Poor validation, is CSV generator working correctly ?

Hello, I tried to make a “licenceplate” detector to get regions with retinanet and I annotated images to get that (don’t trust w and h, that are not width and height but x2 and y2 pixel coords):

So I saved CSV and get this format:

licenceplates/040603/P1010001.jpg,209,214,429,257,licenseplate

Of course there are several other images in CSV, that’s just a check !

And of course, classes.csv file is:

licenseplate,1

Then:

batch_size=16
image_side = 800
train_generator = csv_generator.CSVGenerator(
    'out/train.csv',
    csv_class_file='out/classes.csv',
    base_dir='.',
    batch_size=batch_size,
    image_max_side=image_side)
validation_generator = csv_generator.CSVGenerator(
    'out/validation.csv',
    csv_class_file='out/classes.csv', 
    base_dir='.',
    batch_size=batch_size,
    image_max_side=image_side)

epochs=100
ts = train_ds.shape[0] // batch_size
vs = validation_ds.shape[0] // batch_size
model = resnet.resnet_retinanet(2)
# model = vgg_retinanet(2)
model.compile(
    loss={
        'regression'    : losses.smooth_l1(),
        'classification': losses.focal()
    },
    optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001)
)
model.fit_generator(train_generator,
                    epochs=epochs,
                    verbose=1,
                    steps_per_epoch=ts,
                    validation_data=validation_generator,
                    validation_steps = vs)

I’ve got 0.4 in validation loss after 100 epochs, and boxes are not correct (that detects something else than licenseplates, as sun reflection, line on road, and so on)

Examples:

So I wonder:

• are we ok that the format is ok for CSV => filename,x1,y1,x2,y2,classname
• are we ok that 0,0 is the top left of image ?
• are we ok that values are pixels and not a number between 0 and 1 ?

That should help me to understand what’s wrong 😃

[metal3d]

@nuwanjkit I’m creating a web app named “imannotate” that prepares annotations. It returns a basic CSV containing image,label,coords. (https://github.com/smileinnovation/imannotate => devel branch)

Afterward, I only “download” each image and change “relative coords” to “pixel coords”, and I’m using the code that I gave in my first comment.

When I want to make prediction, I’m using the code in my second comment with “max_side” argument in “resize_image” function call corresponding to the “max_side” that I gave during the train process.

I’m writing a Medium article to work with imannotate tool, and maybe a Medium to treat CSV, train and predict on images 😉

[hgaiser]

I think Slack will be better suited to have these discussions if you don’t mind 😃