Pramay

> Because I used the ICDAR15 vocab to correct it. You can add --use_vocab=False to disable it. Anyway this model is not very good, I'm training a new one now....

Just write these lines before your code ``` from barcode.base import Barcode Barcode.default_writer_options['write_text'] = False ```

> I managed to retrain your approach with my own dataset and it performance quit well! However, the runtime/inference speed seems to be slower than compared with several other approaches...

Hey, are you running this on google Colab? I also encountered a similar error in colab and that was due to google drive time out. Re-running the cell helped. If...

I modified this https://github.com/Wovchena/text-detection-fots.pytorch/blob/04b969c87491630dce38cdb48932aa33115798c6/datasets.py#L159 to ```def __getitem__(self, idx): img = cv2.imread(os.path.join(os.path.join(self.root, self.img_dir), self.image_prefix[idx] + '.jpg'), cv2.IMREAD_COLOR).astype(np.float32) quads = [] texts = [] # lines = [line.rstrip('\n') for line in open(os.path.join(os.path.join(self.root,...

Hey there might be an error in the way I have created annotations. Suppose I have set of polygons points- [(x1,y1)...] so this is how I create the ICDAR format...

So I ran the test.py and saw the visualizations of gt are correctly drawn. So this guarantees that the annotations are correct, right?

Ohh...so which is the correct order of points to be followed?

Actually I meant which point should come first for eg..upper-left, lower-left, etc

Hey I tried training on images with size 384x384 , train loss curve was also fine. But if I resize test images(as mentioned above) the results are incorrect and when...