 19KB test.py 17KB common.py 16KB wandb_utils.py 16KB yolo.py 13KB torch_utils.py 12KB loss.py 9KB detect.py 9KB metrics.py 9KB autoanchor.py 7KB export.py 7KB hubconf.py 6KB experimental.py 5KB google_utils.py 5KB detector.py 4KB activations.py 4KB main.py 3KB resume.py 1KB restapi.py 1KB log_dataset.py 800B example_request.py 299B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B __init__.py 0B datasets.cpython-37.pyc 33KB datasets.cpython-38.pyc 33KB general.cpython-38.pyc 23KB general.cpython-37.pyc 23KB common.cpython-37.pyc 19KB common.cpython-38.pyc 18KB plots.cpython-37.pyc 17KB plots.cpython-38.pyc 16KB torch_utils.cpython-38.pyc 11KB torch_utils.cpython-37.pyc 11KB yolo.cpython-38.pyc 11KB yolo.cpython-37.pyc 11KB metrics.cpython-37.pyc 8KB metrics.cpython-38.pyc 7KB autoanchor.cpython-37.pyc 6KB autoanchor.cpython-38.pyc 6KB experimental.cpython-37.pyc 6KB experimental.cpython-38.pyc 6KB google_utils.cpython-38.pyc 3KB google_utils.cpython-37.pyc 3KB __init__.cpython-38.pyc 172B __init__.cpython-38.pyc 171B __init__.cpython-37.pyc 164B __init__.cpython-37.pyc 163B main.pyproject 43B get_voc.sh 4KB get_argoverse_hd.sh 2KB userdata.sh 1KB get_coco.sh 962B mime.sh 780B get_coco128.sh 618B download_weights.sh 277B
requirements.txt 613B additional_requirements.txt 105B form.ui 3KB main.pyproject.user 10KB objects365.yaml 7KB anchors.yaml 3KB VisDrone.yaml 3KB SKU-110K.yaml 2KB yolov5-p7.yaml 2KB GlobalWheat2020.yaml 2KB yolov5x6.yaml 2KB yolov5s6.yaml 2KB yolov5m6.yaml 2KB yolov5l6.yaml 2KB yolov5-p6.yaml 2KB yolov5-p2.yaml 2KB coco.yaml 2KB hyp.scratch.yaml 2KB coco128.yaml 2KB yolov3-spp.yaml 1KB yolov3.yaml 1KB yolov5-panet.yaml 1KB yolov5s-transformer.yaml 1KB yolov5m.yaml 1KB yolov5s.yaml 1KB yolov5x.yaml 1KB yolov5l.yaml 1KB yolov5-fpn.yaml 1KB yolov3-tiny.yaml 1KB argoverse_hd.yaml 848B hyp.finetune.yaml 846B voc.yaml 737B共 102 条
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