I had around 360 images splitted %25 as validation data. I could train Deeplabv3 with those images without any issue. Later on I have added around 40 images wi
Question: How can I download an older version of PyTorch geometric in google colab? Context: I am trying to use/load a pytorch-geometric graph and am getting th
I'm trying to find the appropriate learning rate for my Neural Network using PyTorch. I've implemented the torch.optim.lr_scheduler.CyclicLR to get the learning
I am using ResNext architecture for classification. the training dataset contains approximately 31000 images distributed among 61 classes. And validation datase
I have defined few parameters in my config.yaml like as below. params: epochs: 10 batch_size: 128 num_classes: 10 loss_function: sparse_categorical_cros
I watched the following video on YouTube https://www.youtube.com/watch?v=jx9iyQZhSwI where it was shown that it is possible to use Gradio and the learned model
I'm working on a lane detection project and I need to load the lane video into my colab for processing from youtube. Can i directly upload using the video's lin
I want to build a new computer for Data Science purposes. What do you think about this hardware: https://www.ldlc.com/configurateur-pc/23fe088422141bb69274a13ca
Is the GlobalAveragePooling1D Layer the same like calculating the mean with a custom Lambda Layer? The data is temporal, so x has shape (batch, time, features)
Does PyTorch's nn.Embedding support manually setting the embedding weights for only specific values? I know I could set the weights of the entire embedding laye
The app can be viewed in huggingface https://huggingface.co/spaces/rowel/asr import gradio as gr from transformers import pipeline model = pipeline(task="auto
I have Keras model: pre-trained CV model + a few added layers on top I would want to be able to do model.predict before model.fit Q: how do I instantiate model
I am trying to figure out if I can use fastai for my problem. I am trying to classify sequences of floats. Each sequence is a vector of 24 floats. In principle,
I am new to tensorflow. i've tried to fit X and y both shape=8 float64 tensors X as feature set and y as target set. X = np.array([-7.0, -4.0, -1.0, 2.0, 5.0, 8
Hello guys i am a biggner at computer vision and classification, i am trying to train a model using cnn method with tensorflow and keras, but i keep getting the
I'm trying to split DNN Models in order to execute part of the network on the edge and the rest on the cloud. Because it has to be cross-platform and work with
I am building an address matching algorithm. The main problem is that previous models like Conditional Random fields (CRF)from Paserator and Averaged Perceptron
I am trying to run some example python3 code https://docs.databricks.com/applications/deep-learning/distributed-training/horovod-runner.html on databricks GPU c
input_shape=(100,100,6) input_tensor=keras.Input(input_shape) model.add(Conv2D(32, 3, padding='same', activation='relu', input_shape=input_shape))
This is the code from https://keras.io/examples/vision/image_classification_from_scratch/ import tensorflow as tf from tensorflow import keras from tensorflo