Initial release: nano/small/full captcha recognition models

- nano (95.49%, 94KB): minimal size for embedded use
- small (99.96%, 390KB): recommended for general deployment
- full (99.97%, 780KB): highest accuracy
- distill-nano: knowledge distillation experiment

.gitattributes

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+*.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

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+checkpoint_epoch_*.pth
+best_model.pth

README.md

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+---
+language:
+  - zh
+license: mit
+tags:
+  - pytorch
+  - image-classification
+  - captcha
+  - ocr
+  - cnn
+pipeline_tag: image-classification
+metrics:
+  - accuracy
+model-index:
+  - name: zfw-captcha-model
+    results:
+      - task:
+          type: image-classification
+          name: Captcha Recognition
+        metrics:
+          - type: accuracy
+            value: 99.96
+            name: Whole-image Accuracy (small)
+          - type: accuracy
+            value: 99.97
+            name: Whole-image Accuracy (full)
+          - type: accuracy
+            value: 95.49
+            name: Whole-image Accuracy (nano)
+---
+
+# ZFW Captcha Recognition Model
+
+针对**正方教务系统自服务平台**的 4 位纯数字验证码识别模型。纯 CNN 架构，无需 RNN/CTC，轻量高效。
+
+## Model Variants
+
+| 文件 | 变体 | 参数量 | 文件大小 | 验证集准确率 | 推荐场景 |
+|------|------|--------|----------|-------------|----------|
+| `small/final_model.pth` | **small** | ~96K | 390 KB | **99.96%** | 通用部署（推荐） |
+| `full/final_model.pth` | full | ~196K | 780 KB | 99.97% | 追求极致精度 |
+| `nano/final_model.pth` | nano | ~21K | 94 KB | 95.49% | 极致压缩 / 嵌入式 |
+| `distill-nano/final_model.pth` | nano (distilled) | ~21K | 94 KB | — | 蒸馏实验产物 |
+
+> **推荐选择 `small`**：390KB 即可达到 99.96% 准确率，性价比最高。
+
+## Task Description
+
+- **验证码类型**：4 位纯数字（0-9），固定长度
+- **来源平台**：正方教务系统（ZFW）自服务平台
+- **干扰形式**：旋转、噪点、干扰线
+- **输入尺寸**：90 × 34 像素，RGB
+
+### Sample
+
+```
+┌──────────────────┐
+│  3 8 0 7         │  ← 带噪点和干扰线的 4 位数字
+└──────────────────┘
+```
+
+## Architecture
+
+```
+Input (3, 34, 90)
+    → [Conv3×3 + BN + ReLU + MaxPool] × 3   (空间降采样)
+    → [Conv3×3 + BN + ReLU] × N             (特征提取)
+    → AdaptiveAvgPool2d(1, 4)                (压缩为 4 列，对应 4 个数字位置)
+    → 4 × Linear(C, 10)                     (每个位置独立 10 分类)
+Output: (B, 4, 10) logits
+```
+
+设计理由：验证码为固定 4 位、位置固定的纯数字，不存在变长对齐问题，因此使用空间池化 + 多头分类代替 RNN/CTC，简单高效。
+
+## Quick Start
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# 1. Define model (copy from src/model.py or install the package)
+from model import build_model
+
+# 2. Load
+model = build_model('small')
+model.load_state_dict(torch.load('small/final_model.pth', map_location='cpu'))
+model.eval()
+
+# 3. Preprocess
+transform = transforms.Compose([
+    transforms.Resize((34, 90)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+img = Image.open('captcha.png').convert('RGB')
+x = transform(img).unsqueeze(0)  # (1, 3, 34, 90)
+
+# 4. Predict
+with torch.no_grad():
+    logits = model(x)              # (1, 4, 10)
+    digits = logits.argmax(dim=2)  # (1, 4)
+    result = ''.join(str(d.item()) for d in digits[0])
+
+print(result)  # e.g. "3807"
+```
+
+## Training
+
+- **框架**：PyTorch
+- **损失函数**：CrossEntropyLoss × 4（每位数字独立）
+- **优化器**：Adam (lr=0.001, fused)
+- **学习率调度**：StepLR (step=10, gamma=0.5)
+- **早停**：patience=8
+- **数据增强**：无（仅 Normalize）
+- **训练监控**：[SwanLab](https://swanlab.cn/@nancunchild/zfw_captcha_train)
+
+### Training Curves
+
+完整训练过程（loss、accuracy、learning rate 曲线）请查看：
+
+**[SwanLab Dashboard](https://swanlab.cn/@nancunchild/zfw_captcha_train)**
+
+## Source Code
+
+训练代码开源：[GitHub - zfw_captcha_train](https://github.com/NanCunChild/zfw_captcha_train)
+
+## Limitations
+
+- 仅支持正方教务系统特定样式的验证码
+- 仅识别 4 位纯数字（0-9），不支持字母或其他字符
+- 输入图片应为 90×34 或等比例尺寸
+
+## License
+
+MIT

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