Initial commit

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README.md

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+---
+license: mit
+tags:
+  - feature-extraction
+  - dinov2
+  - ai-generated-image-detection
+---
+
+# SQuaDE 特征缓存 — DINOv2 ViT-g
+
+[kelvinchua/squade-vitg](https://huggingface.co/kelvinchua/squade-vitg) 三个版本的
+训练与评测所用的**冻结骨干特征**。传这个的唯一理由是省掉重抽的 GPU 时间
+（全量 28 万行约 3.5 小时）。
+
+⚠️ **这些特征绑死在一套配置上**：`facebook/dinov2-giant` + crop 504 + bf16 +
+全层统一 `model.norm`。换骨干、换裁剪尺寸、换归一化方式，全部作废，必须从图像重抽。
+
+---
+
+## 内容
+
+| 目录 | 行数 | 层 | 体积 | 说明 |
+|---|---:|---|---:|---|
+| `mix2_vg6/` | 289,626 | 6 层 | 15.0 GB | **v3 的训练缓存**（自建 + 官方 val 70% + val_hard 70%） |
+| `mix_vg6/` | 287,876 | 6 层 | 14.9 GB | v2 的训练缓存（自建 + 官方 val 70%） |
+| `s6x_vg6/` | 280,876 | 6 层 | 14.5 GB | 纯自建，不含任何官方数据 |
+| `vg_val/` | 10,000 | **41 层** | 3.5 GB | 官方 val 全层，做层级探针用 |
+| `vg_transfer/` | 12,500 | **41 层** | 4.4 GB | 官方 val + val_hard 全层 |
+
+6 层 = `[14, 21, 26, 27, 33, 37]`，即浅组 `{14,21,27}` 与深组 `{26,33,37}` 的并集。
+
+行数上 `s6x_vg6 ⊂ mix_vg6 ⊂ mix2_vg6`，但**行序不同**，训练时的打乱依赖行序，
+要精确复现某一版就用对应那一份。
+
+## 每份的文件
+
+```
+features.npy   (N, L, 4608) fp16   每层 cls|mean(patch)|std(patch) 拼接
+prenorm.npy    (N, L, 2)    fp32   归一化**之前**的 patch token L2 范数的均值与标准差
+meta.csv                          image_id / label / split / deg_* 码字
+config.json                       backbone / layers / crop_size / 指纹
+```
+
+`prenorm` 那两个标量是有意保留的：LayerNorm 是逐 token 的，会抹掉绝对激活强度，
+而压缩与加性噪声的能量证据恰好住在那里。
+
+## 用法
+
+```python
+import numpy as np, csv, json
+f = np.load("mix2_vg6/features.npy", mmap_mode="r")   # 一定用 mmap,15 GB
+rows = list(csv.DictReader(open("mix2_vg6/meta.csv")))
+cfg = json.load(open("mix2_vg6/config.json"))
+li = cfg["layers"].index(27)                           # 取 L27
+```
+
+切成三层的子集（浅组 / 深组）用仓库里的工具，别自己写：
+
+```bash
+python tools/slice_cache.py --src mix2_vg6 --out mix2_shallow --layers 14,21,27
+python tools/slice_cache.py --src mix2_vg6 --out mix2_deep    --layers 26,33,37
+```
+
+**没有传的**：上面那些切片（32.8 GB），以及各评测集的三层切片。它们都是一条
+`slice_cache` 命令的产物，传了是浪费。
+
+## 几个使用上的坑
+
+1. **`deg_*` 码字跨源不可解释。** `mix_vg6` / `mix2_vg6` 是把自建的 6 维码本与官方的
+   19/29 维码本取并集拼起来的，缺的维补 0。同名的 `deg_jpeg` 在两半里强度含义都不同。
+   **只能用于 stage1（均匀融合，不读码字）与二元门（只看是否有任一维 > 0）**，
+   任何逐维/逐档分析都不能用。
+2. **划分已经定死在 `meta.csv` 的 `split` 列里**，按源图分组、按 `source × label` 分层。
+   别自己重划——同一张源图的干净版与退化版必须同侧，否则模型记住内容就能认出来。
+3. **官方 val / val_hard 的 70% 已用于训练 v2/v3。** 对应的固定划分随权重发布在
+   [squade-vitg](https://huggingface.co/kelvinchua/squade-vitg) 的 `v3/` 下。
+
+MIT。DINOv2 权重与 NTIRE 数据各有其许可条款。

mix2_vg6/config.json

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+  "dtype": "torch.bfloat16",
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+  "hidden_size": 1536,
+  "n_registers": 0,
+  "layers": [
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+  ],
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+  "feature_dim": 4608,
+  "n_samples": 280876,
+  "crop_size": 504,
+  "crop_mode": "center",
+  "do_resize": false,
+  "layer_norm": "model.norm applied to every extracted layer",
+  "taxonomy": "synthetic6x",
+  "deg_dims": [
+    "jpeg",
+    "blur",
+    "resize",
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+    "jitter",
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+  "derived_from": [
+    "/workspace/cache/mix_vg6",
+    "/workspace/cache/vh6"
+  ],
+  "derived_op": "concat_cache",
+  "manifest_sha1": "2e9c391f09952fbc9a60e34d6d8a8eb72e7b4dd6"
+}

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