| --- |
| 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 |
|
|
| ### Samples |
|
|
| | 样本 | 标签 | |
| |------|------| |
| |  | `9800` | |
| |  | `9350` | |
|
|
| ## 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 |
|
|
