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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。
Fonte do modelo
Descrição da fonte
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。
Fontes
1 fonteVerificado 10 de set.
Artefatos de modelo
4 artefatosTrechos de fonte
2 trechos| 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。
| 样本 | 标签 |
|---|---|
| 9800 | 9800 |
| 9350 | 9350 |
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。
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"
完整训练过程(loss、accuracy、learning rate 曲线)请查看:
训练代码开源:GitHub - zfw_captcha_train
MIT
nano/final_model.pth
pth · 94,0 KB · SHA-256 e128ba0c158e…7999 · Hugging Face
--- 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...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo NanCunChild/zfw-captcha-model