iNaturalist19

Training code for an Inception 3 model on the iNaturalist 2019 fine grain classification dataset.

After 30 training epochs and 46 fine-tuning the model achieved an accuracy of 72.3% on the test set which was 47th (out of 214) in the Kaggle competition.

Techniques used on the network are:

• Data augmentation with the ImageNet AutoAugment policies (Link)
• Training on high resoluition 560x560 images (Link) *
• Fine-tuning on a balanced subset of the full dataset with a small learning rate (Link) **

* The large increase in number of flops from training on high resolution images did not give a worth while advantage over training on a lower resolution for more epochs. 448x448 is likely ideal for training on a single GPU.

** The technique described in the paper is simply a slightly fancier version of down sampling that gave marginal performance increase.

Requirements

• Python 3
• Pytorch

Required Files

• Download the iNaturalist 2019 dataset from here.
• Download the pretrained Inception 3 weights from here.
• Organize the downloaded files into following file hierarchy:

iNaturalist19
│   ...
│
└───data
│   │   class_weights
│   │   test2019.json
│   │   train2019.json
│   │   val2019.json
│   │ 
│   └───test2019 
│   │   │  ...
│   │   │  
│   │
│   └───train_val2019
│       │  ...
│       │  
│            
└───models
│   │   iNat_2018_InceptionV3.pth.tar 
│   │   inception3.py
│   │            
│   └───checkpoints
│       │  *empty*         
│       │  
│
│   ...
│

Usage

To train, run python3 train.py which will train the network for 10 epochs on the entire dataset and 10 epochs on the balanced dataset with 1/4 the size.

To evaluate a model, run python eval.py which will evaluate checkpoint.pth.tar on the validation set.

Arugments

• train.py:

  • --checkpoint: file to resume training from (default: none)
  • --train_file: training set annotation file
  • --val_file: validation set annotation file
  • --test_file: testing set anotation file
  • --data_root: directory where test2019 and train_val2019 directories are found
  • --epochs: number of epochs on full dataset
  • --epochs_ft: number of epochs for fine-tuning
  • --size: resolution of the images (default: 560)
  • --batch_size: (default: 16)
  • --lr: learning rate for full dataset training (default: 0.0075)
  • --lr_ft: learning rate for fine-tuning (default: 0.00075)
  • --momentum: (default: 0.9)
  • --weight_decay: (default: 1e-4)
  • --lr_decay: proportion for learning rate decay (default: 0.94)
  • --lr_decay_rate: per how many epochs does the lr decay during training (default: 2)
  • --lr_decay_rate_ft: per how many epochs does the lr decay during fine-tuning (default: 4)
  • --n_workers: (default: 4)
  • --ft_clip: shrink the dataset to 1/ft_clip the size of the full dataset (default: 4)

• eval.py:

  • --mode:
    • val: evaluate on validation set (gives accuracy)
    • test: evaluate on test set (no accuracy)
  • --weights: checkpoint file of trained model
  • --val_file: validation set annotation file
  • --test_file: testing set anotation file
  • --data_root: directory where test2019 and train_val2019 directories are found
  • --size: resolution of the images (default: 560)
  • --batch_size: (default: 16)