https://github.com/inaturalist/inatVisionAPI

https://github.com/inaturalist/inatVisionAPI

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/inaturalist/inatVisionAPI
• Owner: inaturalist
• License: mit
• Created: 2017-02-03T21:25:37.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2024-07-03T08:23:30.000Z (3 months ago)
• Last Synced: 2024-07-10T07:48:41.441Z (3 months ago)
• Language: Python
• Size: 362 KB
• Stars: 32
• Watchers: 10
• Forks: 7
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# computervision

We're doing some computer vision stuff at iNat.

#### models

We don't share our models publicly. You could train your own models or perhaps find one from one of the model zoos and adapt it.

#### os x dependencies

- `brew install libmagic`

#### python

- `python3 -m venv venv`

- `source ./venv/bin/activate`

- `pip3 install -U pip`

- `pip3 install -r requirements.txt`

#### installation

Here's a rough script for OS X assuming you already have homebrew, Python, and virtualenv installed.

```bash

# Get dependencies

brew install libmagic

# Get the repo

git clone [email protected]:inaturalist/inatVisionAPI.git

cd inatVisionAPI/

# Set up your python environment

python3 -m venv venv

source venv/bin/activate

pip3 install -U pip

pip3 install -r requirements.txt

# Copy your config file (and edit, of course)

cp config.yml.example config.yml

# Run the app

python app.py

```

Now you should be able to test at http://localhost:6006 via the browser.

##### Notes

If the device you're installing on has AVX extensions (check flags in /proc/cpuinfo), try compiling tensorflow for better performance:

https://www.tensorflow.org/install/install_sources

This is a good idea on AWS or bare metal, but won't make a difference on Rackspace due to them using an old hypervisor.

If you're not compiling, install tensorflow from pip: `pip install tensorflow`

If the device you're installing on has AVX2 or SSE4, install pillow-simd for faster image resizing:

`pip install pillow-simd` if you only have SSE4, or `CC="cc -mavx2" pip install pillow-simd` if you have AVX2. I saw a significant increase in performance from pillow to pillow-simd with SSE4, less of an increase for AVX2.

otherwise, install pillow from pip: `pip install pillow`

tensorflow seems to want to compile against your system copy of numpy on OS X regardless of the virtualenv, so if you see stupid errors like `ImportError: numpy.core.multiarray failed to import`, try running `deactivate` to get out the virtualenv, then `pip install -U numpy` or somesuch to update your system copy of numpy. Then `source inatvision-venv/bin/activate` to get back in your virtualend and try again.

Some performance data from a 15" MBP, 2.5GHz i7:

| task               | pip tensorflow | compiled tensorflow | compiled tensorflow + pillow-simd |

| ------------------ | -------------- | ------------------- | --------------------------------- |

| 100x medium.jpg    | 25 seconds     | 17 seconds          | 15 seconds                        |

| 100x iphone photos | 81 seconds     | 72 seconds          | 46 seconds                        | 

The larger the images coming into the pipeline, the more important optimized resize (like pillow-simd) is.