diff --git a/README.md b/README.md
index 8860f10..051c77c 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,8 @@
 
 本地 CPU 部署 [PaddlePaddle/PaddleOCR-VL-1.6](https://github.com/PaddlePaddle/PaddleOCR) 的 OCR 识别项目，包含完整的性能 Benchmark 和多级优化方案。
 
+> 在线 Demo: [HuggingFace Space](https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.6_Online_Demo) · 模型权重: [HuggingFace](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6)
+
 ## 项目结构
 
 ```
@@ -16,13 +18,13 @@ ocr-VL1.6/
 
 ## 技术栈
 
-| 组件 | 版本 | 说明 |
-|------|------|------|
-| Python | 3.13 | |
-| PaddlePaddle | 3.2.1 | CPU 版（无 CUDA），已编译 oneDNN/MKL-DNN |
-| PaddleOCR | 3.7.0 | 带 `doc-parser` extra |
-| PaddleOCR-VL-1.6 | 0.9B | 主 OCR 视觉语言模型（~1.8GB） |
-| PP-DocLayoutV3 | - | 版面检测模型（~126MB） |
+| 组件             | 版本  | 说明                                     |
+| ---------------- | ----- | ---------------------------------------- |
+| Python           | 3.13  |                                          |
+| PaddlePaddle     | 3.2.1 | CPU 版（无 CUDA），已编译 oneDNN/MKL-DNN |
+| PaddleOCR        | 3.7.0 | 带 `doc-parser` extra                    |
+| PaddleOCR-VL-1.6 | 0.9B  | 主 OCR 视觉语言模型（~1.8GB）            |
+| PP-DocLayoutV3   | -     | 版面检测模型（~126MB）                   |
 
 模型缓存目录：`~/.paddlex/official_models/`
 
@@ -45,8 +47,8 @@ uv sync
 # 单张图片 OCR（自动使用全部 CPU 核心）
 uv run python main.py
 
-# 批量 OCR（多进程并行）
-uv run python batch_ocr.py images/ --workers 4
+# 批量 OCR（多进程并行，安全默认值）
+uv run python batch_ocr.py images/
 ```
 
 首次运行会自动从 ModelScope 下载模型文件（约 2GB），后续使用缓存。
@@ -65,12 +67,12 @@ uv run python batch_ocr.py images/ --workers 4
 
 ### 输出结构
 
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `layout_det_res.boxes` | list[dict] | 版面文本区域（cls_id, label, score, coordinate, polygon_points） |
-| `parsing_res_list` | list[PaddleOCRVLBlock] | 识别文本块（$.label, $.bbox, $.content, $.polygon_points） |
-| `model_settings` | dict | 推理配置开关（版面检测/图表/印章等） |
-| `width` / `height` | int | 图片尺寸 |
+| 字段                   | 类型                   | 说明                                                         |
+| ---------------------- | ---------------------- | ------------------------------------------------------------ |
+| `layout_det_res.boxes` | list[dict]             | 版面文本区域（cls_id, label, score, coordinate, polygon_points） |
+| `parsing_res_list`     | list[PaddleOCRVLBlock] | 识别文本块（$.label, $.bbox, $.content, $.polygon_points）   |
+| `model_settings`       | dict                   | 推理配置开关（版面检测/图表/印章等）                         |
+| `width` / `height`     | int                    | 图片尺寸                                                     |
 
 ## 性能优化迭代
 
@@ -80,40 +82,40 @@ uv run python batch_ocr.py images/ --workers 4
 
 直接调用 `pipeline.predict()`，未设置任何线程参数。
 
-| 阶段 | 耗时 |
-|------|------|
-| 模型初始化（加载权重） | ~60s |
-| 首次推理（含 JIT 编译） | ~285s |
-| 后续推理 | ~238s（~4 min） |
+| 阶段                    | 耗时            |
+| ----------------------- | --------------- |
+| 模型初始化（加载权重）  | ~60s            |
+| 首次推理（含 JIT 编译） | ~285s           |
+| 后续推理                | ~238s（~4 min） |
 
 ### 迭代 1：算子级并行 — `core.set_num_threads()`
 
 **探索过程：**
 
-| 尝试 | 方法 | 结果 |
-|------|------|------|
-| ❌ | `paddle.set_num_threads()` | Paddle 3.x 已移除该 API |
-| ❌ | 环境变量 `OMP_NUM_THREADS` / `MKL_NUM_THREADS` | Paddle 3.x 内部使用 oneDNN，不读取这些变量 |
-| ✅ | `from paddle import core; core.set_num_threads(N)` | **有效！** oneDNN 底层算子（matmul 等）受该 API 控制 |
+| 尝试 | 方法                                               | 结果                                                 |
+| ---- | -------------------------------------------------- | ---------------------------------------------------- |
+| ❌    | `paddle.set_num_threads()`                         | Paddle 3.x 已移除该 API                              |
+| ❌    | 环境变量 `OMP_NUM_THREADS` / `MKL_NUM_THREADS`     | Paddle 3.x 内部使用 oneDNN，不读取这些变量           |
+| ✅    | `from paddle import core; core.set_num_threads(N)` | **有效！** oneDNN 底层算子（matmul 等）受该 API 控制 |
 
 **矩阵乘法微基准测试（4000×4000）：**
 
-| 线程数 | 耗时 (matmul) | 加速比 |
-|--------|--------------|--------|
-| 1 | 0.952s | 1.0x |
-| 4 | 0.419s | 2.3x |
-| 8 | 0.323s | 2.9x |
-| 16 | 0.240s | 4.0x |
-| **20** | **0.223s** | **4.3x** |
+| 线程数 | 耗时 (matmul) | 加速比   |
+| ------ | ------------- | -------- |
+| 1      | 0.952s        | 1.0x     |
+| 4      | 0.419s        | 2.3x     |
+| 8      | 0.323s        | 2.9x     |
+| 16     | 0.240s        | 4.0x     |
+| **20** | **0.223s**    | **4.3x** |
 
 **应用到 OCR 后的实际效果：**
 
 设置 `core.set_num_threads(20)` 后重新评测：
 
-| 阶段 | 优化前 | 优化后 | 提速 |
-|------|--------|--------|------|
-| 模型初始化 | ~60s | ~40s | 1.5x |
-| 推理 | ~238s | **~162s（~2.7 min）** | **1.5x** |
+| 阶段       | 优化前 | 优化后                | 提速     |
+| ---------- | ------ | --------------------- | -------- |
+| 模型初始化 | ~60s   | ~40s                  | 1.5x     |
+| 推理       | ~238s  | **~162s（~2.7 min）** | **1.5x** |
 
 **为什么不是 4.3x？** 矩阵乘法只是 OCR pipeline 的一部分。自回归解码（逐 token 生成）天然串行、I/O 等待、版面检测中的非矩阵运算等不受线程数影响。
 
@@ -123,21 +125,35 @@ uv run python batch_ocr.py images/ --workers 4
 
 **思路：** 多张图片时，用 `multiprocessing.Pool` 启动多个独立进程，每个进程加载一份 pipeline 实例，同时处理不同图片。
 
+**遇到的问题 & 修复（迭代 2.1）：**
+
+| 问题 | 原因 | 修复 |
+|------|------|------|
+| 系统卡顿/黑屏/无响应 | `Pool.starmap` 同时启动 N 个进程，同步加载 N×2GB 模型，CPU + 内存瞬间打满 | ① 进程错峰启动（随机延迟 0~15s）② `psutil` 降低进程优先级 ③ 预留 1 核给 OS ④ `imap_unordered` 替代 `starmap` |
+
 **策略：**
-- 每个子进程独立调用 `core.set_num_threads(总核心 / 进程数)`，避免线程争抢
-- 例如 4 进程 × 5 线程 = 20 核心全部利用
+- 每个子进程独立调用 `core.set_num_threads((总核心-1) / 进程数)`，预留核心给 OS
+- 例如 2 进程 × 9 线程 = 18 核，留 2 核给系统
+- `--stagger` 控制错峰窗口，默认 15s
 
 ```bash
-# 4 进程并行
+# 2 进程并行（安全默认值）
+uv run python batch_ocr.py images/
+
+# 4 进程并行（需 32GB+ RAM）
 uv run python batch_ocr.py images/ --workers 4
+
+# 自定义错峰窗口（值越大内存峰值越低，但总耗时增加）
+uv run python batch_ocr.py images/ --workers 4 --stagger 30
 ```
 
-| 配置 | 适用场景 | 理论加速比 | 内存开销 | 实际限制 |
-|------|---------|-----------|---------|---------|
-| `set_num_threads(N)` | 单张图片 | ~1.5x | 无额外开销 | 自回归解码瓶颈 |
-| `batch_ocr.py` | 批量多图 | ~Nx（N=进程数） | N × 2GB | 内存/内存带宽 |
+| 配置                 | 适用场景 | 理论加速比      | 内存开销   | 实际限制       |
+| -------------------- | -------- | --------------- | ---------- | -------------- |
+| `set_num_threads(N)` | 单张图片 | ~1.5x           | 无额外开销 | 自回归解码瓶颈 |
+| `batch_ocr.py` | 批量多图 | ~Nx（N=进程数） | N × 2GB | 内存/内存带宽，需错峰避免打满系统 |
 
 > ⚠️ 每个进程独立加载模型（~2GB），32GB RAM 建议 `--workers ≤ 4`。
+> 默认 `--workers 2` 为安全值，不会导致系统卡顿。
 
 ---
 
@@ -155,10 +171,10 @@ uv run python batch_ocr.py images/ --workers 4
 
 ## 已知局限
 
-| 问题 | 影响 | 说明 |
-|------|------|------|
-| CPU 推理极慢 | 单图 ~2.7 min（优化后） | 0.9B VL 模型不适合 CPU 实时场景 |
-| 自回归解码串行 | 无法更细粒度并行 | 生成阶段逐 token 依赖，多线程收益有限 |
-| 内存占用大 | 每进程需 ~2GB | 限制了 `batch_ocr.py` 并行度 |
-| Windows 控制台乱码 | 中文输出显示为乱码 | GBK 编码问题，文件写入/pipe 正常 |
-| ccache 警告 | 无实际影响 | 仅影响首次编译加速，可忽略 |
+| 问题               | 影响                    | 说明                                  |
+| ------------------ | ----------------------- | ------------------------------------- |
+| CPU 推理极慢       | 单图 ~2.7 min（优化后） | 0.9B VL 模型不适合 CPU 实时场景       |
+| 自回归解码串行     | 无法更细粒度并行        | 生成阶段逐 token 依赖，多线程收益有限 |
+| 内存占用大         | 每进程需 ~2GB           | 限制了 `batch_ocr.py` 并行度          |
+| Windows 控制台乱码 | 中文输出显示为乱码      | GBK 编码问题，文件写入/pipe 正常      |
+| ccache 警告        | 无实际影响              | 仅影响首次编译加速，可忽略            |
diff --git a/batch_ocr.py b/batch_ocr.py
index 1a75d98..85f03d4 100644
--- a/batch_ocr.py
+++ b/batch_ocr.py
@@ -1,37 +1,78 @@
 """
-批量 OCR 识别 — 多进程并行加速
+批量 OCR 识别 — 多进程并行加速（系统友好版）
 
-原理：每个进程独立加载一份 pipeline 实例，同时处理不同图片。
-适用场景：一次处理多张图片（如文件夹批量 OCR）。
+修复要点:
+  1. 进程错峰启动（随机延迟），避免同时加载 N 个模型导致内存/CUP 打满
+  2. 降低子进程优先级，保证系统 UI 正常响应
+  3. 预留 1-2 个核心给 OS，避免 CPU 完全饱和
+  4. 用 imap_unordered 逐任务分发，而非一次性灌满
 
 用法:
-    python batch_ocr.py <图片目录> [--workers 4] [--threads 10]
+    python batch_ocr.py <图片目录> [--workers 4] [--threads 5]
+
+安全建议:
+    - 32GB RAM 建议 --workers <= 4
+    - 16GB RAM 建议 --workers <= 2
+    - 不确定时先用 --workers 1 测试
 """
 import time
 import os
 import sys
+import random
 import argparse
 from multiprocessing import Pool, cpu_count
 from pathlib import Path
 
+# ── Worker 初始化（在子进程中执行） ──
 
-def ocr_single(image_path: str, threads: int) -> dict:
-    """单张图片 OCR（在子进程中执行）"""
+def _init_worker(threads: int, stagger_max: float):
+    """
+    每个 Worker 启动时：随机延迟 → 设线程数 → 降优先级 → 加载模型。
+
+    随机延迟是关键：避免 N 个进程同时读磁盘/分配内存，
+    将 4×2GB=8GB 的内存峰值分散到 0~15s 的时间窗口中。
+    """
+    delay = random.uniform(0, stagger_max)
+    time.sleep(delay)
+
+    # 算子级线程数
     from paddle import core
     core.set_num_threads(threads)
 
+    # 降低进程优先级（不影响计算吞吐，但让 OS 调度更公平）
+    try:
+        import psutil
+        p = psutil.Process()
+        if sys.platform == "win32":
+            p.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS)
+        else:
+            p.nice(10)
+    except ImportError:
+        pass
+    except Exception:
+        pass
+
+    # 加载 pipeline（~2GB，耗时 ~40s）
     from paddleocr import PaddleOCRVL
+    global _pipeline
+    _pipeline = PaddleOCRVL(pipeline_version="v1.6")
 
-    pipeline = PaddleOCRVL(pipeline_version="v1.6")
 
+def _ocr_task(image_path: str) -> dict:
+    """单张图片 OCR（使用全局 pipeline）"""
+    global _pipeline
     t0 = time.perf_counter()
-    result = pipeline.predict(image_path)
+    result = _pipeline.predict(image_path)
     elapsed = time.perf_counter() - t0
 
     blocks = []
     for block in result[0]["parsing_res_list"]:
         if block.content.strip():
-            blocks.append({"label": block.label, "bbox": block.bbox, "content": block.content})
+            blocks.append({
+                "label": block.label,
+                "bbox": block.bbox,
+                "content": block.content,
+            })
 
     return {
         "path": str(image_path),
@@ -40,53 +81,127 @@ def ocr_single(image_path: str, threads: int) -> dict:
     }
 
 
+# ── 主流程 ──
+
 def main():
-    parser = argparse.ArgumentParser(description="Batch OCR with multiprocessing")
+    total_cores = cpu_count()
+
+    parser = argparse.ArgumentParser(
+        description="批量 OCR — 多进程并行（系统友好版）",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+示例:
+  python batch_ocr.py images/                          # 默认 2 进程
+  python batch_ocr.py images/ --workers 4              # 4 进程（需 32GB RAM）
+  python batch_ocr.py images/ --workers 2 --threads 8  # 指定每进程线程数
+        """,
+    )
     parser.add_argument("dir", type=str, help="图片目录")
-    parser.add_argument("--workers", type=int, default=4, help="并行进程数 (默认 4)")
-    parser.add_argument("--threads", type=int, default=None, help="每进程线程数 (默认: 总核心/workers)")
+    parser.add_argument(
+        "--workers", type=int, default=2,
+        help="并行进程数 (默认 2，安全值；最大建议不超过 RAM_GB/2)",
+    )
+    parser.add_argument(
+        "--threads", type=int, default=None,
+        help=f"每进程线程数 (默认: (总核心-1)/workers，保证 OS 有 1 核可用)",
+    )
+    parser.add_argument(
+        "--stagger", type=float, default=15.0,
+        help="进程启动错峰窗口秒数 (默认 15s，值越大内存峰值越低)",
+    )
     args = parser.parse_args()
 
+    # ── 扫描图片 ──
     image_dir = Path(args.dir)
     if not image_dir.is_dir():
         print(f"[ERROR] 目录不存在: {args.dir}")
         sys.exit(1)
 
-    images = list(image_dir.glob("*.png")) + list(image_dir.glob("*.jpg")) + list(image_dir.glob("*.jpeg"))
+    extensions = ("*.png", "*.jpg", "*.jpeg", "*.bmp", "*.tiff", "*.tif", "*.webp")
+    images = []
+    for ext in extensions:
+        images.extend(image_dir.glob(ext))
+    images = sorted(images)
+
     if not images:
         print(f"[ERROR] 目录中没有图片: {args.dir}")
         sys.exit(1)
 
+    # ── 资源规划 ──
     workers = min(args.workers, len(images))
-    threads = args.threads or max(1, cpu_count() // workers)
+    reserved_for_os = 1  # 至少给 OS 留 1 个逻辑核心
+    if args.threads:
+        threads = args.threads
+    else:
+        threads = max(1, (total_cores - reserved_for_os) // workers)
 
-    print(f"图片数量: {len(images)}")
-    print(f"并行进程: {workers}")
-    print(f"每进程线程: {threads}")
-    print(f"总核心利用: {workers * threads} / {cpu_count()}")
+    total_cpu_used = workers * threads
+    stagger = args.stagger
+
+    # 内存估算
+    model_mem_per_worker = 2.0  # GB, 模型 ~1.8GB + 运行时开销
+    estimated_mem = workers * model_mem_per_worker + 2  # +2GB for OS
+
+    try:
+        import psutil
+        avail_gb = psutil.virtual_memory().available / (1024**3)
+        mem_ok = avail_gb > estimated_mem
+    except ImportError:
+        avail_gb = None
+        mem_ok = True  # 无法检测，假定 OK
+
+    # ── 打印配置 ──
+    print("=" * 60)
+    print(f"  图片数量:       {len(images)}")
+    print(f"  并行进程:       {workers}")
+    print(f"  每进程线程:     {threads}")
+    print(f"  CPU 占用:       {total_cpu_used} / {total_cores} 核 (保留 {total_cores - total_cpu_used} 给 OS)")
+    print(f"  错峰窗口:       {stagger}s")
+    print(f"  预估内存:       ~{estimated_mem:.0f}GB (可用: {avail_gb:.0f}GB)" if avail_gb else f"  预估内存:       ~{estimated_mem:.0f}GB")
+    if not mem_ok:
+        print(f"  [WARNING] 可用内存不足！建议降低 --workers 到 {max(1, int((avail_gb - 2) / model_mem_per_worker))}")
     print("=" * 60)
 
+    if not mem_ok:
+        resp = input("内存不足，是否继续？[y/N] ").strip().lower()
+        if resp != "y":
+            print("已取消。")
+            sys.exit(0)
+
+    # ── 执行 ──
     t0 = time.perf_counter()
 
-    # 每个子进程独立加载模型 + 推理
-    with Pool(workers) as pool:
-        tasks = [(str(img), threads) for img in images]
-        results = pool.starmap(ocr_single, tasks)
+    with Pool(
+        processes=workers,
+        initializer=_init_worker,
+        initargs=(threads, stagger),
+    ) as pool:
+        # imap_unordered: 逐任务分发，先完成的先返回
+        # chunk 大 → 吞吐高但内存峰值高；chunk=1 → 最平滑
+        image_paths = [str(img) for img in images]
+        results = list(pool.imap_unordered(_ocr_task, image_paths, chunksize=1))
 
     total_elapsed = time.perf_counter() - t0
 
-    # 输出结果
+    # ── 输出 ──
     print("\n" + "=" * 60)
-    for r in results:
+    for r in sorted(results, key=lambda x: x["path"]):
         print(f"\n[文件] {r['path']} ({r['elapsed']:.1f}s)")
         for block in r["blocks"]:
-            print(f"  [{block['label']}] {block['content'][:60]}{'...' if len(block['content']) > 60 else ''}")
+            preview = block["content"].replace("\n", "\\n")
+            if len(preview) > 80:
+                preview = preview[:80] + "..."
+            print(f"  [{block['label']}] {preview}")
 
+    total_per_image = sum(r["elapsed"] for r in results)
     print("\n" + "=" * 60)
-    print(f"总图片: {len(images)} | 总耗时: {total_elapsed:.1f}s")
-    print(f"平均每图: {total_elapsed / len(images):.1f}s")
-    print(f"单进程串行预计: {sum(r['elapsed'] for r in results):.1f}s")
-    print(f"并行加速比: {sum(r['elapsed'] for r in results) / total_elapsed:.2f}x")
+    print(f"  总图片:   {len(images)}")
+    print(f"  总耗时:   {total_elapsed:.1f}s ({total_elapsed/60:.1f}min)")
+    print(f"  平均每图: {total_elapsed / len(images):.1f}s")
+    print(f"  串行预计: {total_per_image:.1f}s")
+    if total_elapsed > 0:
+        print(f"  加速比:   {total_per_image / total_elapsed:.2f}x")
+    print("=" * 60)
 
 
 if __name__ == "__main__":
diff --git a/images/名片01.jpg b/images/名片01.jpg
new file mode 100644
index 0000000..206da9b
Binary files /dev/null and b/images/名片01.jpg differ
diff --git a/images/名片02.jpg b/images/名片02.jpg
new file mode 100644
index 0000000..9f9ab28
Binary files /dev/null and b/images/名片02.jpg differ
diff --git a/logs/名片01.log b/logs/名片01.log
new file mode 100644
index 0000000..0a31ad2
--- /dev/null
+++ b/logs/名片01.log
@@ -0,0 +1,85 @@
+PS D:\Dev\ocr-VL1.6> uv run python .\main.py
+信息: 用提供的模式无法找到文件。
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\cpp_extension\extension_utils.py:718: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md
+  warnings.warn(warning_message)
+[Threads] oneDNN compiled, using 20 threads (CPU cores: 20)
+============================================================
+初始化模型...
+Creating model: ('PP-DocLayoutV3', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PP-DocLayoutV3`.
+Creating model: ('PaddleOCR-VL-1.6-0.9B', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6`.
+Bucketed engine_config has no entry for resolved engine 'paddle_dynamic'; using an empty config for that engine.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\config.json
+Loading weights file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\model.safetensors
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.split.html first.
+  warnings.warn(
+Loaded weights file from disk, setting weights to model.
+All model checkpoint weights were used when initializing PaddleOCRVLForConditionalGeneration.
+
+All the weights of PaddleOCRVLForConditionalGeneration were initialized from the model checkpoint at C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use PaddleOCRVLForConditionalGeneration for predictions without further training.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\generation_config.json
+[OK] 模型初始化耗时: 40.03s
+============================================================
+
+开始 OCR 识别: images/名片01.jpg
+预热 0 轮 + 正式测试 1 轮
+
+  推理 1/1... D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\tensor\creation.py:1088: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), rather than paddle.to_tensor(sourceTensor).
+  return tensor(
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.max.html first.
+  warnings.warn(
+81.30s
+
+============================================================
+[Benchmark]
+  图片尺寸: 591 x 360
+  检测文本块: 7 个
+  识别文本块: 6 个
+  推理次数: 1
+  最快: 81.30s
+  最慢: 81.30s
+  平均: 81.30s
+============================================================
+
+[识别结果]
+
+  [text] ([397, 17, 549, 38])
+  材质：250g黄彩石纹
+
+  [header_image] ([39, 65, 184, 106])
+
+
+  [image] ([433, 100, 529, 152])
+
+
+  [paragraph_title] ([30, 188, 306, 244])
+  太原承方科技有限公司
+山西承方印刷物资有限公司
+
+  [text] ([30, 282, 302, 306])
+  地址：太原市南十方街东中环口西南角
+
+  [text] ([32, 302, 471, 332])
+  Q Q: 800806277 手机: 13703585513 网址: www.cfprint.cn
\ No newline at end of file
diff --git a/logs/名片02.log b/logs/名片02.log
new file mode 100644
index 0000000..e20b018
--- /dev/null
+++ b/logs/名片02.log
@@ -0,0 +1,85 @@
+PS D:\Dev\ocr-VL1.6> uv run python .\main.py
+信息: 用提供的模式无法找到文件。
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\cpp_extension\extension_utils.py:718: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md
+  warnings.warn(warning_message)
+[Threads] oneDNN compiled, using 20 threads (CPU cores: 20)
+============================================================
+初始化模型...
+Creating model: ('PP-DocLayoutV3', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PP-DocLayoutV3`.
+Creating model: ('PaddleOCR-VL-1.6-0.9B', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6`.
+Bucketed engine_config has no entry for resolved engine 'paddle_dynamic'; using an empty config for that engine.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\config.json
+Loading weights file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\model.safetensors
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.split.html first.
+  warnings.warn(
+Loaded weights file from disk, setting weights to model.
+All model checkpoint weights were used when initializing PaddleOCRVLForConditionalGeneration.
+
+All the weights of PaddleOCRVLForConditionalGeneration were initialized from the model checkpoint at C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use PaddleOCRVLForConditionalGeneration for predictions without further training.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\generation_config.json
+[OK] 模型初始化耗时: 51.99s
+============================================================
+
+开始 OCR 识别: images/名片02.jpg
+预热 0 轮 + 正式测试 1 轮
+
+  推理 1/1... D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\tensor\creation.py:1088: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), rather than paddle.to_tensor(sourceTensor).
+  return tensor(
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.max.html first.
+  warnings.warn(
+111.56s
+
+============================================================
+[Benchmark]
+  图片尺寸: 1440 x 864
+  检测文本块: 6 个
+  识别文本块: 6 个
+  推理次数: 1
+  最快: 111.56s
+  最慢: 111.56s
+  平均: 111.56s
+============================================================
+
+[识别结果]
+
+  [paragraph_title] ([87, 59, 333, 130])
+  白滑影
+
+  [paragraph_title] ([713, 176, 1006, 300])
+  姜美丽
+13388866888
+
+  [paragraph_title] ([312, 423, 655, 720])
+  夏佳人
+
+  [paragraph_title] ([710, 494, 1346, 563])
+  女子时尚瘦身健美馆
+
+  [text] ([713, 572, 1342, 615])
+  地址：上海市湘江大街88号滨江大厦
+
+  [text] ([714, 619, 1208, 660])
+  电话：83566666 83588888
\ No newline at end of file
diff --git a/运行返回内容.log b/logs/手写01.log
similarity index 100%
rename from 运行返回内容.log
rename to logs/手写01.log
diff --git a/logs/批量识别.log b/logs/批量识别.log
new file mode 100644
index 0000000..68d7949
--- /dev/null
+++ b/logs/批量识别.log
@@ -0,0 +1,121 @@
+PS D:\Dev\ocr-VL1.6> uv run python batch_ocr.py images/
+============================================================
+  图片数量:       3
+  并行进程:       2
+  每进程线程:     9
+  CPU 占用:       18 / 20 核 (保留 2 给 OS)
+  错峰窗口:       15.0s
+  预估内存:       ~6GB (可用: 17GB)
+============================================================
+信息: 用提供的模式无法找到文件。
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\cpp_extension\extension_utils.py:718: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md
+  warnings.warn(warning_message)
+Creating model: ('PP-DocLayoutV3', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PP-DocLayoutV3`.
+信息: 用提供的模式无法找到文件。
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\cpp_extension\extension_utils.py:718: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md
+  warnings.warn(warning_message)
+Creating model: ('PaddleOCR-VL-1.6-0.9B', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6`.
+Bucketed engine_config has no entry for resolved engine 'paddle_dynamic'; using an empty config for that engine.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\config.json
+Loading weights file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\model.safetensors
+Creating model: ('PP-DocLayoutV3', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PP-DocLayoutV3`.
+Creating model: ('PaddleOCR-VL-1.6-0.9B', None, None)
+Model files already exist. Using cached files. To redownload, please delete the directory manually: `C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6`.
+Bucketed engine_config has no entry for resolved engine 'paddle_dynamic'; using an empty config for that engine.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\config.json
+Loading weights file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\model.safetensors
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+use GQA - num_heads: 16- num_key_value_heads: 2
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.split.html first.
+  warnings.warn(
+Loaded weights file from disk, setting weights to model.
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.split.html first.
+  warnings.warn(
+Loaded weights file from disk, setting weights to model.
+All model checkpoint weights were used when initializing PaddleOCRVLForConditionalGeneration.
+
+All the weights of PaddleOCRVLForConditionalGeneration were initialized from the model checkpoint at C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use PaddleOCRVLForConditionalGeneration for predictions without further training.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\generation_config.json
+All model checkpoint weights were used when initializing PaddleOCRVLForConditionalGeneration.
+
+All the weights of PaddleOCRVLForConditionalGeneration were initialized from the model checkpoint at C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use PaddleOCRVLForConditionalGeneration for predictions without further training.
+Loading configuration file C:\Users\kuuhaku_0\.paddlex\official_models\PaddleOCR-VL-1.6\generation_config.json
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\tensor\creation.py:1088: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), rather than paddle.to_tensor(sourceTensor).
+  return tensor(
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.max.html first.
+  warnings.warn(
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\tensor\creation.py:1088: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), rather than paddle.to_tensor(sourceTensor).
+  return tensor(
+D:\Dev\ocr-VL1.6\.venv\Lib\site-packages\paddle\utils\decorator_utils.py:420: Warning:
+Non compatible API. Please refer to https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/model_convert/convert_from_pytorch/api_difference/torch/torch.max.html first.
+  warnings.warn(
+
+============================================================
+
+[文件] images\名片01.jpg (65.1s)
+  [text] 材质：250g黄彩石纹
+  [paragraph_title] 太原承方科技有限公司\n山西承方印刷物资有限公司
+  [text] 地址：太原市南十方街东中环口西南角
+  [text] Q Q: 800806277 手机: 13703585513 网址: www.cfprint.cn
+
+[文件] images\名片02.jpg (83.2s)
+  [paragraph_title] 白滑影
+  [paragraph_title] 姜美丽\n13388866888
+  [paragraph_title] 夏佳人
+  [paragraph_title] 女子时尚瘦身健美馆
+  [text] 地址：上海市湘江大街88号滨江大厦
+  [text] 电话：83566666 83588888
+
+[文件] images\手写01.png (156.6s)
+  [text] 最优质的草场。但是这两年由于旱獭和草原黄鼠的逐年\n保护,人们的行为需要矫正。\n部分。末日的时刻。\n佛巴林卡塔尔等海
+  [text] 很多消费者没有认识到医学验光的重要性,导致诸多后果。\n特许商品的销售便逐渐升温。文化软实力作出重要贡献。
+
+============================================================
+  总图片:   3
+  总耗时:   275.6s (4.6min)
+  平均每图: 91.9s
+  串行预计: 304.9s
+  加速比:   1.11x
+============================================================
\ No newline at end of file
diff --git a/main.py b/main.py
index f56fb0e..eaf21ca 100644
--- a/main.py
+++ b/main.py
@@ -3,7 +3,7 @@ import os
 from paddle import core
 from paddleocr import PaddleOCRVL
 
-IMAGE_PATH = "images/手写01.png"
+IMAGE_PATH = "images/名片02.jpg"
 WARMUP_ROUNDS = 0
 BENCHMARK_ROUNDS = 1