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📚 This guide explains how to **freeze** YOLOv5 🚀 layers when **transfer learning**. Transfer learning is a useful way to quickly retrain a model on new data without having to retrain the entire networ…
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### Motivation and description
A common practice in machine learning is to take a pre-trained model and fine-tune it on a particular dataset. This typically involves freezing the weights in some la…
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Obtained results does not give appreciable results using LeNet model.
Further extend transfer learning using Inception, VGG16 models
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I generated the docs as I saw something on transfer learning, however, the docs don't seem complete. Is there a way to load the ReNet18 with weights based on ImageNet or CIFAR? Or has this not yet bee…
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Highly cited/influential, see https://www.semanticscholar.org/paper/A-Simple-Multi-Modality-Transfer-Learning-Baseline-Chen-Wei/33703b1bfecb918aea4dcc2644a759f1de37c940
Two-Stream Network follows …
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Hi I was wondering if transfer learning freezes all the layers apart from the output layer?
and is there a way we can control the number of layers to freeze?
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I have a pre-trained model from EasyOCR(None-VGG-BiLSTM-CTC), and I want to retrain it on my own data.
Question: Should I freeze FeatureExtraction and SequenceModeling part and just fine-tune CTC?
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@WongKinYiu
Hello Dear @WongKinYiu. I wanted to implement transfer learning in YOLOR-p6 weight. But it is a bit different than Scaled-YOLOv4.
So, can you explain if possible how to do transfer…