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Aug 4, 2023 · 🐶🐾 Welcome to our dogs breed Classification Tutorial! 🦖In this hands-on video, we'll take you on an exciting journey through the world of deep learning an...
- 31 min
- 1479
- Eran Feit
Jul 24, 2020 · About the competition:Who's a good dog? Who likes ear scratches? Well, it seems those fancy deep neural networks don't have all the answers. However, maybe t...
- 13 min
- 2090
- Data with Larry
Sep 20, 2020 · A deep learning model which predicts your dog breed. Let me know what you think.The source code (Open with Google Colab):https://drive.google.com/file/d/1DfF...
- 12 min
- 4.4K
- Yang Zhou
- Transfer Learning
- Importing Libraries
- Importing Dataset
- Image Input Pipeline
- Model Development
- Callback
In a convolutional neural network, the main task of the convolutional layers is to enhance the important features of an image. If a particular filter is used to identify the straight lines in an image then it will work for other images as well this is particularly what we do in transfer learning. There are models which are developed by researchers ...
Pythonlibraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code. 1. Pandas– This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go. 2. Numpy – Numpy arrays are very fast and can perform large computations in a very sho...
The dataset which we will use here has been taken from – https://www.kaggle.com/competitions/dog-breed-identification/data. This dataset includes 10,000 images of 120 different breeds of dogs. In this data set, we have a training images folder. test image folder and a CSV file that contains information regarding the image and the breed it belongs t...
There are times when the dataset is huge and we will be unable to load them into NumPy arrays in one go. Also, we want to apply some custom functions to our images randomly and uniquely such that the images with change do not take up disk space. In such cases image input pipelines build using tf.data.Dataset comes in handy. Output: Below are some o...
We will use pre-trained weight for an Inception network which is trained on imagenet dataset. This dataset contains millions of images for around 1000 classes of images. Output: Let’s check how deep or the number of layers are there in this pre-trained model. Output: This is how deep this model is this also justifies why this model is highly effect...
Callbacks are used to check whether the model is improving with each epoch or not. If not then what are the necessary steps to be taken like ReduceLROnPlateaudecreasing the learning rate further? Even then if model performance is not improving then training will be stopped by EarlyStopping. We can also define some custom callbacks to stop training ...
Correctly classify the breed of dog, for the images that are of dogs. Determine which CNN model architecture (ResNet, AlexNet, or VGG), "best" achieve the objectives 1 and 2. Consider the time resources required to best achieve objectives 1 and 2, and determine if an alternative solution would have given a "good enough" result, given the amount of time each of the algorithms take to run.
Aug 31, 2020 · It is divided in a train and a test sets, to train the CNN and measure its accuracy by testing it on unseen images. To develop a CNN from scratch, the main steps are: Load and preprocess data ...
Using Python skills, you will determine which image classification algorithm works the "best" on classifying images as "dogs" or "not dogs". Determine how well the "best" classification algorithm works on correctly identifying a dog's breed. If you are confused by the term image classifier look at it simply as a tool that has an input and an ...
