diff --git a/README.md b/README.md
index e4a4f0c..a7766fb 100644
--- a/README.md
+++ b/README.md
@@ -29,22 +29,45 @@ GINKA Model 内部集成了 Minamo Model 用做判别器，与 Ginka Model 对
         "yellowGem": {
             "30": 1
         },
-        "item": [47, 49, 50, 51, 52, 53],
+        "item": {
+            "47": 1,
+            "49": 1,
+            "50": 0,
+            "51": 1,
+            "52": 1,
+            "53": 2
+        },
         "potion": {
             "31": 100,
             "32": 200,
             "33": 400,
             "34": 800
         },
-        "key": [21, 22, 23],
-        "door": [81, 82, 83, 85],
-        "wall": [1, 17]
+        "key": {
+            "21": 0,
+            "22": 1,
+            "23": 2,
+            "24": 2,
+            "25": 2
+        },
+        "door": {
+            "81": 0,
+            "82": 1,
+            "83": 2,
+            "84": 2,
+            "85": 3,
+            "86": 2
+        },
+        "wall": [1, 17],
+        "decoration": [],
+        "floor": [87, 88],
+        "arrow": [91, 92, 93, 94]
     },
     "data": {}
 }
 ```
 
-其中，`clip` 属性表示你的每张地图的那一部分会被当成数据集，例如填写 `[0, 0, 13, 13]` 就会让坐标为 `(0, 0)`，长宽为 `(13, 13)` 的矩形内容作为数据集。`special` 不用管。
+其中，`clip` 属性表示你的每张地图的那一部分会被当成数据集，例如填写 `[0, 0, 13, 13]` 就会让坐标为 `(0, 0)`，长宽为 `(13, 13)` 的矩形内容作为数据集。`special` 不用管。注意装饰所使用的贴图是白墙，如果白墙是墙壁的话，需要将白墙设置为墙壁。注意不要忘记保存
 
 2. 使用 [在线工具](https://unanmed.github.io/ginka-process) 处理数据，需要给每个地图添加标签，为每个图块分配种类，有一些图块包含多种等级，需要填写正确。
 3. 将 `project` 文件夹打包发给我
diff --git a/data/src/floor.ts b/data/src/floor.ts
index e308bfa..572bc97 100644
--- a/data/src/floor.ts
+++ b/data/src/floor.ts
@@ -28,16 +28,16 @@ function convert(
     const clipped: number[][] = [];
 
     // 1. 裁剪
-    for (let nx = x; nx < x + w; nx++) {
+    for (let ny = y; ny < y + w; ny++) {
         const row: number[] = [];
-        for (let ny = y; ny < y + h; ny++) {
+        for (let nx = y; nx < x + h; nx++) {
             row.push(map[ny][nx]);
         }
         clipped.push(row);
     }
 
     const res: number[][] = Array.from({ length: clipped.length }, () =>
-        Array.from({ length: clipped[0].length })
+        Array.from({ length: clipped[0].length }, () => 0)
     );
 
     // 2. 初步映射
@@ -278,12 +278,18 @@ function range(from: number, to: number) {
     return Array.from({ length }, (_, i) => i + from);
 }
 
-export function getGinkaRatio(map: number[][]) {
-    return [
-        getRatio(map, [1, ...range(3, 32)]),
-        getRatio(map, [26, 27, 28]),
-        getRatio(map, range(7, 26)),
-        getRatio(map, [3, 4, 5, 6]),
-        getCount(map, [29, 30])
-    ];
+export function getGinkaRatio(map: number[][]): number[] {
+    const arr: number[] = Array(16).fill(0);
+    arr[0] = getRatio(map, [1, ...range(3, 32)]);
+    arr[1] = getRatio(map, [1]);
+    arr[2] = getRatio(map, [2]);
+    arr[3] = getRatio(map, [3, 4, 5, 6]);
+    arr[4] = getRatio(map, [26, 27, 28]);
+    arr[5] = getRatio(map, range(7, 26));
+    arr[6] = getRatio(map, range(10, 19));
+    arr[7] = getRatio(map, range(19, 23));
+    arr[8] = getRatio(map, [7, 8, 9]);
+    arr[9] = getCount(map, [23, 24, 25]);
+    arr[10] = getCount(map, [29, 30]);
+    return arr;
 }
diff --git a/ginka/critic/vision.py b/ginka/critic/vision.py
index 465760c..de317b3 100644
--- a/ginka/critic/vision.py
+++ b/ginka/critic/vision.py
@@ -13,7 +13,10 @@ class MinamoVisionModel(nn.Module):
             spectral_norm(nn.Conv2d(in_ch*2, in_ch*4, 3)), #9*9
             nn.LeakyReLU(0.2),
             
-            spectral_norm(nn.Conv2d(in_ch*4, out_ch, 3)), # 7*7
+            spectral_norm(nn.Conv2d(in_ch*4, in_ch*8, 3)), # 7*7
+            nn.LeakyReLU(0.2),
+            
+            spectral_norm(nn.Conv2d(in_ch*8, out_ch, 3)), # 5*5
             nn.LeakyReLU(0.2),
         )
         
diff --git a/ginka/dataset.py b/ginka/dataset.py
index cbbe4c2..c701c19 100644
--- a/ginka/dataset.py
+++ b/ginka/dataset.py
@@ -6,13 +6,12 @@ from torch.utils.data import Dataset
 import torch
 import torch.nn.functional as F
 from typing import List
-from shared.utils import random_smooth_onehot
 
-STAGE1_MASK = [0, 1, 10, 11]
-STAGE1_REMOVE = [2, 3, 4, 5, 6, 7, 8, 9, 12, 13]
-STAGE2_MASK = [6, 7, 8, 9]
-STAGE2_REMOVE = [2, 3, 4, 5, 12, 13]
-STAGE3_MASK = [2, 3, 4, 5, 12, 13]
+STAGE1_MASK = [0, 1, 2, 29, 30]
+STAGE1_REMOVE = list(range(3, 29))
+STAGE2_MASK = [3, 4, 5, 6, 26, 27, 28]
+STAGE2_REMOVE = list(range(7, 26))
+STAGE3_MASK = list(range(7, 26))
 STAGE3_REMOVE = []
 
 def load_data(path: str):
diff --git a/ginka/generator/loss.py b/ginka/generator/loss.py
index 11bd813..211bbf8 100644
--- a/ginka/generator/loss.py
+++ b/ginka/generator/loss.py
@@ -9,13 +9,13 @@ from shared.constant import VISION_WEIGHT, TOPO_WEIGHT
 from ..critic.model import MinamoModel
 
 CLASS_NUM = 32
-ILLEGAL_MAX_NUM = 13
+ILLEGAL_MAX_NUM = 30
 
 STAGE_ALLOWED = [
     [],
-    [0, 1, 10, 11],
-    [6, 7, 8, 9],
-    [2, 3, 4, 5, 12, 13]
+    [0, 1, 2, 29, 30],
+    [3, 4, 5, 6, 26, 27, 28],
+    list(range(7, 26))
 ]
 
 def get_not_allowed(classes: list[int], include_illegal=False):
@@ -30,14 +30,13 @@ def get_not_allowed(classes: list[int], include_illegal=False):
             
     return res
 
-def outer_border_constraint_loss(pred: torch.Tensor, allowed_classes=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]):
+def outer_border_constraint_loss(pred: torch.Tensor, allowed_classes=[*list(range(0, 29)), 30]):
     """
     强制地图最外圈像素必须为指定类别（墙或箭头）
     
     参数:
         pred: 模型输出的概率分布，形状 [B, C, H, W]
-        allowed_classes: 允许出现在外圈的类别列表（默认[1,11]）
-        penalty_scale: 惩罚强度系数
+        allowed_classes: 允许出现在外圈的类别列表
     
     返回:
         loss: 标量损失值
@@ -62,12 +61,12 @@ def outer_border_constraint_loss(pred: torch.Tensor, allowed_classes=[0, 1, 2, 3
     
     return loss_unallowed
 
-def inner_constraint_loss(pred: torch.Tensor, allowed=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13]):
+def inner_constraint_loss(pred: torch.Tensor, allowed=list(range(0, 30))):
     """限定内部允许出现的图块种类
 
     Args:
         pred (torch.Tensor): 模型输出的概率分布 [B, C, H, W]
-        unallowed (list, optional): 在地图中部（处最外圈）允许出现的图块种类. Defaults to [11].
+        allowed (list, optional): 在地图中部（除最外圈）允许出现的图块种类
     """
     B, C, H, W = pred.shape
     
@@ -100,7 +99,7 @@ def _create_distance_kernel(size):
 
 def entrance_constraint_loss(
     pred: torch.Tensor,
-    entrance_classes=[10, 11],  # 假设10是楼梯，11是箭头
+    entrance_classes=[29, 30],
     min_distance=9,
     presence_threshold=0.8,
     lambda_presence=1.0,
diff --git a/ginka/train_wgan.py b/ginka/train_wgan.py
index 02ce124..1f7ecc7 100644
--- a/ginka/train_wgan.py
+++ b/ginka/train_wgan.py
@@ -17,14 +17,22 @@ from shared.image import matrix_to_image_cv
 
 # 标签定义：
 # 0. 蓝海, 1. 红海, 2: 室内, 3. 野外, 4. 左右对称, 5. 上下对称, 6. 伪对称, 7. 咸鱼层,
-# 8. 剧情层, 9. 水层, 10. 爽塔, 11. Boss层, 12. 纯Boss层, 13. 多房间, 14. 多走廊, 15. 道具塔
+# 8. 剧情层, 9. 水层, 10. 爽塔, 11. Boss层, 12. 纯Boss层, 13. 多房间, 14. 多走廊, 15. 道具风
+# 16. 区域入口, 17. 区域连接, 18. 有机关门, 19. 道具层, 20. 斜向对称, 21. 左右通道, 22. 上下通道, 23. 多机关门
+# 24. 中心对称, 25. 部分对称, 26. 鱼骨
 
 # 标量值定义：
 # 0. 整体密度，非空白图块/地图面积，空白图块还包括装饰图块
-# 1. 怪物密度，怪物数量/地图面积
-# 2. 资源密度，资源数量/地图面积
+# 1. 墙体密度，墙壁/地图面积
+# 2. 装饰密度，装饰数量/地图面积
 # 3. 门密度，门数量/地图面积
-# 4. 入口数量
+# 4. 怪物密度，怪物数量/地图面积
+# 5. 资源密度，资源数量/地图面积
+# 6. 宝石密度，宝石数量/地图面积
+# 7. 血瓶密度，血瓶数量/地图面积
+# 8. 钥匙密度，钥匙数量/地图面积
+# 9. 道具数量
+# 10. 入口数量
 
 # 图块定义：
 # 0. 空地, 1. 墙壁, 2. 装饰（用于野外装饰，视为空地）, 
diff --git a/ginka/validate.py b/ginka/validate.py
index c82e6d0..2e61bd6 100644
--- a/ginka/validate.py
+++ b/ginka/validate.py
@@ -6,10 +6,10 @@ import torch
 import torch.nn.functional as F
 from torch_geometric.loader import DataLoader
 from tqdm import tqdm
-from minamo.model.model import MinamoModel
+from .critic.model import MinamoModel
 from .dataset import GinkaDataset
-from .model.loss import GinkaLoss
-from .model.model import GinkaModel
+from .generator.loss import GinkaLoss
+from .generator.model import GinkaModel
 from shared.image import matrix_to_image_cv
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/shared/image.py b/shared/image.py
index 364e6ae..039d1d0 100644
--- a/shared/image.py
+++ b/shared/image.py
@@ -26,15 +26,15 @@ def matrix_to_image_cv(map_matrix, tile_set, tile_size=32):
             if '0' in tile_set:
                 canvas[y:y+tile_size, x:x+tile_size] = tile_set['0'][:, :, :3]  # 仅填充 RGB
 
-            if tile_index == '11':
+            if tile_index == '30':
                 if row == 0:
-                    tile_index = '11_1'
+                    tile_index = '30_1'
                 elif row == W - 1:
-                    tile_index = '11_3'
+                    tile_index = '30_3'
                 elif col == 0:
-                    tile_index = '11_2'
+                    tile_index = '30_2'
                 elif col == H - 1:
-                    tile_index = '11_4'
+                    tile_index = '30_4'
 
             # 叠加其他透明图块
             if tile_index in tile_set and tile_index != 0:
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