---
# System prepended metadata

title: ICV Project Proposal
tags: [Supervised learning, Food, Introduction to Computer Vision, Multiclass Classification]

---

# ICV Project Proposal
## Artemii Bykov, Maxim Salo B17-DS-01
###### tags: `Introduction to Computer Vision`, `Multiclass Classification`, `Supervised learning`, `Food`
1. **Project title:** Multiclass Food Classification
2. **Project idea:** The model should recognise food by image. It is very useful and can easily applicable to different areas: markets, agricultural industry and services for food delivery (with robots)
3. **Technique/Method:**
    * 1st approach:
        * As a base we are going to use pre-trained **InceptionV3** - a convolutional neural network for assisting in image analysis and object detection
        * Pretrained means that it already has **learned weights**
        * We want to add a few layers on the top to tune the NN for our dataset 
        * It will help us in faster convergence and saved time (Moreover, we have a huge dataset)
    * 2nd approach:
        * We want to try Wide-Slice Residual Networks with Slice Branch Network (you can find paper in the reference part)
4. **Dataset Explanation and [link](https://www.kaggle.com/dansbecker/food-101):**
    * This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images.
5. **Timeline:**
    * *Artemii Bykov*: Parse dataset and perform data augmentation
    * *Maxim Salo*: Read the docs for InceptionV3 and understand how to finetune the model
6. References
    * https://www.kaggle.com/theimgclist/multiclass-food-classification-using-tensorflow/data
    * https://www.tensorflow.org/datasets/catalog/food101
    * https://github.com/DucLeTrong/food101-classification
    * https://github.com/aurotripathy/food-classify
    * https://arxiv.org/pdf/1612.06543.pdf