SSD300

Single Shot MultiBox Detector implemented with TensorFlow

Dependencies

• python3.6.1 or greater
• numpy
• skimage
• TensorFlow
• matplotlib
• OpenCV
• tqdm

Usage

1. Import required modules

import tensorflow as tf
import numpy as np

from util.util import *
from model.SSD300 import *

2. Load test-image

img = load_image('./test.jpg')
img = img.reshape((300, 300, 3))

3. Start Session

with tf.Session() as sess:
        ssd = SSD300(sess)
        sess.run(tf.global_variables_initializer())
        for ep in range(EPOCH):
            ...

4. Training or Evaluating you must just call ssd.eval() !

...

_, _, batch_loc, batch_conf, batch_loss = ssd.eval(minibatch, actual_data, is_training=True)

...

Test Training

you have to extract data-set from zip files. decompress all zip files in datasets/ and move to voc2007/ dir.

$ ls voc2007/ | wc -l    #  => 4954
$ ./setup.sh
$ python train.py

Using Manga109 dataset

• Create the environment file by installing the packages in ./environment/environment_snapshot.yml.
• Unzip the Manga109 file. Rename folder to Manga109 and place in the root of this repo. The dataset can be downloaded from the Manga109 website by requesting access.
• Ensure that the variable USE_MANGA109 is set to true in trainer.py and run the script.
• After training, test an image using the command python inference.py Manga109/images/ARMS/002.jpg

Present Circumstances

I'm checking and testing SSD model, so this model may not be complete.

If I have overlooked something, please tell me.

Welcome PullRequest or E-mail