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https://github.com/terasakisatoshi/chainer-food-101-revised
An example of Chainer software that use Food-101 dataset
https://github.com/terasakisatoshi/chainer-food-101-revised
chainerv6 deeplearning food101
Last synced: 3 days ago
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An example of Chainer software that use Food-101 dataset
- Host: GitHub
- URL: https://github.com/terasakisatoshi/chainer-food-101-revised
- Owner: terasakisatoshi
- License: mit
- Created: 2019-08-03T06:46:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-08-12T05:11:38.000Z (over 5 years ago)
- Last Synced: 2024-10-11T12:41:07.728Z (about 1 month ago)
- Topics: chainerv6, deeplearning, food101
- Language: Python
- Homepage:
- Size: 47.9 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# chainer-food-101-revised
Classify category of food with [Chainer](https://chainer.org/)
- This is an implementation of food 101 Classification with Chainer
- This repository is revised version of my repository [chainer-food-101](https://github.com/terasakisatoshi/chainer-food-101). The old repository contains some bugs (Sorry my delay response...) and ugly implementation, so please use the revised repository instead of old one.
- The difference between old one is ...
- Clean structured implementation
- Update The latest version Chainer/CuPy
- Use ChainerCV to transform images for data augmentation.
- This library is very useful for Chainer users.
- Provide example that use ResNet50 provided ChainerCV project on training.
- Multi GPU training is supported
- `$ python train.py --device 0 1`
- add notebook to observe dataset
- You'll notice Chainer is easy to read/write even create Pythonic code rather than other Deep Learning Framework.
# How to train
## prepare dataset
- Download [Food-101 Data Set: Food-101 -- Mining Discriminative Components with Random Forests](https://www.vision.ee.ethz.ch/datasets_extra/food-101/)
```console
$ mkdir ~/dataset
$ cd ~/dataset
$ wget http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz
$ tar xfvz food-101.tar.gz
```
- Then the structure of directory under this repository should be...
```
tree -d ~/dataset/food-101
├── images
│ ├── apple_pie
│ ├── baby_back_ribs
│ ├── baklava
│ ├── beef_carpaccio
│ ├── beef_tartare
│ ├── beet_salad
│ ├── beignets
│ ├── bibimbap
│ ├── bread_pudding
│ ├── breakfast_burrito
│ ├── bruschetta
│ ├── caesar_salad
│ ├── cannoli
│ ├── caprese_salad
│ ├── carrot_cake
│ ├── ceviche
│ ├── cheese_plate
│ ├── cheesecake
│ ├── chicken_curry
│ ├── chicken_quesadilla
│ ├── chicken_wings
│ ├── chocolate_cake
│ ├── chocolate_mousse
│ ├── churros
│ ├── clam_chowder
│ ├── club_sandwich
│ ├── crab_cakes
│ ├── creme_brulee
│ ├── croque_madame
│ ├── cup_cakes
│ ├── deviled_eggs
│ ├── donuts
│ ├── dumplings
│ ├── edamame
│ ├── eggs_benedict
│ ├── escargots
│ ├── falafel
│ ├── filet_mignon
│ ├── fish_and_chips
│ ├── foie_gras
│ ├── french_fries
│ ├── french_onion_soup
│ ├── french_toast
│ ├── fried_calamari
│ ├── fried_rice
│ ├── frozen_yogurt
│ ├── garlic_bread
│ ├── gnocchi
│ ├── greek_salad
│ ├── grilled_cheese_sandwich
│ ├── grilled_salmon
│ ├── guacamole
│ ├── gyoza
│ ├── hamburger
│ ├── hot_and_sour_soup
│ ├── hot_dog
│ ├── huevos_rancheros
│ ├── hummus
│ ├── ice_cream
│ ├── lasagna
│ ├── lobster_bisque
│ ├── lobster_roll_sandwich
│ ├── macaroni_and_cheese
│ ├── macarons
│ ├── miso_soup
│ ├── mussels
│ ├── nachos
│ ├── omelette
│ ├── onion_rings
│ ├── oysters
│ ├── pad_thai
│ ├── paella
│ ├── pancakes
│ ├── panna_cotta
│ ├── peking_duck
│ ├── pho
│ ├── pizza
│ ├── pork_chop
│ ├── poutine
│ ├── prime_rib
│ ├── pulled_pork_sandwich
│ ├── ramen
│ ├── ravioli
│ ├── red_velvet_cake
│ ├── risotto
│ ├── samosa
│ ├── sashimi
│ ├── scallops
│ ├── seaweed_salad
│ ├── shrimp_and_grits
│ ├── spaghetti_bolognese
│ ├── spaghetti_carbonara
│ ├── spring_rolls
│ ├── steak
│ ├── strawberry_shortcake
│ ├── sushi
│ ├── tacos
│ ├── takoyaki
│ ├── tiramisu
│ ├── tuna_tartare
│ └── waffles
└── meta
```
## Prepare Python Modules
For examle
- Python
- NumPy, Matplotlib, OpenCV
- Chainer(6.2.0), CuPy(6.2.0), ChainerCV(0.13.1)
- Note that the vesion of Chainer and CuPy must be same.
## clone this repository:
```
$ mkdir ~/work
$ cd ~/work
$ git clone https://github.com/terasakisatoshi/chainer-food-101-revised.git
```
## Start training:
- Just do it:
```
$ python train.py
# or specify the location of training objects
$ python train.py --model resnet
# or
$ python train.py --model mv2
```
This script will create `trained` directory (by default) will be created. this directory is going to store snapshot i.e. trainer object or model weights. you can add `--destination` option to specify the location.
- If you like to train using multi gpu, please add `--device` option to specify which gpu use on training. For example
```
$ python train.py --device 0 1
```
- For more information e.g. how to know to configure batch size ..., just try:
```
$ python train.py --help
```
O.K.
## Evaluation
- just run:
```
$ python predict.py
# or specify the location of training objects
$ python predict.py --trained /path/to/directory
```
## Run demo.py
```
$ python demo.py trained
```
## Hard to train ??
O.K. go to our [release page](https://github.com/terasakisatoshi/chainer-food-101-revised/releases)
- ResNet50
```
top1 accuracy 0.8372673267326732
top5 accuracy 0.9601980198019802
```
- MobileNetV2
```
top1 accuracy 0.7696237623762376
top5 accuracy 0.9343762376237624
```