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Segformer Environment 1

This repository contains a fine-tuned SegFormer model for semantic segmentation of outdoor scenes, specifically targeting:

Quellehuggingface.co/Dinusharg/segformer_environment_1/tree/50a432a96fc13267dfb649082c1a54a24c18c0ac

ArtOtherVersionRevision 50a432a

Modellquelle

Art: Other
Dateigröße: 14,2 MB
Version: vRevision 50a432a
Aktualisiert: 15. Sept. 2026
Downloads: 10
Quelle: Hugging Face

Quellenbeschreibung

SegFormer Semantic Segmentation (Road / Grass / Footpath / Water)

This repository contains a fine-tuned SegFormer model for semantic segmentation of outdoor scenes, specifically targeting:

background
footpath
grass
road
water

The model is designed for applications such as:

Autonomous navigation
Robotics perception
Scene understanding
Smart city / mapping solutions

Model Details

Architecture: SegFormer
Framework: Hugging Face Transformers
Input Size: 224 × 224
Task: Semantic Segmentation
Number of Classes: 5

Class Labels

ID	Label
0	background
1	footpath
2	grass
3	road
4	water

Quick Start

1. Install Dependencies

pip install transformers torch pillow

2. Run Inference

from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
from PIL import Image
import torch
import torch.nn.functional as F
import numpy as np

MODEL_ID = "Dinusharg/segformer_environment_1"
IMAGE_PATH = "input_image_path"

OUT_MASK = "segmented_mask.png"
OUT_OVERLAY = "segmented_overlay.png"

palette = {
    0: (0, 0, 0),        # background
    1: (255, 0, 0),      # footpath
    2: (0, 255, 0),      # grass
    3: (128, 128, 128),  # road
    4: (0, 0, 255),      # water
}

processor = SegformerImageProcessor.from_pretrained(MODEL_ID)
model = SegformerForSemanticSegmentation.from_pretrained(MODEL_ID)

image = Image.open(IMAGE_PATH).convert("RGB")
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

upsampled_logits = F.interpolate(
    outputs.logits,
    size=image.size[::-1],
    mode="bilinear",
    align_corners=False
)

pred = upsampled_logits.argmax(dim=1)[0].cpu().numpy()

print("Unique predicted classes:", sorted(set(pred.flatten().tolist())))
print("Labels:", model.config.id2label)


color_mask = np.zeros((pred.shape[0], pred.shape[1], 3), dtype=np.uint8)

for class_id, color in palette.items():
    color_mask[pred == class_id] = color

Image.fromarray(color_mask).save(OUT_MASK)
print(f"Saved mask: {OUT_MASK}")


ALPHA = 0.5

image_np = np.array(image).astype(np.float32)
mask_np = color_mask.astype(np.float32)

overlay = (image_np * (1 - ALPHA) + mask_np * ALPHA).clip(0, 255).astype(np.uint8)

Image.fromarray(overlay).save(OUT_OVERLAY)
print(f"Saved overlay: {OUT_OVERLAY}")

This will returns the colored masked and overlay masked images.

Hugging Face Space (Live Demo)

Try the model interactively:

Launch Demo Space

Upload an image and visualize segmentation results instantly.

Sample Results

Input Image	Segmentation Mask	Overlay Output
image	image	image
image	image	image
image	image	image

Contact

For questions or collaboration:

Quellen

1 Quelle

Hugging FacePrimärVersion 50a432a96fc13267dfb649082c1a54a24c18c0ac

huggingface.co/Dinusharg/segformer_environment_1/tree/50a432a96fc13267dfb649082c1a54a24c18c0ac

Verifiziert 15. Sept.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 14,2 MB · SHA-256 0191def7ebe9…00c7 · Hugging Face

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Quellenauszüge

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Readme15. Sept.huggingface.co/Dinusharg/segformer_environment_1

--- license: agpl-3.0 library_name: transformers pipeline_tag: image-segmentation tags: - segformer - semantic-segmentation - computer-vision - environment - footpath - grass - road - water - robotic - autonomous navigation - Scene understanding --- # SegFormer Semantic Segmentation (Road / Grass / Footpath / Water) This repository contains a fine-tuned **SegFormer** model for semantic segmentation of outdoor scenes, specifically targeting: - background - footpath - grass - road - water The model is designed for applications such as: - Autonomous navigation - Robotics perception - Scene understanding - Smart city / mapping solutions ## Model Details - **Architecture**: SegFormer - **Framework**: Hugging Face Transformers - **Input Size**: 224 × 224 - **Task**: Semantic Segmentation - **Number of Classes**: 5 ## Class Labels |ID |Label | |---|----------| |0 |background| |1 |footpath | |2 |grass | |3 |road | |4 |water | ## Quick Start ### 1. Install Dependencies ``` pip install transformers torch pillow ``` ### 2. Run Inference ```python from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor from PIL import Image import torch import torch.nn.functional as F import numpy as np MODEL_ID = "Dinusharg/segformer_environment_1" IMAGE_PATH = "input_image_path" OUT_MASK = "segmented_mask.png" OUT_OVERLAY = "segmented_overlay.png" palette = { 0: (0, 0, 0), # background 1: (255, 0, 0), # footpath 2: (0, 255, 0), # grass 3: (128, 128, 128), # road 4: (0, 0, 255), # water } processor = SegformerImageProcessor.from_pretrained(MODEL_ID) model = SegformerForSemanticSegmentation.from_pretrained(MODEL_ID) image = Image.open(IMAGE_PATH).convert("RGB") inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) upsampled_logits = F.interpolate( outputs.logits, size=image.size[::-1], mode="bilinear", align_corners=False ) pred = upsampled_logits.argmax(dim=1)[0].cpu().numpy() print("Unique predicted classes:", sorted(set(pred.flatten().tolist()))) print("Labels:", model.config.id2label) color_mask = np.zeros((pred.shape[0], pred.shape[1], 3), dtype=np.uint8) for class_id, color in palette.items(): color_mask[pred == class_id] = color Image.fromarray(color_mask).save(OUT_MASK) print(f"Saved mask: {OUT_MASK}") ALPHA = 0.5 image_np = np.array(image).astype(np.float32) mask_n...

Note15. Sept.huggingface.co/Dinusharg/segformer_environment_1

Source context: 10 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo Dinusharg/segformer_environment_1