# cv::error: (-215:断言失败) inputs[0].size[0] == net.getLayer(0)->outputs[0].size[0] 在函数 'cv::dnn::Net::forward' 中

- **ID:** `opencv/dnn-batch-size-mismatch`
- **领域:** opencv
- **类别:** assertion_error
- **错误码:** `-215`
- **验证级别:** ai_generated
- **修复率:** 88%

## 根因

输入 blob 的批次大小与 DNN 网络第一层期望的批次大小不匹配。

## 版本兼容性

| 版本 | 状态 | 引入 | 弃用 |
|------|------|------|------|
| 4.7.0 | active | — | — |
| 4.8.1 | active | — | — |
| 4.9.0 | active | — | — |

## 解决方案

1. ```
   Inspect the expected input shape using net.getLayer(0).outputs[0].size[0] and set the blob batch size accordingly. For single-image inference, use blobFromImage which defaults to batch size 1.
   ```
2. ```
   If the model requires batch size > 1, create a blob with multiple images using blobFromImages and ensure the first dimension equals the network's expected batch size.
   ```

## 无效尝试

- **Manually reshaping the input blob without checking the network's expected input shape** — Reshaping may alter data layout or fail to match the exact batch size dimension; the network expects a specific batch size (e.g., 1). (90% 失败率)
- **Setting the blob size to a random large batch (e.g., 32) assuming the network supports dynamic batching** — Many pretrained models have a fixed batch size (usually 1) and do not support dynamic batching; this will cause the assertion to fail. (95% 失败率)