# cv::error: (-215:Assertion failed) inputs[0].size[0] == net.getLayer(0)->outputs[0].size[0] in function 'cv::dnn::Net::forward'

- **ID:** `opencv/dnn-batch-size-mismatch`
- **Domain:** opencv
- **Category:** assertion_error
- **Error Code:** `-215`
- **Verification:** ai_generated
- **Fix Rate:** 88%

## Root Cause

The batch size of the input blob does not match the batch size expected by the first layer of the DNN network.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| 4.7.0 | active | — | — |
| 4.8.1 | active | — | — |
| 4.9.0 | active | — | — |

## Workarounds

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.** (95% success)
   ```
   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.** (85% success)
   ```
   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.
   ```

## Dead Ends

- **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% fail)
- **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% fail)