Loading pre-trained weights properly in Pytorch Question: I would like to perform transfer learning by loading a pretrained vision transformer model, modify its last layer and training it with my own data. Hence, I am loading my dataset perform the typical transformation similar to the ImageNet, then, load the model, disable the grad from all …
Can't Import Pre Trained ResNet34 for U-Net Segmentation Question: I load the pre-trained ResNet34 for my downsampling path in encoder with this code from tensorflow.keras.applications import ResNet34 from tensorflow.keras.layers import Input # create the ResNet34 encoder inputs = Input(shape=(512, 512, 3)) encoder = ResNet34(include_top=False, weights=’imagenet’, input_tensor=inputs) # set encoder layers to non-trainable for layer in …
Setting ReLU inplace to 'False' Question: Below I have written code which accepts a pretrained model as argument (vgg, resnet, densenet etc) and returns the model with ReLU state as ‘False’. It is written after testing many different specific architectures. I would like to re-write it in a more compact way as this does not …
How to fine tune the last layers of neural network in target model for transfer learning? Question: I am learning how transfer learning works using this data https://www.kaggle.com/competitions/santander-customer-satisfaction/data .. so this is my simple source model code in tensorflow. and I am saving this model import pandas as pd pd.set_option(‘display.max_rows’, None) import numpy as np …
How to manage epochs when doing Transfer Learning and Fine-tuning Question: I am training a ResNet50 model and I want to apply fine-tuning after the initial training. This is when I train the model without fine-tuning: # Train initial model without fine-tuning initial_epochs = 100 history = model.fit(train_set, validation_data = dev_set, epochs=initial_epochs,verbose=1, callbacks=callbacks) And this …
Transfer learning from a custom model trained with different image sizes Question: I have a custom model trained initially on VGG16 using transfer learning. However, it was initially trained on images with a smaller input size. Now, I am using images with bigger sizes, therefore I’d like to grab the first model and take advantage …
Label Smoothing in PyTorch Question: I’m building a ResNet-18 classification model for the Stanford Cars dataset using transfer learning. I would like to implement label smoothing to penalize overconfident predictions and improve generalization. TensorFlow has a simple keyword argument in CrossEntropyLoss. Has anyone built a similar function for PyTorch that I could plug-and-play with? Asked …
