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Benefits of Using the Cat Breed Identifier: 1. Quick and Accurate: Instantly identify cat breeds with a high level of accuracy. 2. Expand Knowledge: Learn about various cat breeds from different regions and environments. 3. Discover New Breeds: Identify and learn about cat breeds you encounter, whether at home, in shelters, or other settings. 4.
using AI. Below is a free classifier to identify cat breeds. Just upload your image, and our AI will predict which breed it is - in just seconds. Drop image or click to select. JPG, PNG, BMP, or WEBP. Powered by. For best results, limit the image to just the cat. Trying different images can help too. (Drag & drop one of the images to test it out)
Mar 11, 2023 · According to the American Humane Association, millions of cats and dogs are lost yearly. Only a few thousand of them are found and returned home. In this work, we use deep learning to help expedite the procedure of finding lost cats and dogs, for which a new dataset is collected. We applied transfer learning methods on different convolutional neural networks for species classification and ...
- Convolutional Neural Network
- Parameters in CNN
- Description of Deep-Cnn Models
- Plan of Action
- Performance Evaluation
A Convolutional neural network (CNN) is an image classification algorithm. It takes a set of images as input (training images), uses those images to adjust its learnable weights and biases by calculating loss from the image ground truth labels and backpropagating through the whole network. In this way, a CNN can predict the type of the object prese...
Parameters are the weights and biases present in the model that are tweaked by the optimizer during training. The actual knowledge about the data is stored in these parameters. Parameters contribute directly to the model’s predictive ability. Different layers in the CNN model contain a different number of trainable parameters depending on the type ...
CNN’s have achieved great success in the field of computer vision, especially in large scale image classification tasks. Their inherent ability to extract features directly from images has made it possible to overcome the drawbacks of traditional algorithms relying on hand-designed features. Deep-CNN models came into the picture in 2012 when AlexNe...
The process of optimal model selection has been divided into some steps and they work as a layered structure (Fig. 7). Every model has been trained and evaluated using the same procedure to maintain an unbiased scenario. The breed datasets have been created by collecting individual animal images belonging to various breeds. The dataset is then divi...
There are various evaluation metrics available for classification [61,62,63,64,65]. In this study, the primary objective is to train a deep learning-based model that should be able to predict the correct breed of an animal from among multiple breeds available for that animal. This falls under the multi-class classification problem. Such models can ...
- Pritam Ghosh, Subhranil Mustafi, Kaushik Mukherjee, Sanket Dan, Kunal Roy, Satyendra Nath Mandal, Sa...
- 2021
But here’s the catch: with over 70 recognized cat breeds, many of which share similar physical features, accurately identifying a cat’s breed by mere observation is a daunting task. Enter AI ...
Deep learning is one of the latest technologies in computer science. It allows using machines to solve problems in a manner similar to the human brain. Deep learning approaches have significantly improved the performance of visual recognition applications including image classification and image detection. In this paper we benchmark different deep learning models and present an Android ...
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Aug 7, 2024 · Most existing studies on pet breeds classification focus on images with simple backgrounds, leading to the unsatisfactory performance of models in practical applications. This paper investigates training pet breeds classification models using complex images and constructs a dataset for identifying breeds of pet cats and dogs. We use this dataset to fine-tune three SOTA models: ResNet34 ...
