许可证：其他
基础模型：nvidia/mit-b0
标签：

• 视觉
• 图像分割
• 训练生成
  数据集：
• scene_parse_150
  模型索引：
• 名称：segformer-b0-scene-parse-150
  结果：[]
  小部件：
• 示例图片：https://i.imgur.com/G0WTlvI.jpg
  示例标题：测试

segformer-b0-scene-parse-150

该模型是基于nvidia/mit-b0在scene_parse_150数据集上微调的版本。
在评估集上取得了以下结果：

• 损失：2.3431
• 平均交并比（Mean Iou）：0.0959
• 平均准确率（Mean Accuracy）：0.1537
• 整体准确率（Overall Accuracy）：0.5496
• 各类别交并比（Per Category Iou）：[0.44824978876617866, 0.7548671615728508, 0.7119201505944329, 0.5304481563680256, 0.5684691275095736, 0.33051502835188457, 0.6982393617021276, 0.0, 0.3703529914609331, 0.6659141206351092, 0.028823893043720683, 0.17181416221210322, 0.052153820762502065, 0.0, 0.0, 0.0005543923800536699, 0.40565901784724534, 0.05230759173712194, 0.0, 0.07225859019823891, 0.29980315155352005, nan, 0.003601361102652032, 0.0, 0.0, nan, 0.0, 0.0, 0.38898705304076847, 0.05940808241958817, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0]
• 各类别准确率（Per Category Accuracy）：[0.8427949438202247, 0.9402615186644498, 0.7846678763016725, 0.7286579984703183, 0.8303175022736334, 0.469325820621132, 0.9020126572710594, nan, 0.5974398752913491, 0.9683369330453564, 0.05725843345934362, 0.24220857754209693, 0.12377594986290638, 0.0, 0.0, 0.0005611873291065182, 0.9580213623749935, 0.08566177782535773, 0.0, 0.16335928996064641, 0.43531591571750716, nan, 0.0036190907034607555, 0.0, 0.0, nan, nan, 0.0, 0.45750991876062724, 0.24276243093922653, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0]

模型描述

需补充更多信息

预期用途与限制

需补充更多信息

训练与评估数据

需补充更多信息

训练过程

训练超参数

训练期间使用的超参数如下：

• 学习率：6e-05
• 训练批次大小：2
• 评估批次大小：2
• 随机种子：42
• 优化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
• 学习率调度器类型：线性
• 训练轮次：50

训练结果