Mobilenet图像分类器重训练：random_scale等预处理参数取值与解读

Hey there! Let's dive into those preprocessing parameters you're curious about when retraining MobileNet's classifier layer. These settings are all about data augmentation—they help your model generalize better to real-world images by exposing it to more variations during training.

1. random_brightness #

• What it does: This parameter controls random adjustments to your training images' brightness. The value typically represents the maximum percentage change allowed. For example, setting it to 30 means each image's brightness can be increased or decreased by up to 30% randomly during training.
• Setting logic: Brightness variation is super common in real-world scenarios—think photos taken in sunny outdoor light vs. dim indoor rooms. A value like 30 strikes a balance: it introduces enough variation to make the model robust, but not so much that the image becomes unrecognizable. If your dataset uses consistently lit images (like studio shots), you might lower this value or even set it to 0.

2. random_scale #

• What it does: This controls random scaling (resizing) of images before they’re fed into the model. The value is usually a percentage—setting it to 30 means the image can be scaled up by 30% or down by 30% randomly. After scaling, the image is cropped back to the input size MobileNet expects.
• Setting logic: Scaling helps the model learn to recognize objects at slightly different sizes. For example, a dog might be close to the camera (larger in the frame) or far away (smaller) in real photos. A 30% range covers most reasonable size variations without distorting the object beyond recognition. If your dataset has objects of very consistent sizes, you could reduce this value or set it to 0.

3. random_crop #

• What it does: This parameter controls random cropping of images to the required input size. A value of 0 means no random cropping—instead, the image is resized (without cutting any parts) to fit MobileNet’s input dimensions. A positive value (like 20) would mean cropping a random region that’s 20% smaller than the original, then resizing it to the input size (exact implementation can vary by framework).
• Setting logic: Why set this to 0? If your dataset’s images already have objects perfectly centered, or if you can’t risk cutting off critical details (like in medical imaging where the entire structure matters), disabling random cropping makes sense. On the flip side, enabling it helps the model focus on different parts of the object, making it more robust to off-center subjects.

Why Those Specific Values (30, 30, 0)? #

The combination of random_brightness=30, random_scale=30, random_crop=0 is a practical middle-ground for many general-purpose image classification tasks:

• The 30% values for brightness and scale introduce meaningful real-world variation without destroying the image’s core content.
• Disabling random cropping might be because the dataset’s images are already well-framed, or the user wants to keep things simple while starting out with retraining before experimenting with more aggressive augmentation.

Remember, these values aren’t fixed rules! Always tune them based on your specific dataset:

• If your images have huge lighting variations, bump up random_brightness.
• If your objects appear in wildly different sizes, increase random_scale.
• If your objects are often off-center, try enabling random_crop with a small value first (like 10 or 15) and see how your model’s performance changes.

内容的提问来源于stack exchange，提问作者Hao Xi