wound_classification_jax

Wound Classification with JAX / Flax | 使用 JAX / Flax 的创伤图像分类项目

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📌 Overview | 项目简介

This project implements multiple deep-learning models for wound image classification, built using JAX and Flax. The system includes:

• Traditional CNN models
• ResNet18 / ResNet34
• Vision Mamba (a state-space model for vision tasks)
• Hybrid Mamba + CNN
• Hybrid Mamba + ResNet
• Full training and evaluation pipelines
• Dataset cleaning, augmentation, splitting, and loading utilities

本项目实现了一个 基于 JAX / Flax 的伤口图像分类任务，提供：

• 传统 CNN 模型
• ResNet18 / ResNet34
• Vision Mamba（视觉状态空间模型）
• 混合 Mamba + CNN
• 混合 Mamba + ResNet
• 训练与推理全流程脚本
• 数据清洗、增强、划分、加载工具

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📁 Project Structure | 项目结构

WOUND_CLASSIFICATION_JAX
│  requirements.txt
│  terminal_commands.txt
│
├─data
│   └─dataset                 # Cleaned dataset (after processing)
│
├─nets                        # Model architectures
│   └─ BaselineCNN.py
│      CNN.py
│      Hybrid.py
│      Mamba.py               # The implementations of Vision Mamba and the VisionMamba.py file are different.
│      ResNet.py
│      VisionMamba.py
│
├─references
│   └─ Hatamizadeh_MambaVision_CVPR2025.pdf
│
└─scripts                     # Training / Testing / Data Processing
    └─ dataset.py
       data_clean.py
       download_data.py
       test.py
       train.py

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🚀 Features | 功能特点

1. Multiple Model Architectures 多模型支持

• ✔ SimpleCNN（CNN.py）
• ✔ BaselineCNN
• ✔ ResNet18 / ResNet34
• ✔ Vision Mamba
• ✔ Hybrid Mamba + CNN
• ✔ Hybrid Mamba + ResNet

2. Data Processing Toolkit 数据处理工具

• Automatic corruption detection 自动检测损坏图片
• Dataset cleaning & renaming 数据集清洗与重新命名
• Train/Test splitting 自动划分训练/测试集
• On-the-fly augmentation 在线增强（旋转、亮度、对比度、模糊等）

3. Full Training Pipeline 完整训练流程

• Train/eval steps with BatchNorm/Dropout
  • Learning rate, dropout, optimizer configurable
• Checkpoint saving & loading
• Gradient clipping

4. Inference / Evaluation 推理与评估

• Load checkpoint and evaluate on test set
• Supports all model types
• Outputs accuracy and loss metrics

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📦 Installation | 安装

pip install -r requirements.txt

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📂 Dataset Preparation | 数据准备

Download from Kaggle

python scripts/download_data.py

Clean dataset and remove corrupted images

python scripts/data_clean.py

This creates:

data/dataset/
    000001_ClassA.jpg
    000002_ClassB.jpg

Split into train/test

python scripts/data_clean.py --build_split

This generates:

data/dataset_split/train/
data/dataset_split/test/

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🧠 Model Training | 模型训练

Example:

python scripts/train.py \
    --model mamba \
    --batch_size 16 \
    --num_epochs 50 \
    --learning_rate 5e-5 \
    --use_augmentation True

Supported models (choose with --model)

cnn
baseline_cnn
resnet18
resnet34
mamba
vision_mamba
hybrid_mamba_cnn
hybrid_mamba_resnet

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🧪 Model Testing / Evaluation | 模型测试与评估

Example:

python scripts/test.py \
    --model mamba \
    --ckpt_path ../checkpoints/mamba/best.pkl

Outputs:

• Accuracy (准确率)
• Loss (损失值)
• Batch-wise prediction statistics (批次预测统计)

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🧩 Key Files | 关键文件说明

File	Description
scripts/train.py	Full training pipeline（训练主脚本）
scripts/test.py	Inference and evaluation（推理评估脚本）
scripts/dataset.py	Dataset loader + augmentation（数据加载器 + 增强）
scripts/data_clean.py	Clean dataset and split（数据清洗与划分）
nets/	All neural network architectures（所有网络结构）

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💡 Model Highlights | 模型亮点

Vision Mamba

Implements Mamba state-space blocks for vision tasks, including:

• Patch embedding
• Conv + SSM dual-branch encoder
• SwiGLU feed-forward
• Positional embeddings
• Optional class token

Hybrid Models

Fuse Mamba features with CNN/ResNet outputs:

• Weighted sum 融合
• Gated sum 门控融合
• Concatenation + MLP 连接头

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▶ Example Code | 示例代码

Loading an image manually

from scripts.dataset import data_loader

loader = data_loader(data_path="../data/dataset", use_augmentation=True)

img, label_idx, img_idx = loader[0]

Running a forward pass

from nets.CNN import SimpleCNN
import jax
from scripts.dataset import data_loader

loader = data_loader(data_path="../data/dataset", use_augmentation=False)
model = SimpleCNN(num_classes=loader.num_classes)
params = model.init(jax.random.PRNGKey(0), jax.numpy.zeros((1, 224, 224, 3)))
logits = model.apply(params, jax.numpy..zeros((1, 224, 224, 3)))

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📌 Requirements | 依赖

See requirements.txt.

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📄 License | 许可证

See LICENSE

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