YOLOv5 v6.0 compatibility update (#1857)

* Initial commit * Initial commit * Cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix precommit errors * Remove TF builds from CI * export last.pt * Created using Colaboratory * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2021-11-14 22:22:59 +01:00
parent 1be31704c9
commit 7eb23e3c1d
90 changed files with 6642 additions and 4145 deletions
@@ -1,68 +0,0 @@
-# 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 image=@zidane.jpg '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`
@@ -1,13 +0,0 @@
-"""Perform test request"""
-import pprint
-
-import requests
-
-DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-TEST_IMAGE = "zidane.jpg"
-
-image_data = open(TEST_IMAGE, "rb").read()
-
-response = requests.post(DETECTION_URL, files={"image": image_data}).json()
-
-pprint.pprint(response)
@@ -1,37 +0,0 @@
-"""
-Run a rest API exposing the yolov5s object detection model
-"""
-import argparse
-import io
-
-import torch
-from PIL import Image
-from flask import Flask, request
-
-app = Flask(__name__)
-
-DETECTION_URL = "/v1/object-detection/yolov5s"
-
-
-@app.route(DETECTION_URL, methods=["POST"])
-def predict():
-    if not request.method == "POST":
-        return
-
-    if request.files.get("image"):
-        image_file = request.files["image"]
-        image_bytes = image_file.read()
-
-        img = Image.open(io.BytesIO(image_bytes))
-
-        results = model(img, size=640)  # reduce size=320 for faster inference
-        return results.pandas().xyxy[0].to_json(orient="records")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Flask API exposing YOLOv3 model")
-    parser.add_argument("--port", default=5000, type=int, help="port number")
-    args = parser.parse_args()
-
-    model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force_reload to recache
-    app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat