import torch import torch.nn as nn import torch.nn.functional as F class double_conv(nn.Module): ”'(conv => BN => ReLU) * 2”’ def __init__(self, in_ch, out_ch): super(double_conv, self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_ch, out_ch, 3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True), nn.Conv2d(out_ch, out_ch, 3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True) ) def forward(self, x): x = self.conv(x) return x class inconv(nn.Module): def __init__(self, in_ch, out_ch): ..
I am doing a research on multibranched network vs. non-branched network, I am using the UNET as example. The input is an normal image of dimension(256,256,3)and the output is the a set of images concatenated togethor with dimension(256,256,9). In my experiment, I used traditional UNET with single conv layer, the network works fine. Since the ..
I’m fairly new to machine learning. I was trying to do some semantic segmentation using Segmentation models on Google colab. I just installed Segmentations models with the line. pip install git+https://github.com/qubvel/segmentation_models then, at the "model=sm.Unet(~~" line from the block. I got the error code. BACKBONE = ‘resnet34’ preprocess_input = sm.get_preprocessing(BACKBONE) # define model model = ..
I have trained 9 different UNets for image segmentation, each on the same dataset but with different masks since each UNet segments a different part of the image. I have also tried to train one UNet in a multiclass scenario but the individual UNets performed better than the multiclass one and thus I am taking ..
I am working on brain tumor segmentation and got this error: ValueError: Input 0 of layer conv2d is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, 1) input_img = Input((240, 240, 4)) model = Unet(input_img,32,0.4,True) model.summary() learning_rate = 0.00015 decay_rate = 0.0000001 model.compile(optimizer=Adam(lr=learning_rate,decay = decay_rate),loss=’binary_crossentropy’, metrics=[dice_coef_2]) model.fit_generator(generator=training_generator, steps_per_epoch=10, epochs=5) Source: ..
I tried to use U-net from https://github.com/HZCTony/U-net-with-multiple-classification the model is same, except for output layer. conv10 = Conv2D(num_class, 1, activation = ‘softmax’)(conv9) my classes’s number is 4, so I changed data.py code def adjustData(img,mask,flag_multi_class,num_class): if(flag_multi_class): img = img / 255. mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0] mask[(mask!=255.)&(mask!=10.)&(mask!=30.)&(mask!=40.)] = 0. new_mask = np.zeros(mask.shape + ..
RuntimeError: CUDA out of memory. Tried to allocate 818.00 MiB (GPU 0; 2.00 GiB total capacity; 1.05 GiB already allocated; 178.93 MiB free; 1.06 GiB reserved in total by PyTorch) I have Nvidia Geforce GTX 850M (Dedicated memory : 2GB) Nvidia Driver version : 465.89 CUDA Version : 11.3 I was using Jupyter Notebook and ..
I am doing biomedical image segmentation on U-Net. How can I make my model more robust so that it won’t extract the background without performing ROI extraction? I am using Keras library to develop the network. Thanks. Source: Python..
I am using the Image segmentation guide by fchollet to perform semantic segmentation. I have attempted modifying the guide to suit my dataset by labelling the 8-bit img mask values into 1 and 2 like in the Oxford Pets dataset. Question is how do I get the IoU metric of a single class (e.g 1)? ..
Hello guys I am having issue with my unet model I have created for my thesis on tumour segmentation and I am kinda stuck for a couple of weeks. I get very inconsistent results like 0 accuracy or non improving loss function and I think the error is in how I parse in the data ..