Preparing your images
|General image requirements|
|Supported file types||
|Types of images||
AutoML Vision Object Detection models are optimized for
photographs of objects in the real world.
|Training image file size (MB)||30MB maximum size.|
|Prediction image file* size (MB)||1.5MB maximum size.|
|Image size (pixels)||
1024 pixels by 1024 pixels suggested maximum.
For images much larger than 1024 pixels by 1024 pixels some image quality may be lost during AutoML Vision Object Detection's image normalization process.
|Labels and bounding box requirements|
|Label instances for training||
10 annotations (instances) minimum.
For each label you must have at least 10 images, each with at least one annotation (bounding box and the label).
However, for model training purposes it's recommended you use about 100 annotations per label. In general, the more images per label you have the better your model will perform.
|Label ratio (most common label to least common label):||
The model works best when there are at most 100x more images for the most common label than for the least common label.
For model performance, it is recommended that you remove very low frequency labels.
|Bounding box edge length||At least 0.01 * length of a side of an image. For example, a 1000 * 900 pixel image would require bounding boxes of at least 10 * 9 pixels.|
|Bounding box size (pixels)||8 pixels by 8 pixels minimum.|
|Bounding boxes per distinct image||500 maximum.|
|Bounding boxes returned from a prediction request||
100 (default), 500 maximum.
can specify this value as part of the
|Training data and dataset requirements|
|Training image characteristics||
The training data should be as close as possible to the data on which predictions are to be made.
For example, if your use case involves blurry and low-resolution images (such as from a security camera), your training data should be composed of blurry, low-resolution images. In general, you should also consider providing multiple angles, resolutions, and backgrounds for your training images.
AutoML Vision Object Detection models can't generally predict labels that humans can't assign. So, if a human can't be trained to assign labels by looking at the image for 1-2 seconds, the model likely can't be trained to do it either.
|Images in each dataset||150,000 maximum|
|Total annotated bounding boxes in each dataset||1,000,000 maximum|
|Number of labels in each dataset||1 minimum, 1,000 maximum|
Best practice guide
What kind of image data can you use?
- Supported image file formats: JPEG, PNG, GIF, BMP, or ICO.
- The training data should be as close as possible to the data on which predictions are to be made. For example, if your use case involves low-resolution images from cellphone cameras, your training data should be composed of low-resolution images. In general, you should also consider providing multiple angles, resolutions, and backgrounds for your training images. Another example is if you want to detect regions in high resolution images, do not train the model with cropped images.
What are the minimal conditions on data?
- The labels you use must be valid strings (no comma inside). The comma is only
an issue in
CSV-based importing. A way to address this issue is:
- The bounding boxes should be larger than 8 by 8 pixels in all cases. Bounding boxes that are smaller than this will be filtered.
- Images may exceed 1024*1024 pixels, but those images will be downscaled automatically, possibly leading to a loss in image quality. For this reason we recommend a maximum image size of 1024*1024 pixels. Images smaller than these dimensions will not be upscaled.
- All the bounding boxes should be inside images.
- The bounding boxes should be exhaustively labeled: if there are two cars in an image, all of them should be labeled.
How big does the dataset need to be?
- The more, the better. This is almost always true. However, an exception to this would be if adding more samples leads to imbalance or leakage (see below).
- The amount of data needed to train a good model depends on different factors:
- The amount of classes. The more unique classes you have, the more samples per class are needed.
- Complexity/diversity of classes. It's similar to humans: A human can probably quickly learn to distinguish between beer and wine, with just a few samples. He will have to try different wines quite more often to distinguish between 5-6 different sorts of red wines, and for many humans it will be challenging to learn to distinguish between 50 different flavors of red wines. At least one will have to practice a lot. Similarly, neural networks would quickly be able to distinguish between elephants and cats, but they would need many more samples to classify 30 different animals.
- As a rule of thumb, we recommend to have at least 100 training samples per class if you have distinctive and few classes, and more than 200 training samples if the classes are more nuanced and you have more than 50 different classes.
Training vs. evaluation datasets
When training machine learning models you typically divide the dataset usually into three separate datasets:
- a training dataset
- a validation dataset
- a test dataset
A training dataset is used to build a model. The model being trained tries multiple hyper-parameters while searching for patterns in the training data. During the process of pattern identification, AutoML Vision Object Detection uses the validation dataset to test the hyperparameters of the model. AutoML Vision Object Detection chooses the best-performing algorithms and patterns from all options identified during the training stage.
After the best performing algorithms and patterns have been identified they are tested for error rate, quality, and accuracy using the test dataset. Customers must have a separate test dataset that they can use to test the model independently. This test dataset is either specified in the training set by the user or chosen automatically at training time.
Both a validation and a test dataset are used in order to avoid bias in the model. During the validation stage, optimal model parameters are used. Using these optimal model parameters can result in biased metrics. Using the test dataset to assess the quality of the model after the validation stage provides the training process with an unbiased assessment of the quality of the model.
If you manually choose dataset samples, you should structure datasets in a way that represents the same population. Similarly, you should create dataset splits that have similar images, all with a similar distribution of labels.
Manual and automatic dataset splits
You can manually specify the split of training, validation, and test when importing datasets in a CSV file.
If you do not specify it, AutoML Vision Object Detection will randomly split your data. Splits are created in the following manner:
- 80% of images are used for training.
- 10% of images are used for hyper-parameter tuning and/or to decide when to stop training.
- 10% of images are used for evaluating the model. These images are not used in training.
Imbalanced data: In many cases the number of samples per class (label) is not equal. Minor imbalances don't generally create issues, but larger discrepancies between classes can cause an issue. When there is a bigger imbalance, eg. some classes are represented more than 10 times that of other classes, this becomes problematic for model building. While there are approaches to counteract class imbalances, it's not an ideal configuration for model training. When possible, try to avoid model training with highly imbalanced data.
As a general rule, keep the ratio between the most common and the least common classes below 2 to 1.
Bad splits: When you provide training data, AutoML Vision Object Detection can automatically split it into training, validation, and testing datasets. You can also assign the train split labels yourself as well.
There is no guarantee that you will get the same split if you import the same data multiple times.
The training, validation and testing data should not have strong correlation. For example, a common bad case is that when the images are from videos, leading many images to be very similar to each other. If you let the system randomly split the dataset for you it will be very likely that you will get highly similar images in both training and validation/testing data. This will lead to an incorrectly obtained high accuracy on testing data.
Data leakage: Data leakage is a significant problem that can bias models. Data leakage happens when the algorithm is able to use information during model training that it should not, and that will not be available during future predictions. It leads to overly optimistic results on train, validation, and potentially test datasets. However, this performance might not be as good on some future unseen data. This happens often unintentionally, and requires special care during data preparation.
Leakage examples: Positives and negatives from different image sources, or viewing angles.