使用自定義資料集微調 SegFormer 語義分割模型
Original: Fine-Tune a Semantic Segmentation Model with a Custom Dataset
This practical tutorial from Hugging Face provides a detailed guide on how to fine-tune the SegFormer model on a custom dataset for…
本指南介紹如何利用 Hugging Face 的 Transformers 庫微調 SegFormer 進行語義分割。內容涵蓋自定義資料集的準備、使用 SegformerImageProcessor 進行圖像預處理，以及設定 Trainer API 進行訓練。最後，教學展示了如何使用 mIoU 評估模型效能並進行推理。
This practical tutorial from Hugging Face provides a detailed guide on how to fine-tune the SegFormer model on a custom dataset for semantic segmentation. SegFormer is a classic semantic segmentation model developed by NVIDIA that combines a hierarchical Transformer encoder with a lightweight MLP decoder. It is not only highly efficient, but also requires no positional encoding, making it easy to adapt to varying input resolutions.
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