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https://github.com/myatmyintzuthin/jpfood-classification-pytorch
Classification Models [ Pytorch implementation from stratch]
https://github.com/myatmyintzuthin/jpfood-classification-pytorch
classification japanese-food pytorch pytorch-implementation
Last synced: 16 days ago
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Classification Models [ Pytorch implementation from stratch]
- Host: GitHub
- URL: https://github.com/myatmyintzuthin/jpfood-classification-pytorch
- Owner: myatmyintzuthin
- Created: 2022-10-28T03:36:54.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-03-23T08:21:41.000Z (almost 2 years ago)
- Last Synced: 2023-08-02T10:39:38.647Z (over 1 year ago)
- Topics: classification, japanese-food, pytorch, pytorch-implementation
- Language: Python
- Homepage:
- Size: 1.02 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Japanese Food Classification
This repository contains training, evaluation and testing of classification models implementated from stratch using PyTorch.
## Classes
Ramen :ramen: , Sashimi :fish: , Sushi :sushi: , Takoyaki :dango:
## Available Models
| Model | Variants |
| ----------- | ----------- |
| VGG | 11, 13, 16, 19 |
| Resnet | 18, 34, 50, 101, 152 |
| Mobilenet | v2, v3-small, v3-large |
| Shufflenet | v2 |
| ConvNeXt | tiny, small, base, large |
## 1. Dependencies
Please install essential dependencies (see `requirements.txt`)
### 1.1. Create conda virtual environment
```bash
conda create -n jpfood python==3.8
```
### 1.2. Activate environment
```bash
conda activate jpfood
```
### 1.3. Install dependencies
```bash
pip install -r requirements.txt
```
## 2. Dataset
The dataset used in this repository is a subset of [Food-101 dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).\
The original dataset contains 1000 images for each class. \
\
In this repository, train - 200, validation - 100 and test - 100 images are used.\
Japanese food dataset can be downloaded from [link](https://github.com/myatmyintzuthin/japanese-food-classification/releases/download/v1_dataset/japanese_food.zip)
## 3. Prepare pretrained models
Model implementations are available in `models/{model}.py` folder. \
To initialize PyTorch's pretrained model weights to our model architecture, run:
```bash
python convert.py --model resnet --variant 50
options:
--model : Model name {vgg, resnet, mobilenet}
--variant : Model variant
--width_mulit : channel width ratio for mobilenet
--num_class : 1000 default for ImageNet classes
```
Pretrained models will be saved as `pretrained/{model}.pt`
## 4. Prepare Config
Change model and variant of desired model before training and testing in `config.yaml`.
```
model:
name: 'resnet'
variant: '50'
```
Hyperparameters recommendation:
| Model | Optimizer | Learning_rate |
| ----------- | ----------| ------------- |
| VGG | sgd | 0.001 |
| Resnet | sgd | 0.001 |
| Mobilenet | sgd | 0.001 |
| Shufflenet | sgd | 0.005 |
| ConvNeXt | adamw | 0.00005 |
## 5. Training and evaluation
For training, run:
```bash
python train.py
options:
--eval : To evaluate the model without training again
--eval_model : model path for evaluation
```
The result for each model training is saved in `experiments/{model_name}{datetime}/` folder.
## 6. Inference
For inference, run:
```bash
python inference.py --image {image_path} --viz
options:
--image : Folder path to inference images
--viz : Whether to visualize inferece results
```
for C++ inference, change required inputs and run:
```bash
python trace_model.py
```
to convert pytorch model to script model.
After tracing the model, use it in
[libtorch-inference repo.](https://github.com/myatmyintzuthin/libtorch-inference)
### Inference Result
\
## Reference
- [Torch Vision](https://github.com/pytorch/vision/tree/main/torchvision/models)
- [Zero to Mastery Learn PyTorch for Deep Learning](https://www.learnpytorch.io/)