Contents
How many labels does ImageNet have?
ImageNet contains more than 20,000 categories with a typical category, such as “balloon” or “strawberry”, consisting of several hundred images. The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet.
How do I download data from ImageNet?
ImageNet Download: Go to https://www.kaggle.com/c/imagenet-object-localization-challenge and click on the data tab. You can use the Kaggle API to download on a remote computer, or that page to download all the files you want directly. There, they provide both the labels and the image data.
What are the 1000 classes of ImageNet?
IMAGENET 1000 Class List
| Class ID | Class Name |
|---|---|
| 0 | tench, Tinca tinca |
| 1 | goldfish, Carassius auratus |
| 2 | great white shark, white shark, man-eater, man-eating shark, Carcharodon caharias’, |
| 3 | tiger shark, Galeocerdo cuvieri |
How long does it take to train ImageNet?
Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 10^18 single precision operations in total.
How does ImageNet process?
Steps to pre-processing the full ImageNet dataset
- Verify that you have space on the download target.
- Set up the target directories.
- Register on the ImageNet site and request download permission.
- Download the dataset to local disk or Compute Engine VM.
- Run the pre-processing and upload script.
How can I find ImageNet data labels?
During test, do the same, but extract a center 224×224 patch, and use that for evaluating classification accuracy. Some people also use multiple patches for testing. Again, it’s up to you, and you can use higher resolution if you like.
How many images are in the ImageNet dataset?
The dataset spans 200 image classes with 500 training examples per class. The dataset also has 50 validation and 50 test examples per class. The images are down-sampled to 64×64 pixels vs. 256×256 for full ImageNet. The full ImageNet dataset has 1000 classes vs. 200 classes in Tiny ImageNet.
How to find ImageNet data labels in TensorFlow?
You will download three tar archives: one for training data, one for validation data, and one for test data. Training data is contained in 1000 folders, one folder per class ( each folder should contain 1,300 JPEG images ).
How many classes are there in tiny ImageNet?
Tiny ImageNet spans 200 image classes with 500 training examples per class. The post also explores alternatives to the cross-entropy loss function. And, finally, I show pictures with their predictions vs. true labels, saliency maps, and visualizations the convolution filters.