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ADE_val_00000001.jpeg ADDED
ADE_val_00001159.jpg ADDED
ADE_val_00001248.jpg ADDED
ADE_val_00001472.jpg ADDED
README.md CHANGED
@@ -1,10 +1,10 @@
1
  ---
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- title: GradioML2
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- emoji: ⚑
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- colorFrom: blue
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- colorTo: red
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  sdk: gradio
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- sdk_version: 4.2.0
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  app_file: app.py
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  pinned: false
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  ---
 
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  ---
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+ title: Segmentation
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+ emoji: πŸ‘€
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+ colorFrom: red
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+ colorTo: blue
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  sdk: gradio
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+ sdk_version: 3.44.4
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  app_file: app.py
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  pinned: false
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  ---
app.py CHANGED
@@ -8,167 +8,165 @@ import tensorflow as tf
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  from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
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  feature_extractor = SegformerFeatureExtractor.from_pretrained(
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- # "nvidia/segformer-b5-finetuned-ade-640-640"
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- "nvidia/segformer-b0-finetuned-ade-512-512"
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  )
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  model = TFSegformerForSemanticSegmentation.from_pretrained(
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- # "nvidia/segformer-b5-finetuned-ade-640-640"
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- "nvidia/segformer-b0-finetuned-ade-512-512"
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  )
18
 
19
  def ade_palette():
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  """ADE20K palette that maps each class to RGB values."""
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  return [
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- [78, 145, 57],
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- [200, 78, 112],
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- [99, 89, 145],
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- [200, 156, 78],
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- [57, 78, 145],
27
- [78, 57, 99],
28
- [145, 112, 78],
29
- [78, 89, 145],
30
- [210, 99, 28],
31
- [145, 78, 189],
32
- [57, 200, 136],
33
- [89, 156, 78],
34
- [99, 28, 210],
35
- [189, 78, 47],
36
- [28, 210, 99],
37
- [200, 78, 112],
38
- [210, 99, 28],
39
- [78, 145, 57],
40
- [145, 78, 99],
41
- [78, 57, 99],
42
- [78, 145, 57],
43
- [99, 28, 210],
44
- [99, 89, 145],
45
- [145, 78, 99],
46
- [145, 112, 78],
47
- [78, 89, 145],
48
- [57, 78, 145],
49
- [57, 200, 136],
50
- [57, 78, 145],
51
- [99, 28, 210],
52
- [89, 156, 78],
53
- [57, 78, 145],
54
- [78, 89, 145],
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- [145, 112, 78],
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- [200, 156, 78],
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- [57, 200, 136],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58
  [28, 210, 99],
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- [78, 57, 99],
 
 
 
 
60
  [78, 145, 57],
61
- [57, 200, 136],
62
- [57, 78, 145],
63
- [210, 99, 28],
64
- [145, 78, 189],
65
  [200, 78, 112],
66
- [78, 89, 145],
67
- [99, 28, 210],
68
- [189, 78, 47],
69
  [99, 89, 145],
70
- [78, 145, 57],
71
  [200, 156, 78],
72
  [57, 78, 145],
73
- [210, 99, 28],
74
- [145, 78, 189],
75
- [78, 89, 145],
76
- [200, 78, 112],
77
- [57, 78, 145],
78
- [145, 112, 78],
79
- [99, 28, 210],
80
- [57, 200, 136],
81
  [78, 57, 99],
82
- [28, 210, 99],
83
- [189, 78, 47],
84
- [145, 78, 189],
85
- [78, 57, 99],
86
- [99, 28, 210],
87
- [57, 200, 136],
88
- [145, 112, 78],
89
- [78, 89, 145],
90
- [200, 78, 112],
91
- [78, 57, 99],
92
- [99, 28, 210],
93
- [145, 78, 99],
94
- [28, 210, 99],
95
- [145, 112, 78],
96
- [78, 89, 145],
97
- [57, 200, 136],
98
  [57, 78, 145],
99
- [189, 78, 47],
100
- [200, 156, 78],
101
- [99, 28, 210],
102
  [78, 89, 145],
103
- [145, 78, 189],
104
- [57, 78, 145],
105
- [200, 78, 112],
106
- [78, 57, 99],
107
- [99, 89, 145],
108
  [210, 99, 28],
109
  [145, 78, 189],
110
- [28, 210, 99],
111
- [145, 112, 78],
112
- [57, 200, 136],
113
- [78, 57, 99],
114
- [78, 145, 57],
115
- [99, 28, 210],
116
- [200, 156, 78],
117
- [57, 78, 145],
118
- [145, 78, 99],
119
- [78, 89, 145],
120
  [57, 200, 136],
121
- [28, 210, 99],
122
- [99, 89, 145],
123
- [78, 145, 57],
124
  [145, 78, 99],
125
- [200, 78, 112],
126
- [78, 57, 99],
127
- [210, 99, 28],
128
- [57, 78, 145],
129
- [200, 156, 78],
130
  [99, 28, 210],
131
  [189, 78, 47],
132
- [78, 89, 145],
133
- [57, 200, 136],
134
- [145, 112, 78],
135
- [145, 78, 189],
136
  [28, 210, 99],
137
- [99, 89, 145],
138
- [78, 57, 99],
139
- [57, 200, 136],
140
- [210, 99, 28],
141
- [145, 112, 78],
142
  [78, 145, 57],
143
- [78, 89, 145],
144
- [57, 78, 145],
145
- [200, 78, 112],
146
- [189, 78, 47],
147
- [200, 156, 78],
148
- [57, 200, 136],
149
- [99, 89, 145],
150
- [99, 28, 210],
151
- [145, 112, 78],
152
- [145, 78, 99],
153
- [57, 78, 145],
154
- [28, 210, 99],
155
- [78, 57, 99],
156
- [78, 145, 57],
157
- [57, 200, 136],
158
- [78, 89, 145],
159
- [99, 28, 210],
160
- [200, 156, 78],
161
- [145, 78, 189],
162
- [78, 57, 99],
163
- [57, 78, 145],
164
- [210, 99, 28],
165
- [99, 89, 145],
166
- [28, 210, 99],
167
- [145, 112, 78],
168
- [200, 78, 112],
169
- [78, 57, 99],
170
- [57, 78, 145],
171
- [99, 28, 210],
172
  ]
173
 
174
  labels_list = []
@@ -237,7 +235,7 @@ def sepia(input_img):
237
  demo = gr.Interface(fn=sepia,
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  inputs=gr.Image(shape=(400, 600)),
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  outputs=['plot'],
240
- examples=["sample-1.jpg", "sample-2.jpg", "sample-3.png", ],
241
  allow_flagging='never')
242
 
243
 
 
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(
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+ "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],
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+ [112, 185, 212],
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+ [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 = []
 
235
  demo = gr.Interface(fn=sepia,
236
  inputs=gr.Image(shape=(400, 600)),
237
  outputs=['plot'],
238
+ examples=["ADE_val_00000001.jpeg", "ADE_val_00001159.jpg", "ADE_val_00001248.jpg", "ADE_val_00001472.jpg"],
239
  allow_flagging='never')
240
 
241