I was trying to train a Transformer model . But after 2epochs training abruptly stops with ^C. I tried training in Colab Pro+.
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I have a autoencoder model that I initialized and trained on a GPU machine with 128x128 sized-images dataset and a batch-size of 64. I saved the trained model file. Then I load this saved model file and restart training on a new dataset with 128x128 sized-images and batch-size of 64. But I get an OOM error this time. When the scenario is exactly same as before, why is it throwing an OOM error? (The GPU memory is 0% used every time I start training).
Im working on fine tuning detector model. I am freezing the detector in the following way before initiating the training:
detector = keras_ocr.detection.Detector(weights= 'clovaai_general')
for Layer in detector.model.layers:
Layer.trainable = False
detector.model.compile()
If I do not freeze the model , the model performance after training is very bad in terms of detecting the text. That means when I am not freezing , the training is overwriting the pretrained model weights.
My question in this regard is that if the way Im doing the freeze of pretrained model is correct? Or I should do freeze on VGG 16 backbone model only? Should I freeze only specific layers? Which specific layers should I freeze ? Please advise.
I start to training until ckpt-7 then I stopped training. Then again I started training but befor I changed pipline config in fine tune chekpoint on my model. I wrote latest check point and I changed its directory . My loss function approximetly 0.899 before stopped to the training.
When I continue to train but its start to steps 100 and my loss fuction 15.009.
How can I contiune the model before stopped? What should I do?
I am using centernet model with Colab.
Please explain I am new on that topic.
I could understand your question that you could not resume the training where it stopped.
Actually with the updates in TF2, we need not change the finetune checkpoint parameter in the pipeline.config. Re-run the same training script pointing to the same model_dir where your checkpoints are stored.
TF2 will automatically understand and resume from where the training stopped with the help of checkpoints created in the model_dir.
I am new to deep learning, I have a yolov3 model that I have been training on my custom data. Every time I train, the training seems to start from scratch. How do I make the model continue its training from where it stopped last time?
The setup I have is the same as this repo.
You can use model.load_weights(path_to_checkpoint) just after the model is defined at line 41 in train.py and continue training where you left off
I'm trying to train my own custom object detector. I have the tf records of both test and train and also label_map.tbtxt. there are a lot of issues with each step.