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training model with other dataset #9
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Het, @wangbofei11 there's three possibility:
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@hellochick Thank you。I will have a try。By the way,I see you have got a good result with the NYU indoor image, Did you start your training weights from the pretrained model of cityscape provided by author or without any pretrained model? |
Hello @wangbofei11 , I started my training weights from the pre-trained model of cityscapes, and then trained on the ADE20k dataset ( I use 27 classes instead 150 classes ). |
Hello @hellochick , How can I train with other dataset from scratch? I mean without starting with a pre-trained model. I checked the train.py, it seems that what I should do is either to have something in the snapshots directory or load from a existing model. And to my understanding, the snapshots directory is the a place for snapshots of the model during training. So how can I train the model in the beginning? And by the way, I am a newbie in this area (both Python (Tensorflow) and the theory of deep neuron network). The only experience I have comes from roughly going through the CS231n lecture of Stanford online. So I have some basic questions, maybe a little stupid.
And thank you very much for your implementation. |
Yes, @wangbofei11 of course you can train the model in the beginning, but I suggest you to load the Imagenet pretrained model at first, or it cannot recognize anything. For your questions, here are my opinions:
If you have another question, feel free to ask me. |
@hellochick Thank you for your reply. But according to the if-else brunch in train.py, it will either continue the training using snapshot or load an existing model. But if I train from scratch, neither the snapshot nor the model do I have. If a just comment out if-else part (line 180-186 in train.py), the loss is always Nan. And the reason I want to train from scratch is I have some new labels (the lane mark and the ego lane) to train. For a classification network (which has fully-connected layers), I know it is possible to tune the network by re-train the fully-connected layers and the conv layers almost keep the same. But for segmentation network (which has no fully-connected layers), I don't know how to tune it when new labels are involved, maybe keep the encoders and re-train the decoder? I'm not sure and don't know how to implement the tuning either. |
@hellochick And for question 1, where can I specify the mapping between the label and the class? For example if I want to mark a pixel as class 1, which color should I use to do the labeling? |
happy new year, everyone, I am trying to training the ICNet with voc2012 dataset and coco2017 dataset, after the training , the loss was about 0.05. but the inference result and the evaluate result was terrible wrong? i thing you @wangbofei11 @hellochick may had doing this. so can you talk me ,what's you train result using voc or coco, and what parameter you using? |
Dear @hellochick, thank you very much for opening the source code of your implementation! I have few questions. Your response is very important to me!
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I used voc2012(21 categories including background) dataset for training without pretrained model,only change the 'NUM_CLASSES' to 21 in your training code,but after about 200 steps,the total loss can not be droped(about 0.5),the result is completely wrong. can you give some suggestion on training with other dataset ? tks
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