SOC Results

Author: jangsoopark

README.md

@@ -39,10 +39,9 @@ The proposed model only consists of **sparsely connected layers** without any fu
 | activation  | linear |    ReLU    |    ReLU    |    ReLU    |  ReLU  | Softmax |
 
 ## Training
-~~For training, this implementation fixes the random seed to `12321` for `reproducibility`.~~
+For training, this implementation fixes the random seed to `12321` for `reproducibility`.
 
-The experimental conditions are same as in the paper, except for `data augmentation` and `learning rate`. 
-The `learning rate` is initialized with `1e-3` and decreased by a factor of 0.1 **after 26 epochs**.
+The experimental conditions are same as in the paper, except for `data augmentation`.
 You can see the details in `src/model/_base.py` and `experiments/config/AConvNet-SOC.json`
 
 ### Data Augmentation
@@ -52,10 +51,9 @@ You can see the details in `src/model/_base.py` and `experiments/config/AConvNet
 
 - However, for SOC, this repository does not use random shifting tue to accuracy issue.
     - You can see the details in `src/data/generate_dataset.py` and `src/data/mstar.py`
-    - This implementation failed to achieve higher than 98% accuracy when using random sampling.
     - The implementation details for data augmentation is as: 
-        - Crop the center of 94 x 94 size image on 128 x 128 SAR image chip (49 patches per image chip).
-        - Extract 88 x 88 patches with stride 1 from 94 x 94 image.
+        - Crop the center of 94 x 94 size image on 100 x 100 SAR image chip (49 patches per image chip).
+        - Extract 88 x 88 patches with stride 1 from 94 x 94 image with random cropping.
 
 
 ## Experiments
@@ -148,14 +146,14 @@ $ python3 train.py --config_name=config/AConvNet-SOC.json
 ```
 
 #### Results of SOC
-- ~~Final Accuracy is **99.05%** (The official accuracy is 99.13%)~~
+- Final Accuracy is **99.13%** at epoch 26 (The official accuracy is 99.13%)
 - You can see the details in `notebook/experiments-SOC.ipynb`
 
 - Visualization of training loss and test accuracy
 
 ![soc-training-plot](./assets/figure/soc-training-plot.png)
 
-- ~~Confusion Matrix with best model at **epoch 28**~~
+- Confusion Matrix with best model at **epoch 28**
 
 ![soc-confusion-matrix](./assets/figure/soc-confusion-matrix.png)
 
@@ -165,7 +163,7 @@ $ python3 train.py --config_name=config/AConvNet-SOC.json
 
 | Noise | 1% | 5% | 10% | 15%|
 | :---: | :---: | :---: | :---: | :---: |
-| AConvNet-PyTorch | 98.56 | 94.39 | 85.03 | 73.65 |
+| AConvNet-PyTorch | 98.60 | 95.18 | 85.36 | 73.24 |
 | AConvNet-Official | 91.76 | 88.52 | 75.84 | 54.68 |
 
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assets/figure/001.png

@@ -4,10 +4,10 @@
   "num_classes": 4,
   "channels": 2,
   "batch_size": 100,
-  "epochs": 50,
+  "epochs": 100,
   "momentum": 0.9,
-  "lr": 1e-3,
-  "lr_step": [14],
+  "lr": 1e-4,
+  "lr_step": [50],
   "lr_decay": 0.1,
   "weight_decay": 4e-3,
   "dropout_rate": 0.5