- 13 Nov 2023
- 14 Minutes to read
- Print
- DarkLight
- PDF
Custom Training
- Updated on 13 Nov 2023
- 14 Minutes to read
- Print
- DarkLight
- PDF
Custom Training (also called "Advanced Training") is an advanced option recommended for users who are familiar with machine learning. Custom Training allows these users to have more granular control over pre-processing transforms and manual hyperparameter tuning.
Run Custom Training
- Open the Project you want to train.
- Click the downward-facing arrow next to Train and click Train with Custom Options.
Train with Custom Options
- Configure the settings and hyperparameters for custom model training.
Configure Settings and Hyperparameters
- After configuring the settings, click Train.
Train the Model
- LandingLens runs the Model Training process and creates a model. You can then save the model, just as you would for a Fast Train Model. For more information, go to Train Models.
Custom Model Settings
This section describes the settings you can configure to train a Custom Model. LandingLens applies some settings by default. Those defaults display on the Train Model page.
Data Transform
Data Transform allows you to resize or crop all the images in your dataset. This is an important feature because all training images need to be a standard input size for your model. Any Transforms applied here will be applied to the data you provide to your model for training and inference. It's important to be sure that the objects/regions you are trying to detect are clearly visible after applying these Transforms; otherwise, you can risk training poorly performing models.
If you add a Data Transform, you must add a Resize Transform first. This ensures that all data is consistently sized before cropping. Furthermore, resizing your data first prevents you from accidentally cropping outside the image boundaries of small images.
To add Data Transforms:
- Click Add Transform.Add Transform
- Click Resize or Crop accordingly.
Resize or Crop
- Configure the settings.
- Click Add to Pipeline to apply the change to all images in your dataset.
Configure Settings and Add to Pipeline
Resize Images
When you use Custom Training, images are resized to the dimensions you provide. The maximum image dimensions are based on the Project Type, as described in the following table.
Project Type | Maximum Area | Example Maximum Image Size |
---|---|---|
Object Detection | 2,250,000px | 1500x1500px |
Classification | 2,250,000px | 1500x1500px |
Segmentation | 984,064px | 992x992px |
Anomaly Detection | 262,144px | 512x512px |
The maximum image size is measured by the area, not width and height. For example, the maximum image size for Object Detection is 1500x1500px. To get the area, multiply 1500 times 1500, so the equation looks like this:
1500 x 1500 = 2,250,000.
If you want to resize an Object Detection image, the area cannot exceed 2,250,000px. For example:
- A resize of 1000x600px will work because the area of this resize is 600,000px, which is fewer than 640,000px.
- A resize of 1500x1501px will not work because the area of this resize is 2,251,500px, which is greater than 2,250,000px.
Resize Settings
There are two types of Resize settings:
- Manual Resize: Enter the dimensions you want your images to be when you train the model. For example, if the original images are 1200x800 and the Resize is 512x512, all images will be resized into square images, even though the original images were in a 4:3 ratio. If you want to preserve the original aspect ratio, use Rescale with Padding.Manual Resize
- Rescale with Padding: Enter the dimensions you want your images to be when you train the model. The image will be rescaled to that size and keep the original aspect ratio during resizing. This is done by adding padding of a constant pixel value of "0" to the image if the requested resize is not the same aspect ratio as the original image. If the resized image has the same aspect ratio, there will be no padded pixels.
For example, say the original size of an image is 1200x800, and you rescale it to 512x512. Padding will be added around the rescaled image of 512x314 to make the height of the image 512. This is because the aspect ratio of the original image is 4:3, and the requested rescale is 1:1; thus, the extra space in the y dimension must be padded.Note:Rescale with Padding is set by default. You can adjust this setting as needed.Rescale with Padding
Resize: Large Image Support
If you are on LandingLens Enterprise, you can resize your images in Object Detection and Segmentation Projects to larger than the regular maximums. If you resize images to be larger than the following dimensions, the images are considered to be "large images":
- Object Detection: Over 1500x1500px
- Segmentation: Over 992x992px
With LandingLens Enterprise, you can resize your images up to these maximums:
- Object Detection: Up to 36MP
- Segmentation: Up to 36MP
If you resize your images to be "large images", you will only be able to deploy the model using LandingEdge. Cloud Deployment doesn't support models that were created with large images. Additionally, you won't be able to run the Predict tool on these models.
Crop
You can manually crop all images in your dataset. To do this, click and drag the sides of the box to create the crop you want to use for that image and click Add to Pipeline. Then use the left and right arrows to preview the crop of the other images in your dataset.
.gif)
Data Augmentation
The Data Augmentation setting offers several pre-processing modifications that allow you to update all images in your dataset quickly. Here's how it works. First, you select a modification. Then enter the probability that the modification will take effect. For example, you can make it so there's a 50% chance that images will be rotated vertically.
Here are some benefits of adding Data Augmentations:
- You can reduce the time spent collecting data by augmenting images to "create" images you would have otherwise had to capture.
- Augmentations can create variations of your images so your model can be more robust when you deploy it.
- It reduces the chance of overfitting because the model will be shown random versions of images during each epoch cycle to prevent models from simply "memorizing" the data.
This section describes the types of Data Augmentation LandingLens offers.
.png)
Random Brightness
Apply Random Brightness for a chance that the images in your dataset will randomly brighten.
For example, say your camera took several images at night, and the objects in the image are dark. You want your model also to learn how images will look if they are taken during the day. You may be asking yourself why this is important. When you deploy or train a model with poorly lit images, it can perform poorly because when the lighting changes look "different" to the model, it may not recognize the area to detect. In this case, you can use the Random Brightness modification to improve the lighting.
This modification offers many settings, as described in the table below.

# | Setting | Description |
---|---|---|
1 | RandomBrightness Range | Images have a chance to be brightened to any value in the range specified here. |
2 | Probability | The likelihood that images will have random brightness applied. |
3 | Random Brightness | Use this setting to preview how images will look in the selected value. |
4 | Set as Lower Limit | Set a value in the RandomBrightness modification to preview how images will look at that brightness level. Then click Set as Lower Limit to choose the lowest brightness level that you want images to display in. The value in the RandomBrightness setting (#3) will be set as the lowest value in the RandomBrightness Range (#1). |
5 | Set as Upper Limit | Set a value in the RandomBrightness modification to preview how images will look at that brightness level. Then click Set as Upper Limit to choose the highest brightness level that you want images to display in. The value in the RandomBrightness setting (#3) will be set as the highest value in the RandomBrightness Range (#1). |
Blur / Motion Blur / Gaussian Blur
Apply one of the blur modifications for a chance that the images in your dataset will randomly blur. LandingLens offers these types of blurs:
- Blur: Makes the entire image look out of focus. This is a heavy blur.
- Motion Blur: Makes the entire image look like it's in motion.
- Gaussian Blur: Makes the entire image look out of focus. This is a natural blur.
This modification offers many settings, as described in the table below.

# | Setting | Description |
---|---|---|
1 | Blur Range / MotionBlur Range / Gaussian Blur Range | Images have a chance to be blurred to any value in the range specified here. |
2 | Probability | The likelihood that images will have random blur applied. |
3 | Blur / Motion Blur / Gaussian Blur | Use this setting to see how the images will look in the selected value. |
4 | Set as Lower Limit | Set a value in a blur modification to preview how images will look at that blur level. Then click Set as Lower Limit to choose the lowest blur level that you want images to display in. The value in the Blur / Motion Blur / Gaussian Blur setting (#3) will be set as the lowest value in the Blur Range / Motion Blur Range / Gaussian Blur Range (#1). |
5 | Set as Upper Limit | Set a value in a blur modification to preview how images will look at that blur level. Then click Set as Upper Limit to choose the highest blur level that you want images to display in. The value in the Blur / Motion Blur / Gaussian Blur setting (#3) will be set as the highest value in the Blur Range / Motion Blur Range / Gaussian Blur Range (#1). |
Hue Saturation Value
Apply Hue Saturation Value for a chance that the color intensity of the images will change.
For example, some objects come in multiple colors, like a car. You may want to change the hue (color of the image), so the model trains on these various colors.
This modification offers many settings, as described in the table below.

# | Setting | Description |
---|---|---|
1 | Hue / Sat / Val Shift Range | Images have a chance to change the Hue Saturation Value to any value in the range specified here. |
2 | Probability | The likelihood that images will have random Hue Saturation Value applied. |
3 | HueSaturationValue | Use this setting to see how images will look in the selected value. |
4 | Set as Lower Limit | Set a value in the HueSatuationValue modification to preview how images will look at that HSV value. Then click Set as Lower Limit to choose the lowest HSV level that you want images to display in. The value in the HueSaturationValue setting (#3) will be set as the lowest value in the Hue/Sat/Val Shift Range (#1). |
5 | Set as Upper Limit | Set a value in the HueSaturationValue modification to preview how images will look at that HSV value. Then click Set as Upper Limit to choose the highest HSV level that you want images to display in. The value in the HueSaturationValue setting (#3) will be set as the highest value in the Hue/Sat/Val Shift Range (#1). |
Random Contrast
Apply Random Contrast for a chance that the tone of the images in your dataset will randomly change. Random Contrast helps to simulate different camera settings or environments that a deployed model may encounter.
- A low-contrast image will look washed out or slightly dimmed.
- A high-contrast image will look somewhat overexposed, and the colors will merge into each other.
This modification offers many settings, as described in the table below.

# | Setting | Description |
---|---|---|
1 | RandomContrast Range | Images have a chance to have added contrast to any value in the range specified here. |
2 | Probability | The likelihood that images will have random contrast applied. |
3 | RandomContrast | Use this setting to preview how images will look in the selected value. |
4 | Set as Lower Limit | Set a value in the RandomContrast modification to preview how images will look at that contrast level. Then select Set as Lower Limit to choose the lowest contrast level that you want to images to display in. The value in the RandomContrast setting (#3) will be set as the lowest value in the RandomContrast Range (#1). |
5 | Set as Upper Limit | Set a value in the RandomContrast modification to preview how images will look at that contrast level. Then select Set as Upper Limit to choose the highest contrast level that you want images to display in. The value in the RandomContrast setting (#3) will be set as the highest value in the RandomContrast Range (#1). |
Horizontal Flip / Vertical Flip
- Apply Horizontal Flip for a chance that images in your dataset will rotate 180 degrees left or right. This is useful for models to "learn" what objects would look like if they are mirrored.Note:Horizontal Flip is set by default. You can adjust this setting as needed.
- Apply Vertical Flip for a chance that images in your dataset will rotate 180 degrees up or down. This is useful for models to "learn" what objects would look like if they were upside-down.
These modifications offer the Probability setting. This setting indicates the likelihood that images will randomly flip.
.png)
RandAugment
Select RandAugment to randomly apply a set of augmentations to your dataset. The possible augmentations are described in the table below.
Augmentation | Description |
---|---|
Identity | Applies no changes to the image. By adding some images to the dataset that still look like the original image, the Identity method decreases the chances of the model training only on augmented images, and prevents overfitting. |
Equalize | Equalizes the image histogram, which increases contrast. This method is also referred to as histogram equalization. |
Rotate | Rotates the image clockwise or counterclockwise by a random number of degrees. This can change the position of the image within its frame. |
Posterize | Reduces the number of bits for each color channel. This method is also referred to as posterization. |
RandomContrast | Increases or decreases the contrast of the image. |
RandomBrightness | Increases or decreases the contrast of the image. |
ShearX | Shifts the pixels along the X-axis, so that the image looks slanted and stretched horizontally. This can change the position of the image within its frame. This method is also referred to as shear mapping, shear transformation, and shearing. |
ShearY | Shifts the pixels along the Y-axis, so that the image looks slanted and stretched vertically. This can change the position of the image within its frame. This method is also referred to as shear mapping, shear transformation, and shearing. |
TranslateX | Moves the pixels horizontally by the same distance. This can change the position of the image within its frame. This method is also referred to as translation. |
TranslateY | Moves the pixels vertically by the same distance. This can change the position of the image within its frame. This method is also referred to as translation. |
RandAugment Settings
The RandAugment feature offers many settings, as described in the table below.

# | Setting | Description |
---|---|---|
1 | Number Transforms | The number of augmentations you want to be applied to images. LandingLens will randomly apply this number of augmentations to the images. |
2 | Probability | The likelihood that images will have an augmentation applied. |
3 | Magnitude | How strong the applied augmentation will be, ranked 1 to 10 with 1 being weak and 10 being strong. |
Random Rotate
Apply Random Rotate for a chance that images in your dataset will randomly rotate.
This modification offers many settings, as described in the table below.

# | Setting | Description |
---|---|---|
1 | Rotate Range | Images have a chance to rotate to any degree in the range specified here. |
2 | Probability | The likelihood that images will randomly rotate. |
3 | Interpolation | Use this setting to resize images. |
4 | Border Mode | Use this setting to create a border around the image, like a photo frame. |
5 | Rotate | Use this setting to preview how images will look in the selected value. |
6 | Set as Lower Limit | Set a value in the Rotate modification to preview how images will look at that rotate level. Then click Set as Lower Limit to choose the lowest rotation level that you want images to display in. The value in the Rotate setting (#3) will be set as the lowest value in the Rotate Range (#1). |
7 | Set as Upper Limit | Set a value in the Rotate modification to preview how images will look at that rotate level. Then click Set as Upper Limit to choose the highest rotation level that you want images to display in. The value in the Rotate setting (#3) will be set as the lowest value in the Rotate Range (#1). |
Preview Effects
After configuring an advanced setting, you can preview how the images will look. To do this, click Preview Transform Effect or Preview Augmentation Effect accordingly.

.png)
Hyperparameter
When a model trains, it goes through an extensive process in the background. If you want to have more granular control over how your model trains, you can configure the Hyperparameters. A Hyperparameter is a setting used to control the speed and quality of the learning process.
Here are some important notes to keep in mind:
- The Hyperparameter settings are intended for users who are familiar with Machine Learning. If you are unfamiliar with Machine Learning, it is recommended to use the default settings.
- Hyperparameters should only be adjusted to be data-driven.
- Repeatedly training a model with different Hyperparameters (called "Hyperparameter Sweep") is NOT recommended as this can be expensive.
Hyperparameters have a couple of settings, as described in the table below.
Hyperparameter
Setting | Description |
---|---|
Epoch | When your model trains, it works through your dataset multiple times. The completion of one cycle is called an epoch. Enter the number of cycles you want the model to perform in this field. |
Model Size | The size of the model's capacity. Model capacity refers to the Model's ability to learn complex patterns in data.
|
Custom Training FAQ
Is Advanced Training the same thing as Custom Training?
Yes.
When should I use Custom Training?
If the performance results of your model are unsatisfactory after a Fasting Train, switch to Custom Training.
I'm unfamiliar with machine learning. Can I use Custom Training?
We recommend using Fast Training so that LandingLens can fine-tune your model settings for you.
What happens if I switch from Custom Training to Fast Training?
I applied Custom Training settings and trained a model. If I want to train a second model with identical settings, can I click the "Train" (Fast Train) button?
The "Train" button uses default configurations which are different than "Train with Custom Options". However, if you click "Train with Custom Options", your previous settings will be remembered.