docs(readme): Update README.md with detailed installation and usage guide

- Changed default prediction result file suffix from _v4_minute to _minute
- Added project overview and key features description
- Added detailed installation steps and prerequisites
- Provided dataset preparation directory structure example
- Added training and evaluation command-line parameter tables
- Improved model architecture and output structure documentation
- Enhanced SwanLab monitoring feature introduction
- Added advanced usage including checkpoint resume and component disable options

README.md

@@ -1,3 +1,166 @@
 # XNet
 
-XNet: A Staged Dual-Frequency Synergistic Framework via Wavelet-FFT for Medical Image Segmentation of Small Objects and Weak Boundaries
+XNet: A Staged Dual-Frequency Synergistic Framework via Wavelet-FFT for Medical Image Segmentation of Small Objects and Weak Boundaries
+
+## Overview
+
+XNet is a medical image segmentation framework designed for accurate segmentation of small objects and weak boundaries. It combines wavelet transform and FFT enhancement with Swin-UNETR architecture to achieve superior performance in challenging scenarios.
+
+## Key Features
+
+- **Dual-Frequency Enhancement**: Combines wavelet transform and FFT for multiscale feature extraction
+- **Swin-UNETR Backbone**: Leverages transformer-based architecture for robust segmentation
+- **Enhanced Data Augmentation**: Comprehensive augmentation pipeline for better generalization
+- **Multi-Metric Evaluation**: Dice, IoU, and Hausdorff Distance metrics
+
+## Installation
+
+### Prerequisites
+
+```bash
+pip install torch monai swanlab opencv-python numpy
+```
+
+## Quick Start
+
+### 1. Prepare Your Dataset
+
+Organize your dataset in the following structure:
+
+```
+data/
+└── Polyp-Detection-Dataset/
+    └── YourDatasetName/
+        ├── images/
+            ├─────1.png
+            ├─────2.png
+            ├─────...
+        └── masks/
+            ├─────1.png
+            ├─────2.png
+            ├─────...
+        └── train.txt
+        └── val.txt
+```
+
+### 2. Training
+
+Train the model on your dataset:
+
+```bash
+python train.py \
+    --dataset_name YourDatasetName \
+    --data_root ./data/Polyp-Detection-Dataset \
+    --batch_size 4 \
+    --max_epochs 1000 \
+    --learning_rate 1e-4 \
+    --device cuda
+```
+
+#### Key Training Parameters
+
+| Parameter                   | Default    | Description                |
+|-----------------------------|------------|----------------------------|
+| `--dataset_name`            | Required   | Name of your dataset       |
+| `--batch_size`              | 4          | Batch size for training    |
+| `--max_epochs`              | 1000       | Maximum training epochs    |
+| `--learning_rate`           | 1e-4       | Initial learning rate      |
+| `--feature_size`            | 48         | Network feature dimension  |
+| `--target_spatial_size`     | (512, 512) | Input image size           |
+| `--early_stopping_patience` | 100        | Early stopping patience    |
+| `--use_wavelet`             | True       | Enable wavelet enhancement |
+| `--use_fft`                 | True       | Enable FFT enhancement     |
+
+### 3. Evaluation
+
+Evaluate trained models:
+
+```bash
+python eval.py \
+    --dataset_name YourDatasetName \
+    --data_root ./data/Polyp-Detection-Dataset \
+    --outputs_dir ./outputs \
+    --device cuda
+```
+
+#### Key Evaluation Parameters
+
+| Parameter              | Default          | Description                                 |
+|------------------------|------------------|---------------------------------------------|
+| `--dataset_name`       | Required         | Name of your dataset                        |
+| `--outputs_dir`        | ./outputs_minute | Directory containing trained models         |
+| `--batch_size`         | 1                | Batch size for evaluation                   |
+| `--save_visualization` | True             | Save visualization results                  |
+| `--vis_num_samples`    | 1000             | Number of samples to visualize              |
+| `--best_metric`        | False            | Use best overall model (default: best Dice) |
+
+## Model Architecture
+
+XNet integrates three key components:
+
+1. **Wavelet Enhancement Module**: Captures multi-scale frequency features
+2. **FFT Enhancement Module**: Enhances global frequency domain information
+3. **Swin-UNETR v2**: Transformer-based backbone for robust feature extraction
+
+## Output Structure
+
+After training, outputs are organized as:
+
+```
+outputs_minute/
+├── best_dice_model_YourDatasetName.pt
+├── best_iou_model_YourDatasetName.pt
+├── best_metric_model_YourDatasetName.pt
+└── checkpoints_YourDatasetName/
+    └── checkpoint_epoch=X.pt
+```
+
+## Monitoring with SwanLab
+
+Training progress is automatically logged to SwanLab:
+
+
+Metrics tracked:
+- Training/validation loss
+- Dice coefficient
+- IoU (Intersection over Union)
+- Hausdorff Distance
+- Learning rate schedule
+
+## Advanced Usage
+
+### Resume Training
+
+Training automatically resumes from the latest checkpoint:
+
+```bash
+python train.py --dataset_name YourDatasetName
+```
+
+### Disable Components (Ablation Study)
+
+```bash
+# Disable wavelet enhancement
+python train.py --dataset_name YourDatasetName --no_wavelet
+
+# Disable FFT enhancement
+python train.py --dataset_name YourDatasetName --no_fft
+```
+
+### Custom Loss Weights
+
+```bash
+python train.py \
+    --dataset_name YourDatasetName \
+    --dice_weight 1.0 \
+    --ce_weight 1.0 \
+    --iou_weight 1.0
+```
+
+## Citation
+
+If you find this work useful, please cite our paper.
+
+## License
+
+This project is licensed under the Apache License.

eval.py

@@ -139,7 +139,7 @@ def parse_args():
     parser.add_argument(
         "--dir_flag",
         type=str,
-        default="_v4_minute",
+        default="_minute",
         help="Prediction result save file suffix"
     )
     parser.add_argument(