Hi @LewsTherin511 thanks for the overview!
The reason we doing only pencil and pen because their shapes are quite identical and so it might be good dataset to check on how to improve the accuracy with retraining. 1. First, we did use the same tool LabelImg to annotate the images of pencils and pens. We also uses `imgaug` to create augmented images. 2. Next we train our first model with the [train_ssd.py](https://gluon-cv.mxnet.io/build/examples_detection/train_ssd_voc.html). For the custom dataset, we declared a VOCLike to be used in `get_dataset()`: > class VOCLike(VOCDetection): > CLASSES = ["pencil", "pen"] > > def __init__(self, root, splits, transform=None, index_map=None, > preload_label=True): > super(VOCLike, self).__init__(root, splits, transform, index_map, > preload_label) 3. After a few prediction with different images from the training and validation dataset, we found a few images that the model failed to recognized, and have annotated these new images with LabelImg + augmentation like what we did in step#1. However, the question come with the retraining the model with more dataset to the classes: 1. Do we just add the new images to existing dataset in the VOC folder, and train using the same script `train_ssd.py`? 2. Or do we retrain the existing model with [finetune_detection.py](https://gluon-cv.mxnet.io/build/examples_detection/finetune_detection.html)? The script `finetune_detection.py` uses custom model `ssd_512_mobilenet1.0_custom` instead. However, we noticed the script `net.reset_class(classes)`, which is not applicable to us, because we are training for the same classes? --- [Visit Topic](https://discuss.mxnet.io/t/retraining-ssd-for-pencils-and-pens/6528/3) or reply to this email to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.mxnet.io/email/unsubscribe/f74d5d4062c4b15e3764f682901d3b16a5f7bc1df6c474dff9e78c60e1231aac).
