diff --git a/README.md b/README.md
index 9165fcf..504b428 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,7 @@ This project builds a deep learning pipeline for **semantic segmentation** of dr
 - **SegFormer Backbone:** Transformer-based segmentation with a practical training stack
 - **Config-Driven Workflow:** Shared and task-specific settings under `config/`
 - **Robust Evaluation:** Quantitative metrics plus qualitative visualization outputs
+- **Large-Raster Inference:** Patch-wise TIFF inference with vectorized shapefile export
 - **Reliable Checkpointing:** Resume-safe transitions with model head compatibility handling
 
 ## 🗂️ Project Structure
@@ -23,6 +24,7 @@ This project builds a deep learning pipeline for **semantic segmentation** of dr
 ├── pretrain.py                    # Pretrain entrypoint
 ├── train.py                       # Train entrypoint
 ├── evaluate.py                    # Evaluation + visualization entrypoint
+├── main.py                        # Large-image TIFF inference + vector export entrypoint
 ├── model.py                       # SegFormer model definition
 ├── losses.py                      # Losses and segmentation metrics
 ├── utils.py                       # Device/logging/checkpoint helper utilities
@@ -40,7 +42,7 @@ This project builds a deep learning pipeline for **semantic segmentation** of dr
 ├── training/
 │   ├── train.py                   # Core training loop
 │   ├── pretrain.py                # Pretrain-related training helpers
-│   ├── phase_io.py                # Checkpoint/data IO helpers
+│   ├── io.py                      # Checkpoint/data IO helpers
 │   └── primitives.py              # Shared training primitives
 └── data/                          # Local dataset roots and demos
 ```
@@ -63,11 +65,14 @@ Runtime behavior is controlled through files in `config/` (not CLI flags).
 - `LEARNING_RATE = 6e-5`
 - `WEIGHT_DECAY = 0.01`
 - `WARMUP_EPOCHS = 5`
-- `BATCH_SIZE = 8`
-- `NUM_WORKERS = 2`
+- `BATCH_SIZE = 1`
+- `VAL_BATCH_SIZE = 1`
+- `NUM_WORKERS = 1`
+- `PREFETCH_FACTOR = 1`
 - `VAL_INTERVAL = 1`
-- `GRAD_ACCUM_STEPS = 1`
+- `GRAD_ACCUM_STEPS = 8`
 - `USE_GRADIENT_CHECKPOINTING = False`
+- `USE_TORCH_COMPILE = True`
 - `MODEL_PATH = "model.pt"`
 
 **Pretrain defaults** (`config/pretrain.py`):
@@ -75,31 +80,53 @@ Runtime behavior is controlled through files in `config/` (not CLI flags).
 - `NUM_CLASSES_PRETRAIN = 8`
 - `NUM_EPOCHS_PRETRAIN = 20`
 - `NUM_VAL_SAMPLES_PRETRAIN = 150`
-- `PRETRAIN_DATA_ROOT = "./data/phase-2"`
+- `PRETRAIN_DATA_ROOT = "./data/pretrain"`
+- `PRETRAIN_SCENES = ["rural", "urban"]`
 
 **Train defaults** (`config/train.py`):
 
 - `NUM_CLASSES_TRAIN = 4`
 - `NUM_EPOCHS_TRAIN = NUM_EPOCHS_PRETRAIN + 50`
 - `NUM_VAL_SAMPLES_TRAIN = 280`
-- `TRAIN_IMG_DIR = "data/phase-3/TrainningDataset/processed_datasets"`
-- `TRAIN_MASK_DIR = "data/phase-3/TrainningDataset/processed_masks"`
-- `VAL_IMG_DIR = "data/phase-3/ValidationDataset/processed_datasets"`
-- `VAL_MASK_DIR = "data/phase-3/ValidationDataset/processed_masks"`
+- `TRAIN_IMG_DIR = "data/train/training_dataset/processed_datasets"`
+- `TRAIN_MASK_DIR = "data/train/training_dataset/processed_masks"`
+- `VAL_IMG_DIR = "data/train/validation_dataset/processed_datasets"`
+- `VAL_MASK_DIR = "data/train/validation_dataset/processed_masks"`
+
+**Evaluation defaults** (`config/eval.py`):
+
+- `NUM_CLASSES_EVAL = 4`
+- `NUM_BATCHES_EVAL = 8`
+- `MAX_EXAMPLES_EVAL = 5`
+- `IGNORE_LABEL = 255`
+- `INPUT_DIR = "data/train/testing_dataset/processed_datasets"`
+- `MASK_DIR = "data/train/testing_dataset/processed_masks"`
+
+**Inference defaults** (`config/inference.py`):
+
+- `PATCH_SIZE = 1024`
+- `STRIDE = PATCH_SIZE`
+- `NUM_CLASSES_INFERENCE = 4`
+- `TEMP_DATASET_DIR = "data/input_demo"`
+- `TEMP_MASK_DIR = "data/output_demo"`
+- `CLEANUP_TEMP_DIRS = True`
 
 ## 💾 Data Setup
 
 **Pretrain data (LoveDA)**
 
-- Expected root: `data/phase-2/`
+- Expected root: `data/pretrain/`
+- Default layout (already present in this repo): `Train/Rural`, `Train/Urban`, `Val/Rural`, `Val/Urban`
 - Scenes configured by default: `rural`, `urban`
 
 **Train data (target geospatial dataset)**
 
-- Train images: `data/phase-3/TrainningDataset/processed_datasets`
-- Train masks: `data/phase-3/TrainningDataset/processed_masks`
-- Val images: `data/phase-3/ValidationDataset/processed_datasets`
-- Val masks: `data/phase-3/ValidationDataset/processed_masks`
+- Train images: `data/train/training_dataset/processed_datasets`
+- Train masks: `data/train/training_dataset/processed_masks`
+- Val images: `data/train/validation_dataset/processed_datasets`
+- Val masks: `data/train/validation_dataset/processed_masks`
+- Test images (evaluation): `data/train/testing_dataset/processed_datasets`
+- Test masks (evaluation): `data/train/testing_dataset/processed_masks`
 
 **Mask convention**
 
@@ -118,6 +145,9 @@ uv run train.py
 
 # 3) Evaluate
 uv run evaluate.py
+
+# 4) Large-image inference (interactive .tiff path prompt)
+uv run main.py
 ```
 
 Equivalent VS Code tasks are available: `Pretrain`, `Train`, and `Evaluate`.
@@ -128,6 +158,7 @@ Equivalent VS Code tasks are available: `Pretrain`, `Train`, and `Evaluate`.
 - **Primary metrics:** mIoU, per-class IoU, pixel accuracy
 - **Evaluation script outputs:** Mean Pixel Accuracy, Mean IoU, and processed-mask variants
 - **Checkpoints:** Pretrain/Train flows read and write `model.pt`
+- **Inference outputs:** Class-wise shapefiles (`Road.shp`, `BuildUpArea.shp`, `WaterBodies.shp`)
 - **Resume behavior:** On class-count mismatch, incompatible segmentation head state is dropped to allow clean continuation
 
 ## 📊 Results & Visualizations