yolov7ota损失函数

yolov7ota的损失函数包括正样本与目标的IOU loss、objectness loss和分类 loss这三个部分。在train.py中，可以看到以下代码用于计算损失值：

if 'loss_ota' not in hyp or hyp['loss_ota'] == 1:
    loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size
    break
else:
    loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size

其中，如果配置文件中的`loss_ota`不在配置文件中或者等于1，则使用`compute_loss_ota`函数来计算损失值，否则使用`compute_loss`函数。在计算完`compute_loss_ota`或`compute_loss`之后，会得到一个`pair_wise_cls_loss`的维度为[8,99]的结果。然后将该损失与之前计算的IOU loss相加，再乘以3.0，得到最终的cost值。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [解读YOLO v7的代码(三)损失函数](https://blog.csdn.net/gzroy/article/details/128668915)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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