image-classfication-segmentation's Introduction

Image Classfication & Segmentation

This repository implements image classification and segmentation. The image classification model is based on ConvNeXt and achieves accuracy 0.9052 on a dataset with 50 classes. The backbone of the segmentation model is VGG16-FCN32, with pretrained weights available on Pytorch. After finetuning, the model attains mIOU score 0.7425 on satelitte images. For more details, please refer to the slides or the report.

Download Checkpoint

bash hw1_download.sh

Image Classification

Training

python classify.py \
    --train \
    --model-name convnext \
    --train-dir /path/to/train/data \
    --val-dir /path/to/val/data \
    --pretrained \
    --opt sgd \
    --lr 0.01 \
    --scheduler cosine \
    --weight-decay 2e-4 \
    --label-smoothing 0.1 \
    --num-epochs 20 \
    --batch-size 32 \
    --accum-step 4 \
    --augment auto

Model checkpoints and logs would be saved in ckpt/p1.

Testing

bash hw1_1.sh /path/to/test/data /path/to/output.csv

Feature Visualization

The figures illustrate the extracted feature before the linear projection head. we can observe that PCA might not be a proper choice for feature visualization in this case. On the contrary, t-SNE shows that the model is actually learning how to differentiate the images.

Image Segmentation

Training

python segment.py \
    --train \
    --train-dir /path/to/train/data \
    --val-dir /path/to/val/data \
    --model-name fcn-resnet101 \
    --loss focal-dice \
    --lr 0.005 \
    --scheduler linear \
    --weight-decay 2e-4 \
    --num-epochs 500 \
    --batch-size 4 \
    --accum-step 16 \
    --validate-step 1000 \
    --crop-resize

Model checkpoints and logs would be saved in ckpt/p2.

Testing

bash hw1_2.sh /path/to/test/data /path/to/output/dir

Examples

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