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  1. ADE_val_00000001.jpeg +0 -0
  2. ADE_val_00001159.jpg +0 -0
  3. ADE_val_00001248.jpg +0 -0
  4. ADE_val_00001472.jpg +0 -0
  5. app.py +21 -152
  6. labels.txt +18 -150
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app.py CHANGED
@@ -8,165 +8,34 @@ import tensorflow as tf
8
  from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
9
 
10
  feature_extractor = SegformerFeatureExtractor.from_pretrained(
11
- "nvidia/segformer-b5-finetuned-ade-640-640"
12
  )
13
  model = TFSegformerForSemanticSegmentation.from_pretrained(
14
- "nvidia/segformer-b5-finetuned-ade-640-640"
15
  )
16
 
17
  def ade_palette():
18
  """ADE20K palette that maps each class to RGB values."""
19
  return [
20
- [204, 87, 92],
21
- [112, 185, 212],
22
- [45, 189, 106],
23
- [234, 123, 67],
24
- [78, 56, 123],
25
- [210, 32, 89],
26
- [90, 180, 56],
27
- [155, 102, 200],
28
- [33, 147, 176],
29
- [255, 183, 76],
30
- [67, 123, 89],
31
- [190, 60, 45],
32
- [134, 112, 200],
33
- [56, 45, 189],
34
- [200, 56, 123],
35
- [87, 92, 204],
36
- [120, 56, 123],
37
- [45, 78, 123],
38
- [156, 200, 56],
39
- [32, 90, 210],
40
- [56, 123, 67],
41
- [180, 56, 123],
42
- [123, 67, 45],
43
- [45, 134, 200],
44
- [67, 56, 123],
45
- [78, 123, 67],
46
- [32, 210, 90],
47
- [45, 56, 189],
48
- [123, 56, 123],
49
- [56, 156, 200],
50
- [189, 56, 45],
51
- [112, 200, 56],
52
- [56, 123, 45],
53
- [200, 32, 90],
54
- [123, 45, 78],
55
- [200, 156, 56],
56
- [45, 67, 123],
57
- [56, 45, 78],
58
- [45, 56, 123],
59
- [123, 67, 56],
60
- [56, 78, 123],
61
- [210, 90, 32],
62
- [123, 56, 189],
63
- [45, 200, 134],
64
- [67, 123, 56],
65
- [123, 45, 67],
66
- [90, 32, 210],
67
- [200, 45, 78],
68
- [32, 210, 90],
69
- [45, 123, 67],
70
- [165, 42, 87],
71
- [72, 145, 167],
72
- [15, 158, 75],
73
- [209, 89, 40],
74
- [32, 21, 121],
75
- [184, 20, 100],
76
- [56, 135, 15],
77
- [128, 92, 176],
78
- [1, 119, 140],
79
- [220, 151, 43],
80
- [41, 97, 72],
81
- [148, 38, 27],
82
- [107, 86, 176],
83
- [21, 26, 136],
84
- [174, 27, 90],
85
- [91, 96, 204],
86
- [108, 50, 107],
87
- [27, 45, 136],
88
- [168, 200, 52],
89
- [7, 102, 27],
90
- [42, 93, 56],
91
- [140, 52, 112],
92
- [92, 107, 168],
93
- [17, 118, 176],
94
- [59, 50, 174],
95
- [206, 40, 143],
96
- [44, 19, 142],
97
- [23, 168, 75],
98
- [54, 57, 189],
99
- [144, 21, 15],
100
- [15, 176, 35],
101
- [107, 19, 79],
102
- [204, 52, 114],
103
- [48, 173, 83],
104
- [11, 120, 53],
105
- [206, 104, 28],
106
- [20, 31, 153],
107
- [27, 21, 93],
108
- [11, 206, 138],
109
- [112, 30, 83],
110
- [68, 91, 152],
111
- [153, 13, 43],
112
- [25, 114, 54],
113
- [92, 27, 150],
114
- [108, 42, 59],
115
- [194, 77, 5],
116
- [145, 48, 83],
117
- [7, 113, 19],
118
- [25, 92, 113],
119
- [60, 168, 79],
120
- [78, 33, 120],
121
- [89, 176, 205],
122
- [27, 200, 94],
123
- [210, 67, 23],
124
- [123, 89, 189],
125
- [225, 56, 112],
126
- [75, 156, 45],
127
- [172, 104, 200],
128
- [15, 170, 197],
129
- [240, 133, 65],
130
- [89, 156, 112],
131
- [214, 88, 57],
132
- [156, 134, 200],
133
- [78, 57, 189],
134
- [200, 78, 123],
135
- [106, 120, 210],
136
- [145, 56, 112],
137
- [89, 120, 189],
138
- [185, 206, 56],
139
- [47, 99, 28],
140
- [112, 189, 78],
141
- [200, 112, 89],
142
- [89, 145, 112],
143
- [78, 106, 189],
144
- [112, 78, 189],
145
- [156, 112, 78],
146
- [28, 210, 99],
147
- [78, 89, 189],
148
- [189, 78, 57],
149
- [112, 200, 78],
150
- [189, 47, 78],
151
- [205, 112, 57],
152
- [78, 145, 57],
153
- [200, 78, 112],
154
- [99, 89, 145],
155
- [200, 156, 78],
156
- [57, 78, 145],
157
- [78, 57, 99],
158
- [57, 78, 145],
159
- [145, 112, 78],
160
- [78, 89, 145],
161
- [210, 99, 28],
162
- [145, 78, 189],
163
- [57, 200, 136],
164
- [89, 156, 78],
165
- [145, 78, 99],
166
- [99, 28, 210],
167
- [189, 78, 47],
168
- [28, 210, 99],
169
- [78, 145, 57],
170
  ]
171
 
172
  labels_list = []
 
8
  from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
9
 
10
  feature_extractor = SegformerFeatureExtractor.from_pretrained(
11
+ "mattmdjaga/segformer_b2_clothes"
12
  )
13
  model = TFSegformerForSemanticSegmentation.from_pretrained(
14
+ "mattmdjaga/segformer_b2_clothes"
15
  )
16
 
17
  def ade_palette():
18
  """ADE20K palette that maps each class to RGB values."""
19
  return [
20
+ [255, 0, 0], # Class 0
21
+ [0, 255, 0], # Class 1
22
+ [0, 0, 255], # Class 2
23
+ [255, 255, 0], # Class 3
24
+ [255, 0, 255], # Class 4
25
+ [0, 255, 255], # Class 5
26
+ [128, 0, 0], # Class 6
27
+ [0, 128, 0], # Class 7
28
+ [0, 0, 128], # Class 8
29
+ [128, 128, 0], # Class 9
30
+ [128, 0, 128], # Class 10
31
+ [0, 128, 128], # Class 11
32
+ [64, 0, 0], # Class 12
33
+ [0, 64, 0], # Class 13
34
+ [0, 0, 64], # Class 14
35
+ [64, 64, 0], # Class 15
36
+ [64, 0, 64], # Class 16
37
+ [0, 64, 64], # Class 17
38
+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
39
  ]
40
 
41
  labels_list = []
labels.txt CHANGED
@@ -1,150 +1,18 @@
1
- wall
2
- building
3
- sky
4
- floor
5
- tree
6
- ceiling
7
- road
8
- bed
9
- windowpane
10
- grass
11
- cabinet
12
- sidewalk
13
- person
14
- earth
15
- door
16
- table
17
- mountain
18
- plant
19
- curtain
20
- chair
21
- car
22
- water
23
- painting
24
- sofa
25
- shelf
26
- house
27
- sea
28
- mirror
29
- rug
30
- field
31
- armchair
32
- seat
33
- fence
34
- desk
35
- rock
36
- wardrobe
37
- lamp
38
- bathtub
39
- railing
40
- cushion
41
- base
42
- box
43
- column
44
- signboard
45
- chest of drawers
46
- counter
47
- sand
48
- sink
49
- skyscraper
50
- fireplace
51
- refrigerator
52
- grandstand
53
- path
54
- stairs
55
- runway
56
- case
57
- pool table
58
- pillow
59
- screen door
60
- stairway
61
- river
62
- bridge
63
- bookcase
64
- blind
65
- coffee table
66
- toilet
67
- flower
68
- book
69
- hill
70
- bench
71
- countertop
72
- stove
73
- palm
74
- kitchen island
75
- computer
76
- swivel chair
77
- boat
78
- bar
79
- arcade machine
80
- hovel
81
- bus
82
- towel
83
- light
84
- truck
85
- tower
86
- chandelier
87
- awning
88
- streetlight
89
- booth
90
- television receiver
91
- airplane
92
- dirt track
93
- apparel
94
- pole
95
- land
96
- bannister
97
- escalator
98
- ottoman
99
- bottle
100
- buffet
101
- poster
102
- stage
103
- van
104
- ship
105
- fountain
106
- conveyer belt
107
- canopy
108
- washer
109
- plaything
110
- swimming pool
111
- stool
112
- barrel
113
- basket
114
- waterfall
115
- tent
116
- bag
117
- minibike
118
- cradle
119
- oven
120
- ball
121
- food
122
- step
123
- tank
124
- trade name
125
- microwave
126
- pot
127
- animal
128
- bicycle
129
- lake
130
- dishwasher
131
- screen
132
- blanket
133
- sculpture
134
- hood
135
- sconce
136
- vase
137
- traffic light
138
- tray
139
- ashcan
140
- fan
141
- pier
142
- crt screen
143
- plate
144
- monitor
145
- bulletin board
146
- shower
147
- radiator
148
- glass
149
- clock
150
- flag
 
1
+ Background
2
+ Hat
3
+ Hair
4
+ Sunglasses
5
+ Upper-clothes
6
+ Skirt
7
+ Pants
8
+ Dress
9
+ Belt
10
+ Left-shoe
11
+ Right-shoe
12
+ Face
13
+ Left-leg
14
+ Right-leg
15
+ Left-arm
16
+ Right-arm
17
+ Bag
18
+ Scarf