Cats And Dogs Dataset Download

This Kaggle Cats & Dogs dataset is created to train machines to detect dogs and cats from the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). It can be used to build better and break-proof protections for web services. These protection systems are used to protect against brute-force intrusion techniques, act also as blog spam detectors, etc.

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Download 🔥 https://tinurll.com/2y5SLr 🔥

This example shows how to do image classification from scratch, starting from JPEGimage files on disk, without leveraging pre-trained weights or a pre-made KerasApplication model. We demonstrate the workflow on the Kaggle Cats vs Dogs binaryclassification dataset.

When you don't have a large image dataset, it's a good practice to artificiallyintroduce sample diversity by applying random yet realistic transformations to thetraining images, such as random horizontal flipping or small random rotations. Thishelps expose the model to different aspects of the training data while slowing downoverfitting.

Our image are already in a standard size (180x180), as they are being yielded ascontiguous float32 batches by our dataset. However, their RGB channel values are inthe [0, 255] range. This is not ideal for a neural network;in general you should seek to make your input values small. Here, we willstandardize values to be in the [0, 1] by using a Rescaling layer at the start ofour model.

We have created a 37 category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.

We recommend the use of BitTorrent protocol. If its use is not possible, the dataset and annotations are also available for download over http as two separate files: images.tar.gz (dataset) and annotations.tar.gz (groundtruth data).

The following annotations are available for every image in the dataset: (a) species and breed name; (b) a tight bounding box (ROI) around the head of the animal; and (c) a pixel level foreground-background segmentation (Trimap).

The dataset is available to download for commercial/research purposes under a Creative Commons Attribution-ShareAlike 4.0 International License. The copyright remains with the original owners of the images.

In the first lesson of Part 1 v2, Jeremy encourages us to test the notebook on our own dataset. I know that there are some dataset already existing on Kaggle but it would certainly be nice to construct our personal ones to test our own ideas and find the limits of what neural networks can and cannot achieve.

If someone has a script for points 2) and 3) it would be nice to share it. And if some of you have recommendations/experience concerning the creation of an image dataset, it would of course be cool to share it too.

Beware of what limit you set here because the above query can go up to 140k + images (more than 70k each) if you would want to build a humongous dataset. You will still have to put it in correct directory structure though.

If the images were rescaled to 224x224, then why say there were 256x256? I checked the dogs-vs-cats dataset from kaggle in the folder and all the images are of different a various sizes much bigger or smaller than 256x256 (essentially all are randomly sized).

Hi, there I will try to answer your questions here.

1: If you resize the image using transforms.Resize(244,244) all images will be of the size 244 so if the book says the input to the first layer is 256 it is wrong it will be [batch_size, 3,244, 244]

If you use the Kaggle dataset (I found out first hand) you should resize them so they are all the same size or you will get errors.

you can do this if you are not 100% sure about the shape of the tensor when running the code; in your Forward method do this

In our examples we will use two sets of pictures, which we got from Kaggle: 1000 cats and 1000 dogs (although the original dataset had 12,500 cats and 12,500 dogs, we just took the first 1000 images for each class). We also use 400 additional samples from each class as validation data, to evaluate our models.

In the resulting competition, top entrants were able to score over 98% accuracy by using modern deep learning techniques. In our case, because we restrict ourselves to only 8% of the dataset, the problem is much harder.

A message that I hear often is that "deep learning is only relevant when you have a huge amount of data". While not entirely incorrect, this is somewhat misleading. Certainly, deep learning requires the ability to learn features automatically from the data, which is generally only possible when lots of training data is available --especially for problems where the input samples are very high-dimensional, like images. However, convolutional neural networks --a pillar algorithm of deep learning-- are by design one of the best models available for most "perceptual" problems (such as image classification), even with very little data to learn from. Training a convnet from scratch on a small image dataset will still yield reasonable results, without the need for any custom feature engineering. Convnets are just plain good. They are the right tool for the job.

But what's more, deep learning models are by nature highly repurposable: you can take, say, an image classification or speech-to-text model trained on a large-scale dataset then reuse it on a significantly different problem with only minor changes, as we will see in this post. Specifically in the case of computer vision, many pre-trained models (usually trained on the ImageNet dataset) are now publicly available for download and can be used to bootstrap powerful vision models out of very little data.

A more refined approach would be to leverage a network pre-trained on a large dataset. Such a network would have already learned features that are useful for most computer vision problems, and leveraging such features would allow us to reach a better accuracy than any method that would only rely on the available data.

We will use the VGG16 architecture, pre-trained on the ImageNet dataset --a model previously featured on this blog. Because the ImageNet dataset contains several "cat" classes (persian cat, siamese cat...) and many "dog" classes among its total of 1000 classes, this model will already have learned features that are relevant to our classification problem. In fact, it is possible that merely recording the softmax predictions of the model over our data rather than the bottleneck features would be enough to solve our dogs vs. cats classification problem extremely well. However, the method we present here is more likely to generalize well to a broader range of problems, including problems featuring classes absent from ImageNet.

We reach a validation accuracy of 0.90-0.91: not bad at all. This is definitely partly due to the fact that the base model was trained on a dataset that already featured dogs and cats (among hundreds of other classes).

To further improve our previous result, we can try to "fine-tune" the last convolutional block of the VGG16 model alongside the top-level classifier. Fine-tuning consist in starting from a trained network, then re-training it on a new dataset using very small weight updates. In our case, this can be done in 3 steps:

The dogs and cats dataset was first introduced for a Kaggle competition in 2013. To access the dataset, you will need to create a Kaggle account and to log in. No pressure, we're not here for the competition, but to learn!

The instructions to prepare the dataset are for Linux or macOS. If you work on Windows, I'm sure you can easily find a way to do that (e.g. use 7-zip do unpack the archive, and Windows Explorer to create directories and move files around).

To get a close look at this dataset, I used a fast image browser to check all images in the dogs and cats directories. Actually, I simply used the Preview application on my mac to browse through the small preview icons. The brain is very fast to spot obvious issues even if you just let your eyes wander on a large number of pictures. So this (tedious) work took me no more than 20 minutes. But of course, I have certainly missed a lot of less obvious issues.

Some of these images are completely meaningless, like 5604 and 8736.For 10401 and 10797, we actually see a cat in the picture! sigh... Keeping or not cartoon dogs is debatable. My feeling is that it's going to be better to remove them. Same for 6413, we could keep it, but I'm afraid that the network would focus on the drawings around the dog picture.

For example, from Jan. 1, 2017 through October 19, 2017, for dogs we had 559 Adoptions, 1,042 dogs returned to owner, and 2,656 transfers to rescue organizations. Our total Outcome for dogs for that time period was 5,805 dogs, and the total number of owner requests for euthanasia for dogs was 479. The number of dogs that were dead on arrival was 445. To calculate the LLR, you would do as follows:

Code is available in a jupyter notebook here. You will need to download the data from the Kaggle competition. The dataset contains 25,000 images of dogs and cats (12,500 from each class). We will create a new dataset containing 3 subsets, a training set with 16,000 images, a validation dataset with 4,500 images and a test set with 4,500 images.

All efforts have been made to ensure this dataset was collected in line with copyright legislation regarding fair use. All samples were collected via YouTube and any derivative works of this provided dataset must reference YouTube and the author of this dataset.

What I am trying to understand according to your instructions previously, is how to add the dataset to what is already there in coco128.yaml. I followed everything to the best knowledge I can but something is missing as the errors is continuously complaining about the dataset increasing from 80 to 81. 17dc91bb1f