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large model files

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  1. .gitattributes +174 -0
  2. convert_models.ipynb +168 -0
  3. densenet169_pooling=avg/group1-shard10of13.bin +3 -0
  4. densenet169_pooling=avg/group1-shard11of13.bin +3 -0
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  6. densenet169_pooling=avg/group1-shard13of13.bin +3 -0
  7. densenet169_pooling=avg/group1-shard1of13.bin +3 -0
  8. densenet169_pooling=avg/group1-shard2of13.bin +3 -0
  9. densenet169_pooling=avg/group1-shard3of13.bin +3 -0
  10. densenet169_pooling=avg/group1-shard4of13.bin +3 -0
  11. densenet169_pooling=avg/group1-shard5of13.bin +3 -0
  12. densenet169_pooling=avg/group1-shard6of13.bin +3 -0
  13. densenet169_pooling=avg/group1-shard7of13.bin +3 -0
  14. densenet169_pooling=avg/group1-shard8of13.bin +3 -0
  15. densenet169_pooling=avg/group1-shard9of13.bin +3 -0
  16. densenet169_pooling=avg/model.json +0 -0
  17. densenet169_pooling=max/group1-shard10of13.bin +3 -0
  18. densenet169_pooling=max/group1-shard11of13.bin +3 -0
  19. densenet169_pooling=max/group1-shard12of13.bin +3 -0
  20. densenet169_pooling=max/group1-shard13of13.bin +3 -0
  21. densenet169_pooling=max/group1-shard1of13.bin +3 -0
  22. densenet169_pooling=max/group1-shard2of13.bin +3 -0
  23. densenet169_pooling=max/group1-shard3of13.bin +3 -0
  24. densenet169_pooling=max/group1-shard4of13.bin +3 -0
  25. densenet169_pooling=max/group1-shard5of13.bin +3 -0
  26. densenet169_pooling=max/group1-shard6of13.bin +3 -0
  27. densenet169_pooling=max/group1-shard7of13.bin +3 -0
  28. densenet169_pooling=max/group1-shard8of13.bin +3 -0
  29. densenet169_pooling=max/group1-shard9of13.bin +3 -0
  30. densenet169_pooling=max/model.json +0 -0
  31. densenet201_pooling=avg/group1-shard10of18.bin +3 -0
  32. densenet201_pooling=avg/group1-shard11of18.bin +3 -0
  33. densenet201_pooling=avg/group1-shard12of18.bin +3 -0
  34. densenet201_pooling=avg/group1-shard13of18.bin +3 -0
  35. densenet201_pooling=avg/group1-shard14of18.bin +3 -0
  36. densenet201_pooling=avg/group1-shard15of18.bin +3 -0
  37. densenet201_pooling=avg/group1-shard16of18.bin +3 -0
  38. densenet201_pooling=avg/group1-shard17of18.bin +3 -0
  39. densenet201_pooling=avg/group1-shard18of18.bin +3 -0
  40. densenet201_pooling=avg/group1-shard1of18.bin +3 -0
  41. densenet201_pooling=avg/group1-shard2of18.bin +3 -0
  42. densenet201_pooling=avg/group1-shard3of18.bin +3 -0
  43. densenet201_pooling=avg/group1-shard4of18.bin +3 -0
  44. densenet201_pooling=avg/group1-shard5of18.bin +3 -0
  45. densenet201_pooling=avg/group1-shard6of18.bin +3 -0
  46. densenet201_pooling=avg/group1-shard7of18.bin +3 -0
  47. densenet201_pooling=avg/group1-shard8of18.bin +3 -0
  48. densenet201_pooling=avg/group1-shard9of18.bin +3 -0
  49. densenet201_pooling=avg/model.json +0 -0
  50. densenet201_pooling=max/group1-shard10of18.bin +3 -0
.gitattributes ADDED
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+ densenet169_pooling=max/group1-shard10of13.bin filter=lfs diff=lfs merge=lfs -text
166
+ densenet169_pooling=max/group1-shard6of13.bin filter=lfs diff=lfs merge=lfs -text
167
+ densenet169_pooling=max/group1-shard8of13.bin filter=lfs diff=lfs merge=lfs -text
168
+ densenet201_pooling=max/group1-shard10of18.bin filter=lfs diff=lfs merge=lfs -text
169
+ densenet201_pooling=max/group1-shard13of18.bin filter=lfs diff=lfs merge=lfs -text
170
+ resnet152_pooling=avg/group1-shard14of56.bin filter=lfs diff=lfs merge=lfs -text
171
+ resnet152_pooling=avg/group1-shard29of56.bin filter=lfs diff=lfs merge=lfs -text
172
+ resnet152_pooling=avg/group1-shard8of56.bin filter=lfs diff=lfs merge=lfs -text
173
+ resnet152_pooling=max/group1-shard2of56.bin filter=lfs diff=lfs merge=lfs -text
174
+ resnet152_pooling=max/group1-shard51of56.bin filter=lfs diff=lfs merge=lfs -text
convert_models.ipynb ADDED
@@ -0,0 +1,168 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 12,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import tensorflow as tf\n",
10
+ "weights = \"imagenet\""
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": 13,
16
+ "metadata": {},
17
+ "outputs": [],
18
+ "source": [
19
+ "def convert_keras_to_tfjs(model, filename):\n",
20
+ " model.save(filename + \".h5\")\n",
21
+ " import os\n",
22
+ " os.mkdir(filename)\n",
23
+ " os.system(\"tensorflowjs_converter --input_format keras ./{}.h5 ./{}\".format(filename, filename))\n",
24
+ " os.system(\"rm {}.h5\".format(filename))\n"
25
+ ]
26
+ },
27
+ {
28
+ "cell_type": "markdown",
29
+ "metadata": {},
30
+ "source": [
31
+ "# Densenet201"
32
+ ]
33
+ },
34
+ {
35
+ "cell_type": "code",
36
+ "execution_count": 14,
37
+ "metadata": {},
38
+ "outputs": [
39
+ {
40
+ "name": "stdout",
41
+ "output_type": "stream",
42
+ "text": [
43
+ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
44
+ "74836368/74836368 [==============================] - 5s 0us/step\n",
45
+ "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
46
+ ]
47
+ },
48
+ {
49
+ "name": "stderr",
50
+ "output_type": "stream",
51
+ "text": [
52
+ "/Users/teosanchez/anaconda3/lib/python3.11/site-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
53
+ " saving_api.save_model(\n"
54
+ ]
55
+ },
56
+ {
57
+ "name": "stdout",
58
+ "output_type": "stream",
59
+ "text": [
60
+ "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
61
+ ]
62
+ }
63
+ ],
64
+ "source": [
65
+ "pooling = [\"avg\", \"max\"]\n",
66
+ "for pool in pooling:\n",
67
+ " model = tf.keras.applications.DenseNet201(include_top = False, weights = weights, pooling = pool)\n",
68
+ " filename = \"densenet201_pooling={1}\".format(weights, pool)\n",
69
+ " convert_keras_to_tfjs(model, filename)"
70
+ ]
71
+ },
72
+ {
73
+ "cell_type": "markdown",
74
+ "metadata": {},
75
+ "source": [
76
+ "# Densenet169"
77
+ ]
78
+ },
79
+ {
80
+ "cell_type": "code",
81
+ "execution_count": 16,
82
+ "metadata": {},
83
+ "outputs": [
84
+ {
85
+ "name": "stdout",
86
+ "output_type": "stream",
87
+ "text": [
88
+ "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
89
+ "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
90
+ ]
91
+ }
92
+ ],
93
+ "source": [
94
+ "pooling = [\"avg\", \"max\"]\n",
95
+ "for pool in pooling:\n",
96
+ " model = tf.keras.applications.DenseNet169(include_top = False, weights = weights, pooling = pool)\n",
97
+ " filename = \"densenet169_pooling={1}\".format(weights, pool)\n",
98
+ " convert_keras_to_tfjs(model, filename)"
99
+ ]
100
+ },
101
+ {
102
+ "cell_type": "markdown",
103
+ "metadata": {},
104
+ "source": [
105
+ "# Resnet152"
106
+ ]
107
+ },
108
+ {
109
+ "cell_type": "code",
110
+ "execution_count": 17,
111
+ "metadata": {},
112
+ "outputs": [
113
+ {
114
+ "name": "stdout",
115
+ "output_type": "stream",
116
+ "text": [
117
+ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
118
+ "234698864/234698864 [==============================] - 10s 0us/step\n",
119
+ "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
120
+ ]
121
+ },
122
+ {
123
+ "name": "stderr",
124
+ "output_type": "stream",
125
+ "text": [
126
+ "/Users/teosanchez/anaconda3/lib/python3.11/site-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
127
+ " saving_api.save_model(\n"
128
+ ]
129
+ },
130
+ {
131
+ "name": "stdout",
132
+ "output_type": "stream",
133
+ "text": [
134
+ "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
135
+ ]
136
+ }
137
+ ],
138
+ "source": [
139
+ "pooling = [\"avg\", \"max\"]\n",
140
+ "for pool in pooling:\n",
141
+ " model = tf.keras.applications.ResNet152(include_top = False, weights = weights, pooling = pool)\n",
142
+ " filename = \"resnet152_pooling={1}\".format(weights, pool)\n",
143
+ " convert_keras_to_tfjs(model, filename)"
144
+ ]
145
+ }
146
+ ],
147
+ "metadata": {
148
+ "kernelspec": {
149
+ "display_name": "base",
150
+ "language": "python",
151
+ "name": "python3"
152
+ },
153
+ "language_info": {
154
+ "codemirror_mode": {
155
+ "name": "ipython",
156
+ "version": 3
157
+ },
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+ "file_extension": ".py",
159
+ "mimetype": "text/x-python",
160
+ "name": "python",
161
+ "nbconvert_exporter": "python",
162
+ "pygments_lexer": "ipython3",
163
+ "version": "3.11.4"
164
+ }
165
+ },
166
+ "nbformat": 4,
167
+ "nbformat_minor": 2
168
+ }
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