EgorGrinevich commited on
Commit
c6d8a28
1 Parent(s): 856131a

Training in progress epoch 0

Browse files
Files changed (4) hide show
  1. README.md +357 -0
  2. config.json +374 -0
  3. preprocessor_config.json +23 -0
  4. tf_model.h5 +3 -0
README.md ADDED
@@ -0,0 +1,357 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ base_model: nvidia/mit-b0
4
+ tags:
5
+ - generated_from_keras_callback
6
+ model-index:
7
+ - name: EgorGrinevich/scene_segmentation
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information Keras had access to. You should
12
+ probably proofread and complete it, then remove this comment. -->
13
+
14
+ # EgorGrinevich/scene_segmentation
15
+
16
+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Train Loss: nan
19
+ - Validation Loss: nan
20
+ - Validation Mean Iou: 0.0024
21
+ - Validation Mean Accuracy: 0.0222
22
+ - Validation Overall Accuracy: 0.1310
23
+ - Validation Accuracy Wall: 1.0
24
+ - Validation Accuracy Building: 0.0
25
+ - Validation Accuracy Sky: 0.0
26
+ - Validation Accuracy Floor: 0.0
27
+ - Validation Accuracy Tree: 0.0
28
+ - Validation Accuracy Ceiling: 0.0
29
+ - Validation Accuracy Road: 0.0
30
+ - Validation Accuracy Bed : 0.0
31
+ - Validation Accuracy Windowpane: nan
32
+ - Validation Accuracy Grass: 0.0
33
+ - Validation Accuracy Cabinet: 0.0
34
+ - Validation Accuracy Sidewalk: 0.0
35
+ - Validation Accuracy Person: 0.0
36
+ - Validation Accuracy Earth: 0.0
37
+ - Validation Accuracy Door: 0.0
38
+ - Validation Accuracy Table: 0.0
39
+ - Validation Accuracy Mountain: nan
40
+ - Validation Accuracy Plant: 0.0
41
+ - Validation Accuracy Curtain: nan
42
+ - Validation Accuracy Chair: 0.0
43
+ - Validation Accuracy Car: 0.0
44
+ - Validation Accuracy Water: 0.0
45
+ - Validation Accuracy Painting: nan
46
+ - Validation Accuracy Sofa: nan
47
+ - Validation Accuracy Shelf: nan
48
+ - Validation Accuracy House: nan
49
+ - Validation Accuracy Sea: 0.0
50
+ - Validation Accuracy Mirror: nan
51
+ - Validation Accuracy Rug: nan
52
+ - Validation Accuracy Field: 0.0
53
+ - Validation Accuracy Armchair: nan
54
+ - Validation Accuracy Seat: 0.0
55
+ - Validation Accuracy Fence: nan
56
+ - Validation Accuracy Desk: 0.0
57
+ - Validation Accuracy Rock: nan
58
+ - Validation Accuracy Wardrobe: 0.0
59
+ - Validation Accuracy Lamp: nan
60
+ - Validation Accuracy Bathtub: nan
61
+ - Validation Accuracy Railing: 0.0
62
+ - Validation Accuracy Cushion: nan
63
+ - Validation Accuracy Base: nan
64
+ - Validation Accuracy Box: nan
65
+ - Validation Accuracy Column: nan
66
+ - Validation Accuracy Signboard: 0.0
67
+ - Validation Accuracy Chest of drawers: nan
68
+ - Validation Accuracy Counter: nan
69
+ - Validation Accuracy Sand: 0.0
70
+ - Validation Accuracy Sink: nan
71
+ - Validation Accuracy Skyscraper: nan
72
+ - Validation Accuracy Fireplace: 0.0
73
+ - Validation Accuracy Refrigerator: nan
74
+ - Validation Accuracy Grandstand: nan
75
+ - Validation Accuracy Path: nan
76
+ - Validation Accuracy Stairs: nan
77
+ - Validation Accuracy Runway: nan
78
+ - Validation Accuracy Case: nan
79
+ - Validation Accuracy Pool table: 0.0
80
+ - Validation Accuracy Pillow: nan
81
+ - Validation Accuracy Screen door: nan
82
+ - Validation Accuracy Stairway: nan
83
+ - Validation Accuracy River: nan
84
+ - Validation Accuracy Bridge: nan
85
+ - Validation Accuracy Bookcase: nan
86
+ - Validation Accuracy Blind: 0.0
87
+ - Validation Accuracy Coffee table: nan
88
+ - Validation Accuracy Toilet: nan
89
+ - Validation Accuracy Flower: nan
90
+ - Validation Accuracy Book: 0.0
91
+ - Validation Accuracy Hill: nan
92
+ - Validation Accuracy Bench: nan
93
+ - Validation Accuracy Countertop: 0.0
94
+ - Validation Accuracy Stove: nan
95
+ - Validation Accuracy Palm: nan
96
+ - Validation Accuracy Kitchen island: nan
97
+ - Validation Accuracy Computer: nan
98
+ - Validation Accuracy Swivel chair: nan
99
+ - Validation Accuracy Boat: nan
100
+ - Validation Accuracy Bar: nan
101
+ - Validation Accuracy Arcade machine: nan
102
+ - Validation Accuracy Hovel: nan
103
+ - Validation Accuracy Bus: 0.0
104
+ - Validation Accuracy Towel: 0.0
105
+ - Validation Accuracy Light: 0.0
106
+ - Validation Accuracy Truck: nan
107
+ - Validation Accuracy Tower: nan
108
+ - Validation Accuracy Chandelier: nan
109
+ - Validation Accuracy Awning: nan
110
+ - Validation Accuracy Streetlight: nan
111
+ - Validation Accuracy Booth: nan
112
+ - Validation Accuracy Television receiver: nan
113
+ - Validation Accuracy Airplane: 0.0
114
+ - Validation Accuracy Dirt track: nan
115
+ - Validation Accuracy Apparel: 0.0
116
+ - Validation Accuracy Pole: nan
117
+ - Validation Accuracy Land: nan
118
+ - Validation Accuracy Bannister: nan
119
+ - Validation Accuracy Escalator: nan
120
+ - Validation Accuracy Ottoman: nan
121
+ - Validation Accuracy Bottle: nan
122
+ - Validation Accuracy Buffet: nan
123
+ - Validation Accuracy Poster: nan
124
+ - Validation Accuracy Stage: nan
125
+ - Validation Accuracy Van: nan
126
+ - Validation Accuracy Ship: nan
127
+ - Validation Accuracy Fountain: nan
128
+ - Validation Accuracy Conveyer belt: nan
129
+ - Validation Accuracy Canopy: nan
130
+ - Validation Accuracy Washer: nan
131
+ - Validation Accuracy Plaything: nan
132
+ - Validation Accuracy Swimming pool: 0.0
133
+ - Validation Accuracy Stool: nan
134
+ - Validation Accuracy Barrel: nan
135
+ - Validation Accuracy Basket: 0.0
136
+ - Validation Accuracy Waterfall: nan
137
+ - Validation Accuracy Tent: 0.0
138
+ - Validation Accuracy Bag: nan
139
+ - Validation Accuracy Minibike: nan
140
+ - Validation Accuracy Cradle: nan
141
+ - Validation Accuracy Oven: nan
142
+ - Validation Accuracy Ball: nan
143
+ - Validation Accuracy Food: nan
144
+ - Validation Accuracy Step: nan
145
+ - Validation Accuracy Tank: nan
146
+ - Validation Accuracy Trade name: 0.0
147
+ - Validation Accuracy Microwave: nan
148
+ - Validation Accuracy Pot: nan
149
+ - Validation Accuracy Animal: nan
150
+ - Validation Accuracy Bicycle: nan
151
+ - Validation Accuracy Lake: 0.0
152
+ - Validation Accuracy Dishwasher: nan
153
+ - Validation Accuracy Screen: nan
154
+ - Validation Accuracy Blanket: nan
155
+ - Validation Accuracy Sculpture: nan
156
+ - Validation Accuracy Hood: 0.0
157
+ - Validation Accuracy Sconce: nan
158
+ - Validation Accuracy Vase: 0.0
159
+ - Validation Accuracy Traffic light: nan
160
+ - Validation Accuracy Tray: 0.0
161
+ - Validation Accuracy Ashcan: nan
162
+ - Validation Accuracy Fan: nan
163
+ - Validation Accuracy Pier: nan
164
+ - Validation Accuracy Crt screen: nan
165
+ - Validation Accuracy Plate: nan
166
+ - Validation Accuracy Monitor: nan
167
+ - Validation Accuracy Bulletin board: nan
168
+ - Validation Accuracy Shower: nan
169
+ - Validation Accuracy Radiator: nan
170
+ - Validation Accuracy Glass: nan
171
+ - Validation Accuracy Clock: nan
172
+ - Validation Accuracy Flag: nan
173
+ - Validation Iou Wall: 0.1065
174
+ - Validation Iou Building: 0.0
175
+ - Validation Iou Sky: 0.0
176
+ - Validation Iou Floor: 0.0
177
+ - Validation Iou Tree: 0.0
178
+ - Validation Iou Ceiling: 0.0
179
+ - Validation Iou Road: 0.0
180
+ - Validation Iou Bed : 0.0
181
+ - Validation Iou Windowpane: nan
182
+ - Validation Iou Grass: 0.0
183
+ - Validation Iou Cabinet: 0.0
184
+ - Validation Iou Sidewalk: 0.0
185
+ - Validation Iou Person: 0.0
186
+ - Validation Iou Earth: 0.0
187
+ - Validation Iou Door: 0.0
188
+ - Validation Iou Table: 0.0
189
+ - Validation Iou Mountain: nan
190
+ - Validation Iou Plant: 0.0
191
+ - Validation Iou Curtain: nan
192
+ - Validation Iou Chair: 0.0
193
+ - Validation Iou Car: 0.0
194
+ - Validation Iou Water: 0.0
195
+ - Validation Iou Painting: nan
196
+ - Validation Iou Sofa: nan
197
+ - Validation Iou Shelf: nan
198
+ - Validation Iou House: nan
199
+ - Validation Iou Sea: 0.0
200
+ - Validation Iou Mirror: nan
201
+ - Validation Iou Rug: nan
202
+ - Validation Iou Field: 0.0
203
+ - Validation Iou Armchair: nan
204
+ - Validation Iou Seat: 0.0
205
+ - Validation Iou Fence: nan
206
+ - Validation Iou Desk: 0.0
207
+ - Validation Iou Rock: nan
208
+ - Validation Iou Wardrobe: 0.0
209
+ - Validation Iou Lamp: nan
210
+ - Validation Iou Bathtub: nan
211
+ - Validation Iou Railing: 0.0
212
+ - Validation Iou Cushion: nan
213
+ - Validation Iou Base: nan
214
+ - Validation Iou Box: nan
215
+ - Validation Iou Column: nan
216
+ - Validation Iou Signboard: 0.0
217
+ - Validation Iou Chest of drawers: nan
218
+ - Validation Iou Counter: nan
219
+ - Validation Iou Sand: 0.0
220
+ - Validation Iou Sink: nan
221
+ - Validation Iou Skyscraper: nan
222
+ - Validation Iou Fireplace: 0.0
223
+ - Validation Iou Refrigerator: nan
224
+ - Validation Iou Grandstand: nan
225
+ - Validation Iou Path: nan
226
+ - Validation Iou Stairs: nan
227
+ - Validation Iou Runway: nan
228
+ - Validation Iou Case: nan
229
+ - Validation Iou Pool table: 0.0
230
+ - Validation Iou Pillow: nan
231
+ - Validation Iou Screen door: nan
232
+ - Validation Iou Stairway: nan
233
+ - Validation Iou River: nan
234
+ - Validation Iou Bridge: nan
235
+ - Validation Iou Bookcase: nan
236
+ - Validation Iou Blind: 0.0
237
+ - Validation Iou Coffee table: nan
238
+ - Validation Iou Toilet: nan
239
+ - Validation Iou Flower: nan
240
+ - Validation Iou Book: 0.0
241
+ - Validation Iou Hill: nan
242
+ - Validation Iou Bench: nan
243
+ - Validation Iou Countertop: 0.0
244
+ - Validation Iou Stove: nan
245
+ - Validation Iou Palm: nan
246
+ - Validation Iou Kitchen island: nan
247
+ - Validation Iou Computer: nan
248
+ - Validation Iou Swivel chair: nan
249
+ - Validation Iou Boat: nan
250
+ - Validation Iou Bar: nan
251
+ - Validation Iou Arcade machine: nan
252
+ - Validation Iou Hovel: nan
253
+ - Validation Iou Bus: 0.0
254
+ - Validation Iou Towel: 0.0
255
+ - Validation Iou Light: 0.0
256
+ - Validation Iou Truck: nan
257
+ - Validation Iou Tower: nan
258
+ - Validation Iou Chandelier: nan
259
+ - Validation Iou Awning: nan
260
+ - Validation Iou Streetlight: nan
261
+ - Validation Iou Booth: nan
262
+ - Validation Iou Television receiver: nan
263
+ - Validation Iou Airplane: 0.0
264
+ - Validation Iou Dirt track: nan
265
+ - Validation Iou Apparel: 0.0
266
+ - Validation Iou Pole: nan
267
+ - Validation Iou Land: nan
268
+ - Validation Iou Bannister: nan
269
+ - Validation Iou Escalator: nan
270
+ - Validation Iou Ottoman: nan
271
+ - Validation Iou Bottle: nan
272
+ - Validation Iou Buffet: nan
273
+ - Validation Iou Poster: nan
274
+ - Validation Iou Stage: nan
275
+ - Validation Iou Van: nan
276
+ - Validation Iou Ship: nan
277
+ - Validation Iou Fountain: nan
278
+ - Validation Iou Conveyer belt: nan
279
+ - Validation Iou Canopy: nan
280
+ - Validation Iou Washer: nan
281
+ - Validation Iou Plaything: nan
282
+ - Validation Iou Swimming pool: 0.0
283
+ - Validation Iou Stool: nan
284
+ - Validation Iou Barrel: nan
285
+ - Validation Iou Basket: 0.0
286
+ - Validation Iou Waterfall: nan
287
+ - Validation Iou Tent: 0.0
288
+ - Validation Iou Bag: nan
289
+ - Validation Iou Minibike: nan
290
+ - Validation Iou Cradle: nan
291
+ - Validation Iou Oven: nan
292
+ - Validation Iou Ball: nan
293
+ - Validation Iou Food: nan
294
+ - Validation Iou Step: nan
295
+ - Validation Iou Tank: nan
296
+ - Validation Iou Trade name: 0.0
297
+ - Validation Iou Microwave: nan
298
+ - Validation Iou Pot: nan
299
+ - Validation Iou Animal: nan
300
+ - Validation Iou Bicycle: nan
301
+ - Validation Iou Lake: 0.0
302
+ - Validation Iou Dishwasher: nan
303
+ - Validation Iou Screen: nan
304
+ - Validation Iou Blanket: nan
305
+ - Validation Iou Sculpture: nan
306
+ - Validation Iou Hood: 0.0
307
+ - Validation Iou Sconce: nan
308
+ - Validation Iou Vase: 0.0
309
+ - Validation Iou Traffic light: nan
310
+ - Validation Iou Tray: 0.0
311
+ - Validation Iou Ashcan: nan
312
+ - Validation Iou Fan: nan
313
+ - Validation Iou Pier: nan
314
+ - Validation Iou Crt screen: nan
315
+ - Validation Iou Plate: nan
316
+ - Validation Iou Monitor: nan
317
+ - Validation Iou Bulletin board: nan
318
+ - Validation Iou Shower: nan
319
+ - Validation Iou Radiator: nan
320
+ - Validation Iou Glass: nan
321
+ - Validation Iou Clock: nan
322
+ - Validation Iou Flag: nan
323
+ - Epoch: 0
324
+
325
+ ## Model description
326
+
327
+ More information needed
328
+
329
+ ## Intended uses & limitations
330
+
331
+ More information needed
332
+
333
+ ## Training and evaluation data
334
+
335
+ More information needed
336
+
337
+ ## Training procedure
338
+
339
+ ### Training hyperparameters
340
+
341
+ The following hyperparameters were used during training:
342
+ - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 2000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
343
+ - training_precision: float32
344
+
345
+ ### Training results
346
+
347
+ | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Wall | Validation Accuracy Building | Validation Accuracy Sky | Validation Accuracy Floor | Validation Accuracy Tree | Validation Accuracy Ceiling | Validation Accuracy Road | Validation Accuracy Bed | Validation Accuracy Windowpane | Validation Accuracy Grass | Validation Accuracy Cabinet | Validation Accuracy Sidewalk | Validation Accuracy Person | Validation Accuracy Earth | Validation Accuracy Door | Validation Accuracy Table | Validation Accuracy Mountain | Validation Accuracy Plant | Validation Accuracy Curtain | Validation Accuracy Chair | Validation Accuracy Car | Validation Accuracy Water | Validation Accuracy Painting | Validation Accuracy Sofa | Validation Accuracy Shelf | Validation Accuracy House | Validation Accuracy Sea | Validation Accuracy Mirror | Validation Accuracy Rug | Validation Accuracy Field | Validation Accuracy Armchair | Validation Accuracy Seat | Validation Accuracy Fence | Validation Accuracy Desk | Validation Accuracy Rock | Validation Accuracy Wardrobe | Validation Accuracy Lamp | Validation Accuracy Bathtub | Validation Accuracy Railing | Validation Accuracy Cushion | Validation Accuracy Base | Validation Accuracy Box | Validation Accuracy Column | Validation Accuracy Signboard | Validation Accuracy Chest of drawers | Validation Accuracy Counter | Validation Accuracy Sand | Validation Accuracy Sink | Validation Accuracy Skyscraper | Validation Accuracy Fireplace | Validation Accuracy Refrigerator | Validation Accuracy Grandstand | Validation Accuracy Path | Validation Accuracy Stairs | Validation Accuracy Runway | Validation Accuracy Case | Validation Accuracy Pool table | Validation Accuracy Pillow | Validation Accuracy Screen door | Validation Accuracy Stairway | Validation Accuracy River | Validation Accuracy Bridge | Validation Accuracy Bookcase | Validation Accuracy Blind | Validation Accuracy Coffee table | Validation Accuracy Toilet | Validation Accuracy Flower | Validation Accuracy Book | Validation Accuracy Hill | Validation Accuracy Bench | Validation Accuracy Countertop | Validation Accuracy Stove | Validation Accuracy Palm | Validation Accuracy Kitchen island | Validation Accuracy Computer | Validation Accuracy Swivel chair | Validation Accuracy Boat | Validation Accuracy Bar | Validation Accuracy Arcade machine | Validation Accuracy Hovel | Validation Accuracy Bus | Validation Accuracy Towel | Validation Accuracy Light | Validation Accuracy Truck | Validation Accuracy Tower | Validation Accuracy Chandelier | Validation Accuracy Awning | Validation Accuracy Streetlight | Validation Accuracy Booth | Validation Accuracy Television receiver | Validation Accuracy Airplane | Validation Accuracy Dirt track | Validation Accuracy Apparel | Validation Accuracy Pole | Validation Accuracy Land | Validation Accuracy Bannister | Validation Accuracy Escalator | Validation Accuracy Ottoman | Validation Accuracy Bottle | Validation Accuracy Buffet | Validation Accuracy Poster | Validation Accuracy Stage | Validation Accuracy Van | Validation Accuracy Ship | Validation Accuracy Fountain | Validation Accuracy Conveyer belt | Validation Accuracy Canopy | Validation Accuracy Washer | Validation Accuracy Plaything | Validation Accuracy Swimming pool | Validation Accuracy Stool | Validation Accuracy Barrel | Validation Accuracy Basket | Validation Accuracy Waterfall | Validation Accuracy Tent | Validation Accuracy Bag | Validation Accuracy Minibike | Validation Accuracy Cradle | Validation Accuracy Oven | Validation Accuracy Ball | Validation Accuracy Food | Validation Accuracy Step | Validation Accuracy Tank | Validation Accuracy Trade name | Validation Accuracy Microwave | Validation Accuracy Pot | Validation Accuracy Animal | Validation Accuracy Bicycle | Validation Accuracy Lake | Validation Accuracy Dishwasher | Validation Accuracy Screen | Validation Accuracy Blanket | Validation Accuracy Sculpture | Validation Accuracy Hood | Validation Accuracy Sconce | Validation Accuracy Vase | Validation Accuracy Traffic light | Validation Accuracy Tray | Validation Accuracy Ashcan | Validation Accuracy Fan | Validation Accuracy Pier | Validation Accuracy Crt screen | Validation Accuracy Plate | Validation Accuracy Monitor | Validation Accuracy Bulletin board | Validation Accuracy Shower | Validation Accuracy Radiator | Validation Accuracy Glass | Validation Accuracy Clock | Validation Accuracy Flag | Validation Iou Wall | Validation Iou Building | Validation Iou Sky | Validation Iou Floor | Validation Iou Tree | Validation Iou Ceiling | Validation Iou Road | Validation Iou Bed | Validation Iou Windowpane | Validation Iou Grass | Validation Iou Cabinet | Validation Iou Sidewalk | Validation Iou Person | Validation Iou Earth | Validation Iou Door | Validation Iou Table | Validation Iou Mountain | Validation Iou Plant | Validation Iou Curtain | Validation Iou Chair | Validation Iou Car | Validation Iou Water | Validation Iou Painting | Validation Iou Sofa | Validation Iou Shelf | Validation Iou House | Validation Iou Sea | Validation Iou Mirror | Validation Iou Rug | Validation Iou Field | Validation Iou Armchair | Validation Iou Seat | Validation Iou Fence | Validation Iou Desk | Validation Iou Rock | Validation Iou Wardrobe | Validation Iou Lamp | Validation Iou Bathtub | Validation Iou Railing | Validation Iou Cushion | Validation Iou Base | Validation Iou Box | Validation Iou Column | Validation Iou Signboard | Validation Iou Chest of drawers | Validation Iou Counter | Validation Iou Sand | Validation Iou Sink | Validation Iou Skyscraper | Validation Iou Fireplace | Validation Iou Refrigerator | Validation Iou Grandstand | Validation Iou Path | Validation Iou Stairs | Validation Iou Runway | Validation Iou Case | Validation Iou Pool table | Validation Iou Pillow | Validation Iou Screen door | Validation Iou Stairway | Validation Iou River | Validation Iou Bridge | Validation Iou Bookcase | Validation Iou Blind | Validation Iou Coffee table | Validation Iou Toilet | Validation Iou Flower | Validation Iou Book | Validation Iou Hill | Validation Iou Bench | Validation Iou Countertop | Validation Iou Stove | Validation Iou Palm | Validation Iou Kitchen island | Validation Iou Computer | Validation Iou Swivel chair | Validation Iou Boat | Validation Iou Bar | Validation Iou Arcade machine | Validation Iou Hovel | Validation Iou Bus | Validation Iou Towel | Validation Iou Light | Validation Iou Truck | Validation Iou Tower | Validation Iou Chandelier | Validation Iou Awning | Validation Iou Streetlight | Validation Iou Booth | Validation Iou Television receiver | Validation Iou Airplane | Validation Iou Dirt track | Validation Iou Apparel | Validation Iou Pole | Validation Iou Land | Validation Iou Bannister | Validation Iou Escalator | Validation Iou Ottoman | Validation Iou Bottle | Validation Iou Buffet | Validation Iou Poster | Validation Iou Stage | Validation Iou Van | Validation Iou Ship | Validation Iou Fountain | Validation Iou Conveyer belt | Validation Iou Canopy | Validation Iou Washer | Validation Iou Plaything | Validation Iou Swimming pool | Validation Iou Stool | Validation Iou Barrel | Validation Iou Basket | Validation Iou Waterfall | Validation Iou Tent | Validation Iou Bag | Validation Iou Minibike | Validation Iou Cradle | Validation Iou Oven | Validation Iou Ball | Validation Iou Food | Validation Iou Step | Validation Iou Tank | Validation Iou Trade name | Validation Iou Microwave | Validation Iou Pot | Validation Iou Animal | Validation Iou Bicycle | Validation Iou Lake | Validation Iou Dishwasher | Validation Iou Screen | Validation Iou Blanket | Validation Iou Sculpture | Validation Iou Hood | Validation Iou Sconce | Validation Iou Vase | Validation Iou Traffic light | Validation Iou Tray | Validation Iou Ashcan | Validation Iou Fan | Validation Iou Pier | Validation Iou Crt screen | Validation Iou Plate | Validation Iou Monitor | Validation Iou Bulletin board | Validation Iou Shower | Validation Iou Radiator | Validation Iou Glass | Validation Iou Clock | Validation Iou Flag | Epoch |
348
+ |:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:------------------------:|:----------------------------:|:-----------------------:|:-------------------------:|:------------------------:|:---------------------------:|:------------------------:|:------------------------:|:------------------------------:|:-------------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-------------------------:|:------------------------:|:-------------------------:|:----------------------------:|:-------------------------:|:---------------------------:|:-------------------------:|:-----------------------:|:-------------------------:|:----------------------------:|:------------------------:|:-------------------------:|:-------------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------------:|:----------------------------:|:------------------------:|:-------------------------:|:------------------------:|:------------------------:|:----------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:------------------------:|:-----------------------:|:--------------------------:|:-----------------------------:|:------------------------------------:|:---------------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------------:|:--------------------------------:|:------------------------------:|:------------------------:|:--------------------------:|:--------------------------:|:------------------------:|:------------------------------:|:--------------------------:|:-------------------------------:|:----------------------------:|:-------------------------:|:--------------------------:|:----------------------------:|:-------------------------:|:--------------------------------:|:--------------------------:|:--------------------------:|:------------------------:|:------------------------:|:-------------------------:|:------------------------------:|:-------------------------:|:------------------------:|:----------------------------------:|:----------------------------:|:--------------------------------:|:------------------------:|:-----------------------:|:----------------------------------:|:-------------------------:|:-----------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:------------------------------:|:--------------------------:|:-------------------------------:|:-------------------------:|:---------------------------------------:|:----------------------------:|:------------------------------:|:---------------------------:|:------------------------:|:------------------------:|:-----------------------------:|:-----------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------:|:-----------------------:|:------------------------:|:----------------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:---------------------------------:|:-------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:------------------------:|:-----------------------:|:----------------------------:|:--------------------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------------:|:-----------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------------:|:--------------------------:|:---------------------------:|:-----------------------------:|:------------------------:|:--------------------------:|:------------------------:|:---------------------------------:|:------------------------:|:--------------------------:|:-----------------------:|:------------------------:|:------------------------------:|:-------------------------:|:---------------------------:|:----------------------------------:|:--------------------------:|:----------------------------:|:-------------------------:|:-------------------------:|:------------------------:|:-------------------:|:-----------------------:|:------------------:|:--------------------:|:-------------------:|:----------------------:|:-------------------:|:-------------------:|:-------------------------:|:--------------------:|:----------------------:|:-----------------------:|:---------------------:|:--------------------:|:-------------------:|:--------------------:|:-----------------------:|:--------------------:|:----------------------:|:--------------------:|:------------------:|:--------------------:|:-----------------------:|:-------------------:|:--------------------:|:--------------------:|:------------------:|:---------------------:|:------------------:|:--------------------:|:-----------------------:|:-------------------:|:--------------------:|:-------------------:|:-------------------:|:-----------------------:|:-------------------:|:----------------------:|:----------------------:|:----------------------:|:-------------------:|:------------------:|:---------------------:|:------------------------:|:-------------------------------:|:----------------------:|:-------------------:|:-------------------:|:-------------------------:|:------------------------:|:---------------------------:|:-------------------------:|:-------------------:|:---------------------:|:---------------------:|:-------------------:|:-------------------------:|:---------------------:|:--------------------------:|:-----------------------:|:--------------------:|:---------------------:|:-----------------------:|:--------------------:|:---------------------------:|:---------------------:|:---------------------:|:-------------------:|:-------------------:|:--------------------:|:-------------------------:|:--------------------:|:-------------------:|:-----------------------------:|:-----------------------:|:---------------------------:|:-------------------:|:------------------:|:-----------------------------:|:--------------------:|:------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:-------------------------:|:---------------------:|:--------------------------:|:--------------------:|:----------------------------------:|:-----------------------:|:-------------------------:|:----------------------:|:-------------------:|:-------------------:|:------------------------:|:------------------------:|:----------------------:|:---------------------:|:---------------------:|:---------------------:|:--------------------:|:------------------:|:-------------------:|:-----------------------:|:----------------------------:|:---------------------:|:---------------------:|:------------------------:|:----------------------------:|:--------------------:|:---------------------:|:---------------------:|:------------------------:|:-------------------:|:------------------:|:-----------------------:|:---------------------:|:-------------------:|:-------------------:|:-------------------:|:-------------------:|:-------------------:|:-------------------------:|:------------------------:|:------------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------------:|:---------------------:|:----------------------:|:------------------------:|:-------------------:|:---------------------:|:-------------------:|:----------------------------:|:-------------------:|:---------------------:|:------------------:|:-------------------:|:-------------------------:|:--------------------:|:----------------------:|:-----------------------------:|:---------------------:|:-----------------------:|:--------------------:|:--------------------:|:-------------------:|:-----:|
349
+ | nan | nan | 0.0024 | 0.0222 | 0.1310 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.1065 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | 0.0 | 0.0 | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | nan | nan | nan | 0.0 | nan | 0.0 | nan | 0.0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0 |
350
+
351
+
352
+ ### Framework versions
353
+
354
+ - Transformers 4.35.2
355
+ - TensorFlow 2.14.0
356
+ - Datasets 2.15.0
357
+ - Tokenizers 0.15.0
config.json ADDED
@@ -0,0 +1,374 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "nvidia/mit-b0",
3
+ "architectures": [
4
+ "SegformerForSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
8
+ "decoder_hidden_size": 256,
9
+ "depths": [
10
+ 2,
11
+ 2,
12
+ 2,
13
+ 2
14
+ ],
15
+ "downsampling_rates": [
16
+ 1,
17
+ 4,
18
+ 8,
19
+ 16
20
+ ],
21
+ "drop_path_rate": 0.1,
22
+ "hidden_act": "gelu",
23
+ "hidden_dropout_prob": 0.0,
24
+ "hidden_sizes": [
25
+ 32,
26
+ 64,
27
+ 160,
28
+ 256
29
+ ],
30
+ "id2label": {
31
+ "0": "wall",
32
+ "1": "building",
33
+ "2": "sky",
34
+ "3": "floor",
35
+ "4": "tree",
36
+ "5": "ceiling",
37
+ "6": "road",
38
+ "7": "bed ",
39
+ "8": "windowpane",
40
+ "9": "grass",
41
+ "10": "cabinet",
42
+ "11": "sidewalk",
43
+ "12": "person",
44
+ "13": "earth",
45
+ "14": "door",
46
+ "15": "table",
47
+ "16": "mountain",
48
+ "17": "plant",
49
+ "18": "curtain",
50
+ "19": "chair",
51
+ "20": "car",
52
+ "21": "water",
53
+ "22": "painting",
54
+ "23": "sofa",
55
+ "24": "shelf",
56
+ "25": "house",
57
+ "26": "sea",
58
+ "27": "mirror",
59
+ "28": "rug",
60
+ "29": "field",
61
+ "30": "armchair",
62
+ "31": "seat",
63
+ "32": "fence",
64
+ "33": "desk",
65
+ "34": "rock",
66
+ "35": "wardrobe",
67
+ "36": "lamp",
68
+ "37": "bathtub",
69
+ "38": "railing",
70
+ "39": "cushion",
71
+ "40": "base",
72
+ "41": "box",
73
+ "42": "column",
74
+ "43": "signboard",
75
+ "44": "chest of drawers",
76
+ "45": "counter",
77
+ "46": "sand",
78
+ "47": "sink",
79
+ "48": "skyscraper",
80
+ "49": "fireplace",
81
+ "50": "refrigerator",
82
+ "51": "grandstand",
83
+ "52": "path",
84
+ "53": "stairs",
85
+ "54": "runway",
86
+ "55": "case",
87
+ "56": "pool table",
88
+ "57": "pillow",
89
+ "58": "screen door",
90
+ "59": "stairway",
91
+ "60": "river",
92
+ "61": "bridge",
93
+ "62": "bookcase",
94
+ "63": "blind",
95
+ "64": "coffee table",
96
+ "65": "toilet",
97
+ "66": "flower",
98
+ "67": "book",
99
+ "68": "hill",
100
+ "69": "bench",
101
+ "70": "countertop",
102
+ "71": "stove",
103
+ "72": "palm",
104
+ "73": "kitchen island",
105
+ "74": "computer",
106
+ "75": "swivel chair",
107
+ "76": "boat",
108
+ "77": "bar",
109
+ "78": "arcade machine",
110
+ "79": "hovel",
111
+ "80": "bus",
112
+ "81": "towel",
113
+ "82": "light",
114
+ "83": "truck",
115
+ "84": "tower",
116
+ "85": "chandelier",
117
+ "86": "awning",
118
+ "87": "streetlight",
119
+ "88": "booth",
120
+ "89": "television receiver",
121
+ "90": "airplane",
122
+ "91": "dirt track",
123
+ "92": "apparel",
124
+ "93": "pole",
125
+ "94": "land",
126
+ "95": "bannister",
127
+ "96": "escalator",
128
+ "97": "ottoman",
129
+ "98": "bottle",
130
+ "99": "buffet",
131
+ "100": "poster",
132
+ "101": "stage",
133
+ "102": "van",
134
+ "103": "ship",
135
+ "104": "fountain",
136
+ "105": "conveyer belt",
137
+ "106": "canopy",
138
+ "107": "washer",
139
+ "108": "plaything",
140
+ "109": "swimming pool",
141
+ "110": "stool",
142
+ "111": "barrel",
143
+ "112": "basket",
144
+ "113": "waterfall",
145
+ "114": "tent",
146
+ "115": "bag",
147
+ "116": "minibike",
148
+ "117": "cradle",
149
+ "118": "oven",
150
+ "119": "ball",
151
+ "120": "food",
152
+ "121": "step",
153
+ "122": "tank",
154
+ "123": "trade name",
155
+ "124": "microwave",
156
+ "125": "pot",
157
+ "126": "animal",
158
+ "127": "bicycle",
159
+ "128": "lake",
160
+ "129": "dishwasher",
161
+ "130": "screen",
162
+ "131": "blanket",
163
+ "132": "sculpture",
164
+ "133": "hood",
165
+ "134": "sconce",
166
+ "135": "vase",
167
+ "136": "traffic light",
168
+ "137": "tray",
169
+ "138": "ashcan",
170
+ "139": "fan",
171
+ "140": "pier",
172
+ "141": "crt screen",
173
+ "142": "plate",
174
+ "143": "monitor",
175
+ "144": "bulletin board",
176
+ "145": "shower",
177
+ "146": "radiator",
178
+ "147": "glass",
179
+ "148": "clock",
180
+ "149": "flag"
181
+ },
182
+ "image_size": 224,
183
+ "initializer_range": 0.02,
184
+ "label2id": {
185
+ "airplane": 90,
186
+ "animal": 126,
187
+ "apparel": 92,
188
+ "arcade machine": 78,
189
+ "armchair": 30,
190
+ "ashcan": 138,
191
+ "awning": 86,
192
+ "bag": 115,
193
+ "ball": 119,
194
+ "bannister": 95,
195
+ "bar": 77,
196
+ "barrel": 111,
197
+ "base": 40,
198
+ "basket": 112,
199
+ "bathtub": 37,
200
+ "bed ": 7,
201
+ "bench": 69,
202
+ "bicycle": 127,
203
+ "blanket": 131,
204
+ "blind": 63,
205
+ "boat": 76,
206
+ "book": 67,
207
+ "bookcase": 62,
208
+ "booth": 88,
209
+ "bottle": 98,
210
+ "box": 41,
211
+ "bridge": 61,
212
+ "buffet": 99,
213
+ "building": 1,
214
+ "bulletin board": 144,
215
+ "bus": 80,
216
+ "cabinet": 10,
217
+ "canopy": 106,
218
+ "car": 20,
219
+ "case": 55,
220
+ "ceiling": 5,
221
+ "chair": 19,
222
+ "chandelier": 85,
223
+ "chest of drawers": 44,
224
+ "clock": 148,
225
+ "coffee table": 64,
226
+ "column": 42,
227
+ "computer": 74,
228
+ "conveyer belt": 105,
229
+ "counter": 45,
230
+ "countertop": 70,
231
+ "cradle": 117,
232
+ "crt screen": 141,
233
+ "curtain": 18,
234
+ "cushion": 39,
235
+ "desk": 33,
236
+ "dirt track": 91,
237
+ "dishwasher": 129,
238
+ "door": 14,
239
+ "earth": 13,
240
+ "escalator": 96,
241
+ "fan": 139,
242
+ "fence": 32,
243
+ "field": 29,
244
+ "fireplace": 49,
245
+ "flag": 149,
246
+ "floor": 3,
247
+ "flower": 66,
248
+ "food": 120,
249
+ "fountain": 104,
250
+ "glass": 147,
251
+ "grandstand": 51,
252
+ "grass": 9,
253
+ "hill": 68,
254
+ "hood": 133,
255
+ "house": 25,
256
+ "hovel": 79,
257
+ "kitchen island": 73,
258
+ "lake": 128,
259
+ "lamp": 36,
260
+ "land": 94,
261
+ "light": 82,
262
+ "microwave": 124,
263
+ "minibike": 116,
264
+ "mirror": 27,
265
+ "monitor": 143,
266
+ "mountain": 16,
267
+ "ottoman": 97,
268
+ "oven": 118,
269
+ "painting": 22,
270
+ "palm": 72,
271
+ "path": 52,
272
+ "person": 12,
273
+ "pier": 140,
274
+ "pillow": 57,
275
+ "plant": 17,
276
+ "plate": 142,
277
+ "plaything": 108,
278
+ "pole": 93,
279
+ "pool table": 56,
280
+ "poster": 100,
281
+ "pot": 125,
282
+ "radiator": 146,
283
+ "railing": 38,
284
+ "refrigerator": 50,
285
+ "river": 60,
286
+ "road": 6,
287
+ "rock": 34,
288
+ "rug": 28,
289
+ "runway": 54,
290
+ "sand": 46,
291
+ "sconce": 134,
292
+ "screen": 130,
293
+ "screen door": 58,
294
+ "sculpture": 132,
295
+ "sea": 26,
296
+ "seat": 31,
297
+ "shelf": 24,
298
+ "ship": 103,
299
+ "shower": 145,
300
+ "sidewalk": 11,
301
+ "signboard": 43,
302
+ "sink": 47,
303
+ "sky": 2,
304
+ "skyscraper": 48,
305
+ "sofa": 23,
306
+ "stage": 101,
307
+ "stairs": 53,
308
+ "stairway": 59,
309
+ "step": 121,
310
+ "stool": 110,
311
+ "stove": 71,
312
+ "streetlight": 87,
313
+ "swimming pool": 109,
314
+ "swivel chair": 75,
315
+ "table": 15,
316
+ "tank": 122,
317
+ "television receiver": 89,
318
+ "tent": 114,
319
+ "toilet": 65,
320
+ "towel": 81,
321
+ "tower": 84,
322
+ "trade name": 123,
323
+ "traffic light": 136,
324
+ "tray": 137,
325
+ "tree": 4,
326
+ "truck": 83,
327
+ "van": 102,
328
+ "vase": 135,
329
+ "wall": 0,
330
+ "wardrobe": 35,
331
+ "washer": 107,
332
+ "water": 21,
333
+ "waterfall": 113,
334
+ "windowpane": 8
335
+ },
336
+ "layer_norm_eps": 1e-06,
337
+ "mlp_ratios": [
338
+ 4,
339
+ 4,
340
+ 4,
341
+ 4
342
+ ],
343
+ "model_type": "segformer",
344
+ "num_attention_heads": [
345
+ 1,
346
+ 2,
347
+ 5,
348
+ 8
349
+ ],
350
+ "num_channels": 3,
351
+ "num_encoder_blocks": 4,
352
+ "patch_sizes": [
353
+ 7,
354
+ 3,
355
+ 3,
356
+ 3
357
+ ],
358
+ "reshape_last_stage": true,
359
+ "semantic_loss_ignore_index": 255,
360
+ "sr_ratios": [
361
+ 8,
362
+ 4,
363
+ 2,
364
+ 1
365
+ ],
366
+ "strides": [
367
+ 4,
368
+ 2,
369
+ 2,
370
+ 2
371
+ ],
372
+ "torch_dtype": "float32",
373
+ "transformers_version": "4.35.2"
374
+ }
preprocessor_config.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "do_normalize": true,
3
+ "do_reduce_labels": true,
4
+ "do_rescale": true,
5
+ "do_resize": true,
6
+ "image_mean": [
7
+ 0.485,
8
+ 0.456,
9
+ 0.406
10
+ ],
11
+ "image_processor_type": "SegformerImageProcessor",
12
+ "image_std": [
13
+ 0.229,
14
+ 0.224,
15
+ 0.225
16
+ ],
17
+ "resample": 2,
18
+ "rescale_factor": 0.00392156862745098,
19
+ "size": {
20
+ "height": 512,
21
+ "width": 512
22
+ }
23
+ }
tf_model.h5 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dddab121be85203bf41ee0d7160f9e8d3c813c37cc2ab51b9ac66786342387f1
3
+ size 15285696