add

README.md

@@ -1,7 +1,7 @@
 ---
-title: Machine Learning
-emoji: 👁
-colorFrom: purple
+title: Segmentation
+emoji: 📚
+colorFrom: red
 colorTo: blue
 sdk: gradio
 sdk_version: 5.49.1

app.py

@@ -6,31 +6,38 @@ from PIL import Image
 import torch
 from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
 
-MODEL_ID = "mattmdjaga/segformer_b2_clothes"
+MODEL_ID = "nvidia/segformer-b5-finetuned-ade-640-640"
 processor = AutoImageProcessor.from_pretrained(MODEL_ID)
 model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID)
 
 def ade_palette():
     """ADE20K palette that maps each class to RGB values."""
     return [
-        [0, 0, 0],  # background
-        [255, 0, 0],  # hat
-        [255, 255, 0],  # hair
-        [0, 255, 255],  # sunglasses
-        [0, 128, 255],  # upper-clothes
-        [255, 0, 255],  # skirt
-        [0, 200, 0],  # pants
-        [255, 128, 0],  # dress
-        [128, 0, 255],  # belt
-        [255, 192, 203],  # left-shoe
-        [255, 165, 0],  # right-shoe
-        [180, 180, 180],  # face
-        [0, 100, 0],  # left-leg
-        [34, 139, 34],  # right-leg
-        [70, 130, 180],  # left-arm
-        [25, 25, 112],  # right-arm
-        [210, 105, 30],  # bag
-        [123, 104, 238],  # scarf
+        [204, 87, 92],[112, 185, 212],[45, 189, 106],[234, 123, 67],[78, 56, 123],[210, 32, 89],
+        [90, 180, 56],[155, 102, 200],[33, 147, 176],[255, 183, 76],[67, 123, 89],[190, 60, 45],
+        [134, 112, 200],[56, 45, 189],[200, 56, 123],[87, 92, 204],[120, 56, 123],[45, 78, 123],
+        [156, 200, 56],[32, 90, 210],[56, 123, 67],[180, 56, 123],[123, 67, 45],[45, 134, 200],
+        [67, 56, 123],[78, 123, 67],[32, 210, 90],[45, 56, 189],[123, 56, 123],[56, 156, 200],
+        [189, 56, 45],[112, 200, 56],[56, 123, 45],[200, 32, 90],[123, 45, 78],[200, 156, 56],
+        [45, 67, 123],[56, 45, 78],[45, 56, 123],[123, 67, 56],[56, 78, 123],[210, 90, 32],
+        [123, 56, 189],[45, 200, 134],[67, 123, 56],[123, 45, 67],[90, 32, 210],[200, 45, 78],
+        [32, 210, 90],[45, 123, 67],[165, 42, 87],[72, 145, 167],[15, 158, 75],[209, 89, 40],
+        [32, 21, 121],[184, 20, 100],[56, 135, 15],[128, 92, 176],[1, 119, 140],[220, 151, 43],
+        [41, 97, 72],[148, 38, 27],[107, 86, 176],[21, 26, 136],[174, 27, 90],[91, 96, 204],
+        [108, 50, 107],[27, 45, 136],[168, 200, 52],[7, 102, 27],[42, 93, 56],[140, 52, 112],
+        [92, 107, 168],[17, 118, 176],[59, 50, 174],[206, 40, 143],[44, 19, 142],[23, 168, 75],
+        [54, 57, 189],[144, 21, 15],[15, 176, 35],[107, 19, 79],[204, 52, 114],[48, 173, 83],
+        [11, 120, 53],[206, 104, 28],[20, 31, 153],[27, 21, 93],[11, 206, 138],[112, 30, 83],
+        [68, 91, 152],[153, 13, 43],[25, 114, 54],[92, 27, 150],[108, 42, 59],[194, 77, 5],
+        [145, 48, 83],[7, 113, 19],[25, 92, 113],[60, 168, 79],[78, 33, 120],[89, 176, 205],
+        [27, 200, 94],[210, 67, 23],[123, 89, 189],[225, 56, 112],[75, 156, 45],[172, 104, 200],
+        [15, 170, 197],[240, 133, 65],[89, 156, 112],[214, 88, 57],[156, 134, 200],[78, 57, 189],
+        [200, 78, 123],[106, 120, 210],[145, 56, 112],[89, 120, 189],[185, 206, 56],[47, 99, 28],
+        [112, 189, 78],[200, 112, 89],[89, 145, 112],[78, 106, 189],[112, 78, 189],[156, 112, 78],
+        [28, 210, 99],[78, 89, 189],[189, 78, 57],[112, 200, 78],[189, 47, 78],[205, 112, 57],
+        [78, 145, 57],[200, 78, 112],[99, 89, 145],[200, 156, 78],[57, 78, 145],[78, 57, 99],
+        [57, 78, 145],[145, 112, 78],[78, 89, 145],[210, 99, 28],[145, 78, 189],[57, 200, 136],
+        [89, 156, 78],[145, 78, 99],[99, 28, 210],[189, 78, 47],[28, 210, 99],[78, 145, 57],
     ]
 
 labels_list = []
@@ -97,11 +104,10 @@ demo = gr.Interface(
     inputs=gr.Image(type="numpy", label="Input Image"),
     outputs=gr.Plot(label="Overlay + Legend"),
     examples=[
-        "person-1.jpg",
-        "person-2.jpg",
-        "person-3.jpg",
-        "person-4.jpg",
-        "person-5.jpg"
+        "ADE_val_00000001.jpeg",
+        "ADE_val_00001159.jpg",
+        "ADE_val_00001248.jpg",
+        "ADE_val_00001472.jpg"
     ],
     flagging_mode="never",
     cache_examples=False,

labels.txt

@@ -1,18 +1,150 @@
-Background
-Hat
-Hair
-Sunglasses
-Upper-clothes
-Skirt
-Pants
-Dress
-Belt
-Left-shoe
-Right-shoe
-Face
-Left-leg
-Right-leg
-Left-arm
-Right-arm
-Bag
-Scarf
+wall
+building
+sky
+floor
+tree
+ceiling
+road
+bed
+windowpane
+grass
+cabinet
+sidewalk
+person
+earth
+door
+table
+mountain
+plant
+curtain
+chair
+car
+water
+painting
+sofa
+shelf
+house
+sea
+mirror
+rug
+field
+armchair
+seat
+fence
+desk
+rock
+wardrobe
+lamp
+bathtub
+railing
+cushion
+base
+box
+column
+signboard
+chest of drawers
+counter
+sand
+sink
+skyscraper
+fireplace
+refrigerator
+grandstand
+path
+stairs
+runway
+case
+pool table
+pillow
+screen door
+stairway
+river
+bridge
+bookcase
+blind
+coffee table
+toilet
+flower
+book
+hill
+bench
+countertop
+stove
+palm
+kitchen island
+computer
+swivel chair
+boat
+bar
+arcade machine
+hovel
+bus
+towel
+light
+truck
+tower
+chandelier
+awning
+streetlight
+booth
+television receiver
+airplane
+dirt track
+apparel
+pole
+land
+bannister
+escalator
+ottoman
+bottle
+buffet
+poster
+stage
+van
+ship
+fountain
+conveyer belt
+canopy
+washer
+plaything
+swimming pool
+stool
+barrel
+basket
+waterfall
+tent
+bag
+minibike
+cradle
+oven
+ball
+food
+step
+tank
+trade name
+microwave
+pot
+animal
+bicycle
+lake
+dishwasher
+screen
+blanket
+sculpture
+hood
+sconce
+vase
+traffic light
+tray
+ashcan
+fan
+pier
+crt screen
+plate
+monitor
+bulletin board
+shower
+radiator
+glass
+clock
+flag