FOTS.PyTorch

by jiangxiluning

jiangxiluning / FOTS.PyTorch

FOTS Pytorch Implementation

485 Stars 158 Forks Last release: Not found BSD 3-Clause "New" or "Revised" License 116 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

How to get the theta argument of affine_grid? (Send me email if you need an english version)

Paper.创意.5.png

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 ?

    python
    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
  1. 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"
    }

}

  1. train your model
   python train.py -c config

Evaluation

python eval.py -m  -i  -o 

</model.tar.gz>

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.