# ABOUT
This is a clone from yolov5 with a simple API to query for detections. It's a simple demo to test model deployment as a microservice. The default detect method has been slightly modified to use a config dict instead of the given script arguments to facilitate the integration with the API
# First Steps
Once you have your machine up and running, createa new sudo user to avoid using root, it is not secure and might casue issues with some packages
`sudo useradd -s /path/to/shell -d /home/{dirname} -m -G {secondary-group} {username}`
## 1. Create Sudo User
Before following the instructions below, create a new virtual environment with virtualenv or [pyenv](https://github.com/pyenv/pyenv-installer)
## 2. Install Pyenv (virtual environment manager)
1. Install Pyenv Dependancies
`sudo apt update ; sudo apt install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python-openssl git`
2. Install pyenv
`curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash`
3. add the lines below to your .bashrc
```
export PATH="/home/ubuntu/.pyenv/bin:$PATH"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"
```
1. Run the following command to make sure pyenv is callable:
`source ~/.bashrc`
## 3. Create New virtualenv
2. Install python 3.8>
`pyenv install 3.8.3`
3. Create a new virtualenv with the installed version
`pyenv virtualenv 3.8.3 demoyolo`
4. Activate your virtualenv
`pyenv activate demoyolo`
5. Set the environment variables for the project
This projects uses a couple of environment variables, to set them copy the .env.example file to .env in the projects root directory, and change the environment variables needed (by default environment variables are already set to facilitate the demo)
`cp .env.example .env`
# Optional Steps
# Low RAM? , add a swapfile!
If your server has 3G<= of RAM `opencv`, and `torch` might fail to build. Instructions on how to add swap can be found here [here](https://linuxize.com/post/how-to-add-swap-space-on-ubuntu-20-04/)
# Yolov5's original readme.
1. Read the steps below to get the model working and be able to test the api..
2. Once the model is up and running, follow the steps ad api/README.md
<a href="https://apps.apple.com/app/id1452689527" target="_blank">
<img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="1000"></a>
 
![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
<img src="https://user-images.githubusercontent.com/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).
- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
- **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
- **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models.
## Pretrained Checkpoints
| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 37.0 | 37.0 | 56.2 | **2.4ms** | **416** || 7.5M | 13.2B
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 44.3 | 44.3 | 63.2 | 3.4ms | 294 || 21.8M | 39.4B
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 47.7 | 47.7 | 66.5 | 4.4ms | 227 || 47.8M | 88.1B
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | **49.2** | **49.2** | **67.7** | 6.9ms | 145 || 89.0M | 166.4B
| | | | | | || |
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms | 39 || 89.0M | 354.3B
| | | | | | || |
| [YOLOv3-SPP](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B
** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.001`
** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.1`
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce** by `python test.py --data coco.yaml --img 832 --augment`
## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.6`. To install run:
```bash
$ pip install -r requirements.txt
```
## Tutorials
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
## Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with al
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Simple Demo of yolov5 (FastAPI, Celery, Redis).zip (43个子文件)
cmd
__init__.py 0B
.env.example 55B
weights
download_weights.sh 245B
LICENSE 34KB
api
__init__.py 0B
config_files
reverse-proxy-yolo.conf 242B
gunicorn_api.service 388B
main.py 3KB
utils
__init__.py 0B
filesystem.py 631B
requirements.txt 115B
detect.py 7KB
README.md 2KB
utils
evolve.sh 747B
__init__.py 0B
google_utils.py 5KB
general.py 52KB
activations.py 2KB
datasets.py 38KB
torch_utils.py 9KB
sotabench.py 14KB
requirements.txt 569B
models
hub
yolov5-panet.yaml 1KB
yolov3-spp.yaml 1KB
yolov5-fpn.yaml 1KB
__init__.py 0B
export.py 3KB
yolov5m.yaml 1KB
yolov5s.yaml 1KB
yolov5l.yaml 1KB
common.py 4KB
experimental.py 5KB
yolov5x.yaml 1KB
yolo.py 11KB
detect.py 7KB
setup_scripts
setup_daemons.sh 1KB
setup_virtualenv.sh 2KB
setup_server.sh 1KB
.gitignore 4KB
settings.py 304B
train.py 27KB
test.py 13KB
README.md 12KB
共 43 条
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