README.md

metadata

library_name: transformers
tags:
  - vision
  - semantic-segmentation
  - segformer
  - sky
  - sea
  - obstacle
  - huggingface
license: apache-2.0
datasets:
  - Wilbur1240/MaSTr1325_512x384
base_model:
  - nvidia/segformer-b0-finetuned-ade-512-512

Segformer Fine-Tuned on Custom Sky/Sea/Obstacle Dataset

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on a custom dataset with 3 semantic classes:

• Sky
• Sea
• Obstacle

It is intended for use in vision-based autonomous surface navigation and maritime scene understanding.

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Model Details

Model Description

• Base architecture: SegFormer-B0
• Pretrained on: ADE20K dataset
• Fine-tuned for: Semantic segmentation on maritime images
• Number of classes: 3
• Ignore index: 255
• Resolution: 512×512 input images
• Training precision: fp32
• Framework: PyTorch with 🤗 Transformers

---

Model Sources

• Base model: nvidia/segformer-b0-finetuned-ade-512-512
• Codebase: Uses Hugging Face Transformers + Datasets

---

Usage

from transformers import AutoModelForSemanticSegmentation, AutoImageProcessor
from PIL import Image
import torch

# Load model and processor
model = AutoModelForSemanticSegmentation.from_pretrained("Wilbur1240/segformer-b0-finetuned-ade-512-512-finetune-mastr1325")
processor = AutoImageProcessor.from_pretrained("Wilbur1240/segformer-b0-finetuned-ade-512-512-finetune-mastr1325")

# Load and preprocess an image
image = Image.open("example.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

# Inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits  # [1, num_classes, H, W]
    pred_seg = logits.argmax(dim=1)  # [1, H, W]