summer316 / FOTS.PyTorch

FOTS Pytorch Implementation

Sorry about the pause of updating，since I had more important envent, ICDAR 2019 SROIE, to work on. Now, I am planning to pay off the technical debt.

I have finished the detection branch and am still training the model to verify its correctness. All the features will be published to develop branch, and keep master stable.

• ICDAR Dataset
• SynthText 800K Dataset
• detection branch (verified on the training set, It works!)
• eval
• multi-gpu training
• crnn (not be verified)
• reasonable project structure
• val loss
• tensorboardx visualization

Introduction

This is a PyTorch implementation of FOTS.

Questions

• Should I fix weights of the backbone network, resnet50 ?

  for param in self.backbone.parameters():
      param.requires_grad = False

  Answer: Yes, the backbone network is used as a feature extractor, so we do not need to modify the weights.

• For crnn, the padding size should all be 1, since the width may less than the kernel size, and the outputs' sizes of conv layer in CRNN are all the same?

Instruction

Requirements

1. build tools

   ./build.sh
   

2. prepare ICDAR Dataset

Training

1. understand your training configuration

   {
        "name": "FOTS",
        "cuda": false,
        "gpus": [0, 1, 2, 3],
        "data_loader": {
            "dataset":"icdar2015",
            "data_dir": "/Users/luning/Dev/data/icdar/icdar2015/4.4/training",
            "batch_size": 32,
            "shuffle": true,
            "workers": 4
        },
        "validation": {
            "validation_split": 0.1,
            "shuffle": true
        },
    
        "lr_scheduler_type": "ExponentialLR",
        "lr_scheduler_freq": 10000,
        "lr_scheduler": {
                "gamma": 0.94
        },
     
        "optimizer_type": "Adam",
        "optimizer": {
            "lr": 0.0001,
            "weight_decay": 1e-5
        },
        "loss": "FOTSLoss",
        "metrics": ["my_metric", "my_metric2"],
        "trainer": {
            "epochs": 100000,
            "save_dir": "saved/",
            "save_freq": 10,
            "verbosity": 2,
            "monitor": "val_loss",
            "monitor_mode": "min"
        },
        "arch": "FOTSModel",
        "model": {
            "mode": "detection"
        }
   }
   
   

2. train your model

   python train.py -c config
   
   

Evaluation

python eval.py -m <model.tar.gz> -i <input_images_folder> -o <output_folders>