- Problem Statement: Deep convolutional neural networks have been shown to perform remarkably well in image classification tasks in recent years. However, these networks require extremely large training sets in order to train effectively and avoid overfitting. This problem of generalization can be critical to certain tasks, where it is either not possible or not feasible to obtain a suitably large dataset.
- Intended Approach: Image augmentation is the technique of deriving augmented images from an existing training set and creating a new training set from the original and augmented images. Using the technique of image augmentation, deep learning image classifiers can achieve superior performance with smaller datasets, thus alleviating the data collection and labeling problem. We intend to utilize a variety of image augmentation techniques learned in ECE 4580 - Digital Image Processing in Spring 2021.
- ResNet9 train, validate, & save script can be found at here.
- ResNet9 model-summary can be found at here.
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Affine:
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Frequency Filters - Gaussian, Ideal, Butterworth:
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Intensity Manipulations:
How To Test Image Augmentation Methods (Recommend Using This For Testing Augmentation Methods For Your Uses)
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Augmentation-method testing script can be found at here.
python test_image_aug.py
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Note on set up machine for augmenting and training agents reprodution:
- CUDA 10.0.
- Pytorch 1.2.0.
- GPU that I use - Cloudera:
- Augmenting dataset(s) & training models will take a very long time, even with GPU. If you are interested in pre-augmented "Imagenette-160" and pre-trained ResNet9 models, please check the section below.
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Images Augmentation Task:
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Step 1: Make sure your CPU is strong enough. I use 2 vCPU / 4 Gib Memory.
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Step 2: Set up the right image path and image-save path in process_image.py.
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Step 3: Choose the augmentation methods that you prefer in process_image.py.
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Step 4 Run and Wait 10 minutes to 1.25 hour per sections of the training datasets (depends on what augmentation method you choose).
python preprocess_image.py
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Training:
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Step 1: Make sure your CPU is strong enough. I use 2 vCPU / 4 Gib Memory with 1 GPU - CUDA v10.0.
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Step 2: Choose the augmented dataset that you want in model_train.py.
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Step 3: Choose the path that you want to save your model in model_train.py.
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Step 4 Run and Wait around 17 minutes per sections of the model training session.
python model_train.py
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Results for training and validating ResNet9 on each image augmentation datasets can be found at here including:
- "Accuracy vs Each Epoch" graphs.
- "Learning Rate vs Batch Number" graphs.
- "Loss Rate vs Each Epoch" graphs.
- 1 testing image classification in testing batch.
- Training results after 30 epochs.
- Training shuffled 64 images batch.
- Testing 64 images batch.
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Comparison Table of ResNet9's Performances on each dataset (the information is extracted from here:
| Dataset | Best Accuracy | Latest Accuracy at Epoch 30 | Dataset Augmenting Time | ResNet9 Training Time on GPU |
|---|---|---|---|---|
| Imagenette-160 Original | 72.09% | 72.09% | 0 | 17 minutes |
| Imageneete-160 Horizontal Flip | 78.53% | 62.24% | 45 minutes | 17 minutes |
| Imagenette-160 Vertical Flip | 77.25% | 73.54% | 45 minutes | 17 minutes |
| Imagenette-160 Random Rotation | 68.86% | 65.80% | 47 minutes | 17 minutes |
| Imagenette-160 Horizontal Shear | 75.26% | 72.16 | 47 minutes | 17 minutes |
| Imagenette-160 Vertical Shear | 74.41% | 73.41% | 45 minutes | 17 minutes |
| Imagenette-160 Translation | 74.31% | 63.22% | 48 minutes | 17 minutes |
| Imagenette-160 Ideal Highpass Filter | 68.69% | 66.64% | 185 minutes | 17 minutes |
| Imagenette-160 Ideal Lowpass Filter | 63.54% | 59.96% | 185 minutes | 17 minutes |
| Imagenette-160 Gaussian Highpass Filter | 70.42% | 66.88% | 185 minutes | 17 minutes |
| Imagenette-160 Gaussian Lowpass Filter | 69.15% | 63.71% | 185 minutes | 17 minutes |
| Imagenette-160 Butterworth Highpass Filter | 68.32% | 62.55% | 185 minutes | 17 minutes |
| Imagenette-160 Butterworth Lowpass Filter | 67.76% | 59.94% | 185 minute | 17 minutes |
| Imagenette-160 Adaptive Median Filter | 78.29% | 69.44% | 595 minutes | 17 minutes |
| Imagenette-160 Histogram Equalization | 69.4% | 68.30% | 46 minutes | 17 minutes |
| Imagenette-160 Invert | 69.80% | 67.62% | 43 minutes | 17 minutes |
| Imagenette-160 Canny Edge Detection | 62.63% | 61.99% | 130 minutes | 17 minutes |
- Augmented Imagenette-160 datasets can be found at here.
- Trained Pytorch models can be found at here.
- C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, 2019.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv.org, 10-Dec-2015. [Online]. Available. [Accessed: 06-May-2021].
- Fastai, “fastai/imagenette,” GitHub. [Online]. Available. [Accessed: 08-Apr-2021].
- Collections of Digital Image Processing Techniques.
- One Cycle Learning Rate.
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