# camera_identification

**Repository Path**: lwgaoxin/camera_identification

## Basic Information

- **Project Name**: camera_identification
- **Description**: IEEE's Signal Processing Society - Camera Model Identification
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-19
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# IEEE's Signal Processing Society - Camera Model Identification

Code for Kaggle competition: https://www.kaggle.com/c/sp-society-camera-model-identification

This repo contains code for training and predicting single Resnet50 model which achieves 98% accuracy on private LB.

# Run training

Download competition's dataset https://www.kaggle.com/c/sp-society-camera-model-identification/data

You can use more data from flickr to reduce overfitting.

Next, download pretrained weight from https://download.pytorch.org/models/resnet50-19c8e357.pth

Next, call
`python train.py --train_files train_files --val_files val_files --pretrained_weights_path resnet50-19c8e357.pth --batch_size 128 --model_save_path model.pth`

You will need some time to train a model. It takes ~4 hours on a single Tesla M40.

# Run prediction

Just call

`python predict.py --test_files test_files --batch_size 128 --model_path model.pth --submit_path submit.csv`

In the finish, there will be a file submit.csv which you will be able to submit on Kaggle.