Update README.md

README.md

@@ -80,26 +80,6 @@ The wide training range enables FRTSearch to handle both **drift scan short-term
 
 ---
 
-## Model Performance
-
-Benchmarked on **FAST-FREX** dataset (600 FRB bursts):
-
-| Metric | Value |
-|--------|-------|
-| **Recall** | 98.0% |
-| **False Positives Per Image (FPPI)** | 4.1 |
-| **Speedup vs PRESTO** | 25.5× |
-| **Cross-facility Generalization** | 100% (19/19 ASKAP FRBs detected) |
-
-### Key Advantages
-
-- ✅ **High Sensitivity**: 98.0% recall on benchmark dataset
-- ✅ **Low False Positives**: Reduces false positives by >99.9% vs traditional methods
-- ✅ **Cross-Telescope Robustness**: Successfully generalizes to FAST, Parkes, and SKA without retraining
-- ✅ **Efficiency**: Up to 25.5× faster than traditional pipelines
-- ✅ **Unified Framework**: Eliminates redundant DM-width grid searches
-
----
 
 ## Supported Data Formats
 
@@ -112,7 +92,7 @@ Benchmarked on **FAST-FREX** dataset (600 FRB bursts):
 - 2-bit quantization
 - 4-bit quantization
 - 8-bit quantization
-
+- 32-bit quantization
 ---
 
 ## Test Samples
@@ -178,52 +158,6 @@ python FRTSearch.py data.fits config.py --slide-size 128
 - `data.fits`: Observation data file (.fits or .fil format)
 - `config.py`: Model configuration file (see `configs/` directory)
 - `--slide-size`: Number of sub-integrations per batch (default: 128)
-
-### Example Configuration
-
-```python
-# configs/detector_FAST.py
-data_cfg = dict(
-    freq_range=[1000.0, 1500.0],  # MHz
-    downsample_time=1,
-    downsample_freq=16
-)
-
-model_cfg = dict(
-    detector_cfg='path/to/detector_config.py',
-    score_thr=0.3,
-    detector_model='models/hrnet_epoch_36.pth'
-)
-
-aug_cfg = dict(
-    type='dm_filtering',
-    threshold=3.0  # Filter candidates with DM < 3.0
-)
-```
-
----
-
-## Hyperparameter Tuning
-
-### Model Input Constraint
-
-The model is trained on input dimensions of **256 (freq) × 8192 (time)**. This wide training range enables FRTSearch to handle both drift scan short-term observations and tracking long-term observation data processing.
-
-### Tuning Guidelines
-
-**Key Parameters:**
-- `freq_range`: **CRITICAL** - Must match your observation frequency range
-- `downsample_time`: Time downsampling factor
-- `downsample_freq`: Frequency downsampling factor
-- `--slide-size`: Batch size for processing long observations
-
-**Critical Rules:**
-1. Time dimension after downsampling must be ≤ 8192
-2. Frequency dimension should target ≈ 256 bins (can be higher, avoid very low values)
-3. Always update `freq_range` to match your data
-
-See [test_sample/README.md](test_sample/README.md) for telescope-specific tuning strategies.
-
 ---
 
 ## Telescope Coverage