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@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cnn.keras filter=lfs diff=lfs merge=lfs -text
+inceptionv4.keras filter=lfs diff=lfs merge=lfs -text
+mobilenetv3large.keras filter=lfs diff=lfs merge=lfs -text
+mobilenetv3small.keras filter=lfs diff=lfs merge=lfs -text
+vgg16.keras filter=lfs diff=lfs merge=lfs -text

cnn.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14eebfae4f7dd9eda5b66deb064044332868e3e3e5c6190e810de5175468c173
+size 155387187

inception_v4.py

@@ -0,0 +1,303 @@
+'''
+Copyright 2017 TensorFlow Authors and Kent Sommer
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+'''
+import tensorflow as tf
+
+# Sys
+import warnings
+# Keras Core
+from keras.layers import MaxPooling2D, Convolution2D, AveragePooling2D
+from keras.layers import Input, Dropout, Dense, Flatten, Activation
+from keras.layers import BatchNormalization
+from keras.layers import concatenate
+from keras import regularizers
+from keras import initializers
+from keras.models import Model
+# Backend
+from keras import backend as K
+# Utils
+from keras.utils import get_file
+
+
+#########################################################################################
+# Implements the Inception Network v4 (http://arxiv.org/pdf/1602.07261v1.pdf) in Keras. #
+#########################################################################################
+
+WEIGHTS_PATH = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_NO_TOP = 'https://github.com/kentsommer/keras-inceptionV4/releases/download/2.1/inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5'
+
+
+def preprocess_input(x):
+    x = tf.divide(x, 255.0)
+    x = tf.subtract(x, 0.5)
+    x = tf.multiply(x, 2.0)
+    return x
+
+
+def conv2d_bn(x, nb_filter, num_row, num_col,
+              padding='same', strides=(1, 1), use_bias=False):
+    """
+    Utility function to apply conv + BN. 
+    (Slightly modified from https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py)
+    """
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+    x = Convolution2D(nb_filter, (num_row, num_col),
+                      strides=strides,
+                      padding=padding,
+                      use_bias=use_bias,
+                      kernel_regularizer=regularizers.l2(0.00004),
+                      kernel_initializer=initializers.VarianceScaling(scale=2.0, mode='fan_in', distribution='normal', seed=None))(x)
+    x = BatchNormalization(axis=channel_axis, momentum=0.9997, scale=False)(x)
+    x = Activation('relu')(x)
+    return x
+
+
+def block_inception_a(input):
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+
+    branch_0 = conv2d_bn(input, 96, 1, 1)
+
+    branch_1 = conv2d_bn(input, 64, 1, 1)
+    branch_1 = conv2d_bn(branch_1, 96, 3, 3)
+
+    branch_2 = conv2d_bn(input, 64, 1, 1)
+    branch_2 = conv2d_bn(branch_2, 96, 3, 3)
+    branch_2 = conv2d_bn(branch_2, 96, 3, 3)
+
+    branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input)
+    branch_3 = conv2d_bn(branch_3, 96, 1, 1)
+
+    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
+    return x
+
+
+def block_reduction_a(input):
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+
+    branch_0 = conv2d_bn(input, 384, 3, 3, strides=(2,2), padding='valid')
+
+    branch_1 = conv2d_bn(input, 192, 1, 1)
+    branch_1 = conv2d_bn(branch_1, 224, 3, 3)
+    branch_1 = conv2d_bn(branch_1, 256, 3, 3, strides=(2,2), padding='valid')
+
+    branch_2 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(input)
+
+    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
+    return x
+
+
+def block_inception_b(input):
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+
+    branch_0 = conv2d_bn(input, 384, 1, 1)
+
+    branch_1 = conv2d_bn(input, 192, 1, 1)
+    branch_1 = conv2d_bn(branch_1, 224, 1, 7)
+    branch_1 = conv2d_bn(branch_1, 256, 7, 1)
+
+    branch_2 = conv2d_bn(input, 192, 1, 1)
+    branch_2 = conv2d_bn(branch_2, 192, 7, 1)
+    branch_2 = conv2d_bn(branch_2, 224, 1, 7)
+    branch_2 = conv2d_bn(branch_2, 224, 7, 1)
+    branch_2 = conv2d_bn(branch_2, 256, 1, 7)
+
+    branch_3 = AveragePooling2D((3,3), strides=(1,1), padding='same')(input)
+    branch_3 = conv2d_bn(branch_3, 128, 1, 1)
+
+    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
+    return x
+
+
+def block_reduction_b(input):
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+
+    branch_0 = conv2d_bn(input, 192, 1, 1)
+    branch_0 = conv2d_bn(branch_0, 192, 3, 3, strides=(2, 2), padding='valid')
+
+    branch_1 = conv2d_bn(input, 256, 1, 1)
+    branch_1 = conv2d_bn(branch_1, 256, 1, 7)
+    branch_1 = conv2d_bn(branch_1, 320, 7, 1)
+    branch_1 = conv2d_bn(branch_1, 320, 3, 3, strides=(2,2), padding='valid')
+
+    branch_2 = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(input)
+
+    x = concatenate([branch_0, branch_1, branch_2], axis=channel_axis)
+    return x
+
+
+def block_inception_c(input):
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+
+    branch_0 = conv2d_bn(input, 256, 1, 1)
+
+    branch_1 = conv2d_bn(input, 384, 1, 1)
+    branch_10 = conv2d_bn(branch_1, 256, 1, 3)
+    branch_11 = conv2d_bn(branch_1, 256, 3, 1)
+    branch_1 = concatenate([branch_10, branch_11], axis=channel_axis)
+
+
+    branch_2 = conv2d_bn(input, 384, 1, 1)
+    branch_2 = conv2d_bn(branch_2, 448, 3, 1)
+    branch_2 = conv2d_bn(branch_2, 512, 1, 3)
+    branch_20 = conv2d_bn(branch_2, 256, 1, 3)
+    branch_21 = conv2d_bn(branch_2, 256, 3, 1)
+    branch_2 = concatenate([branch_20, branch_21], axis=channel_axis)
+
+    branch_3 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(input)
+    branch_3 = conv2d_bn(branch_3, 256, 1, 1)
+
+    x = concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis)
+    return x
+
+
+def inception_v4_base(input):
+    if K.image_data_format() == 'channels_first':
+        channel_axis = 1
+    else:
+        channel_axis = -1
+
+    # Input Shape is 299 x 299 x 3 (th) or 3 x 299 x 299 (th)
+    net = conv2d_bn(input, 32, 3, 3, strides=(2,2), padding='valid')
+    net = conv2d_bn(net, 32, 3, 3, padding='valid')
+    net = conv2d_bn(net, 64, 3, 3)
+
+    branch_0 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(net)
+
+    branch_1 = conv2d_bn(net, 96, 3, 3, strides=(2,2), padding='valid')
+
+    net = concatenate([branch_0, branch_1], axis=channel_axis)
+
+    branch_0 = conv2d_bn(net, 64, 1, 1)
+    branch_0 = conv2d_bn(branch_0, 96, 3, 3, padding='valid')
+
+    branch_1 = conv2d_bn(net, 64, 1, 1)
+    branch_1 = conv2d_bn(branch_1, 64, 1, 7)
+    branch_1 = conv2d_bn(branch_1, 64, 7, 1)
+    branch_1 = conv2d_bn(branch_1, 96, 3, 3, padding='valid')
+
+    net = concatenate([branch_0, branch_1], axis=channel_axis)
+
+    branch_0 = conv2d_bn(net, 192, 3, 3, strides=(2,2), padding='valid')
+    branch_1 = MaxPooling2D((3,3), strides=(2,2), padding='valid')(net)
+
+    net = concatenate([branch_0, branch_1], axis=channel_axis)
+
+    # 35 x 35 x 384
+    # 4 x Inception-A blocks
+    for idx in range(4):
+        net = block_inception_a(net)
+
+    # 35 x 35 x 384
+    # Reduction-A block
+    net = block_reduction_a(net)
+
+    # 17 x 17 x 1024
+    # 7 x Inception-B blocks
+    for idx in range(7):
+        net = block_inception_b(net)
+
+    # 17 x 17 x 1024
+    # Reduction-B block
+    net = block_reduction_b(net)
+
+    # 8 x 8 x 1536
+    # 3 x Inception-C blocks
+    for idx in range(3):
+        net = block_inception_c(net)
+
+    return net
+
+
+def inception_v4(num_classes, dropout_keep_prob, weights, include_top, input_shape=(299, 299, 3)):
+    '''
+    Creates the inception v4 network
+
+    Args:
+    	num_classes: number of classes
+    	dropout_keep_prob: float, the fraction to keep before final layer.
+    
+    Returns: 
+    	logits: the logits outputs of the model.
+    '''
+
+    # Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
+    if K.image_data_format() == 'channels_first':
+        inputs = Input((3, input_shape[1], input_shape[2]))
+    else:
+        inputs = Input(input_shape)
+
+    # Make inception base
+    x = inception_v4_base(inputs)
+
+
+    # Final pooling and prediction
+    if include_top:
+        # 1 x 1 x 1536
+        x = AveragePooling2D((8,8), padding='valid')(x)
+        x = Dropout(dropout_keep_prob)(x)
+        x = Flatten()(x)
+        # 1536
+        x = Dense(units=num_classes, activation='softmax')(x)
+
+    model = Model(inputs, x, name='inception_v4')
+
+    # load weights
+    if weights == 'imagenet':
+        if K.image_data_format() == 'channels_first':
+            if K.backend() == 'tensorflow':
+                warnings.warn('You are using the TensorFlow backend, yet you '
+                              'are using the Theano '
+                              'image data format convention '
+                              '(`image_data_format="channels_first"`). '
+                              'For best performance, set '
+                              '`image_data_format="channels_last"` in '
+                              'your Keras config '
+                              'at ~/.keras/keras.json.')
+        if include_top:
+            weights_path = get_file(
+                'inception-v4_weights_tf_dim_ordering_tf_kernels.h5',
+                WEIGHTS_PATH,
+                cache_subdir='models',
+                md5_hash='9fe79d77f793fe874470d84ca6ba4a3b')
+        else:
+            weights_path = get_file(
+                'inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                WEIGHTS_PATH_NO_TOP,
+                cache_subdir='models',
+                md5_hash='9296b46b5971573064d12e4669110969')
+        model.load_weights(weights_path)
+    return model
+
+
+def InceptionV4(num_classes=1001, dropout_prob=0.2, weights=None, include_top=True, input_shape=(299, 299, 3)):
+    return inception_v4(num_classes, dropout_prob, weights, include_top, input_shape=input_shape)

inceptionv4.keras

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+oid sha256:649dc6525e61a5c57f9e7370ec7674f4dd2aa1de6c4c14d0f5fcb57b04d78b0b
+size 171288944

labelscnn.txt

@@ -0,0 +1,54 @@
+ABBOTTS BABBLER
+ABBOTTS BOOBY
+ASIAN OPENBILL STORK
+AUSTRALASIAN FIGBIRD
+BALI STARLING
+BAR-TAILED GODWIT
+BARN SWALLOW
+BLACK AND YELLOW BROADBILL
+BLACK BAZA
+BORNEAN BRISTLEHEAD
+BORNEAN LEAFBIRD
+BROWN NOODY
+BULWERS PHEASANT
+CASPIAN TERN
+CHESTNUT WINGED CUCKOO
+CHINESE POND HERON
+COMMON IORA
+COPPERSMITH BARBET
+CRESTED SERPENT EAGLE
+CRESTED WOOD PARTRIDGE
+DOUBLE EYED FIG PARROT
+DUSKY LORY
+FIERY MINIVET
+FOREST WAGTAIL
+GLOSSY IBIS
+GREAT ARGUS
+GREEN BROADBILL
+GREY HEADED FISH EAGLE
+INDIGO FLYCATCHER
+JAVA SPARROW
+LESSER ADJUTANT
+MAGPIE GOOSE
+MALEO
+MASKED LAPWING
+NICOBAR PIGEON
+NOISY FRIARBIRD
+ORANGE BREASTED TROGON
+ORIENTAL BAY OWL
+OSPREY
+PEREGRINE FALCON
+POMARINE JAEGER
+RED BEARDED BEE EATER
+ROCK DOVE
+RUDY KINGFISHER
+SAMATRAN THRUSH
+SPOON BILED SANDPIPER
+SPOTTED WHISTLING DUCK
+STORK BILLED KINGFISHER
+VICTORIA CROWNED PIGEON
+VIOLET CUCKOO
+WHITE BREASTED WATERHEN
+WHITE BROWED CRAKE
+WILSONS BIRD OF PARADISE
+ZEBRA DOVE

labelsinceptionv4.txt

@@ -0,0 +1,54 @@
+ABBOTTS BABBLER
+ABBOTTS BOOBY
+ASIAN OPENBILL STORK
+AUSTRALASIAN FIGBIRD
+BALI STARLING
+BAR-TAILED GODWIT
+BARN SWALLOW
+BLACK AND YELLOW BROADBILL
+BLACK BAZA
+BORNEAN BRISTLEHEAD
+BORNEAN LEAFBIRD
+BROWN NOODY
+BULWERS PHEASANT
+CASPIAN TERN
+CHESTNUT WINGED CUCKOO
+CHINESE POND HERON
+COMMON IORA
+COPPERSMITH BARBET
+CRESTED SERPENT EAGLE
+CRESTED WOOD PARTRIDGE
+DOUBLE EYED FIG PARROT
+DUSKY LORY
+FIERY MINIVET
+FOREST WAGTAIL
+GLOSSY IBIS
+GREAT ARGUS
+GREEN BROADBILL
+GREY HEADED FISH EAGLE
+INDIGO FLYCATCHER
+JAVA SPARROW
+LESSER ADJUTANT
+MAGPIE GOOSE
+MALEO
+MASKED LAPWING
+NICOBAR PIGEON
+NOISY FRIARBIRD
+ORANGE BREASTED TROGON
+ORIENTAL BAY OWL
+OSPREY
+PEREGRINE FALCON
+POMARINE JAEGER
+RED BEARDED BEE EATER
+ROCK DOVE
+RUDY KINGFISHER
+SAMATRAN THRUSH
+SPOON BILED SANDPIPER
+SPOTTED WHISTLING DUCK
+STORK BILLED KINGFISHER
+VICTORIA CROWNED PIGEON
+VIOLET CUCKOO
+WHITE BREASTED WATERHEN
+WHITE BROWED CRAKE
+WILSONS BIRD OF PARADISE
+ZEBRA DOVE

labelslarge.txt

@@ -0,0 +1,54 @@
+ABBOTTS BABBLER
+ABBOTTS BOOBY
+ASIAN OPENBILL STORK
+AUSTRALASIAN FIGBIRD
+BALI STARLING
+BAR-TAILED GODWIT
+BARN SWALLOW
+BLACK AND YELLOW BROADBILL
+BLACK BAZA
+BORNEAN BRISTLEHEAD
+BORNEAN LEAFBIRD
+BROWN NOODY
+BULWERS PHEASANT
+CASPIAN TERN
+CHESTNUT WINGED CUCKOO
+CHINESE POND HERON
+COMMON IORA
+COPPERSMITH BARBET
+CRESTED SERPENT EAGLE
+CRESTED WOOD PARTRIDGE
+DOUBLE EYED FIG PARROT
+DUSKY LORY
+FIERY MINIVET
+FOREST WAGTAIL
+GLOSSY IBIS
+GREAT ARGUS
+GREEN BROADBILL
+GREY HEADED FISH EAGLE
+INDIGO FLYCATCHER
+JAVA SPARROW
+LESSER ADJUTANT
+MAGPIE GOOSE
+MALEO
+MASKED LAPWING
+NICOBAR PIGEON
+NOISY FRIARBIRD
+ORANGE BREASTED TROGON
+ORIENTAL BAY OWL
+OSPREY
+PEREGRINE FALCON
+POMARINE JAEGER
+RED BEARDED BEE EATER
+ROCK DOVE
+RUDY KINGFISHER
+SAMATRAN THRUSH
+SPOON BILED SANDPIPER
+SPOTTED WHISTLING DUCK
+STORK BILLED KINGFISHER
+VICTORIA CROWNED PIGEON
+VIOLET CUCKOO
+WHITE BREASTED WATERHEN
+WHITE BROWED CRAKE
+WILSONS BIRD OF PARADISE
+ZEBRA DOVE

labelssmall.txt

@@ -0,0 +1,54 @@
+ABBOTTS BABBLER
+ABBOTTS BOOBY
+ASIAN OPENBILL STORK
+AUSTRALASIAN FIGBIRD
+BALI STARLING
+BAR-TAILED GODWIT
+BARN SWALLOW
+BLACK AND YELLOW BROADBILL
+BLACK BAZA
+BORNEAN BRISTLEHEAD
+BORNEAN LEAFBIRD
+BROWN NOODY
+BULWERS PHEASANT
+CASPIAN TERN
+CHESTNUT WINGED CUCKOO
+CHINESE POND HERON
+COMMON IORA
+COPPERSMITH BARBET
+CRESTED SERPENT EAGLE
+CRESTED WOOD PARTRIDGE
+DOUBLE EYED FIG PARROT
+DUSKY LORY
+FIERY MINIVET
+FOREST WAGTAIL
+GLOSSY IBIS
+GREAT ARGUS
+GREEN BROADBILL
+GREY HEADED FISH EAGLE
+INDIGO FLYCATCHER
+JAVA SPARROW
+LESSER ADJUTANT
+MAGPIE GOOSE
+MALEO
+MASKED LAPWING
+NICOBAR PIGEON
+NOISY FRIARBIRD
+ORANGE BREASTED TROGON
+ORIENTAL BAY OWL
+OSPREY
+PEREGRINE FALCON
+POMARINE JAEGER
+RED BEARDED BEE EATER
+ROCK DOVE
+RUDY KINGFISHER
+SAMATRAN THRUSH
+SPOON BILED SANDPIPER
+SPOTTED WHISTLING DUCK
+STORK BILLED KINGFISHER
+VICTORIA CROWNED PIGEON
+VIOLET CUCKOO
+WHITE BREASTED WATERHEN
+WHITE BROWED CRAKE
+WILSONS BIRD OF PARADISE
+ZEBRA DOVE

labelsvgg.txt

@@ -0,0 +1,54 @@
+ABBOTTS BABBLER
+ABBOTTS BOOBY
+ASIAN OPENBILL STORK
+AUSTRALASIAN FIGBIRD
+BALI STARLING
+BAR-TAILED GODWIT
+BARN SWALLOW
+BLACK AND YELLOW BROADBILL
+BLACK BAZA
+BORNEAN BRISTLEHEAD
+BORNEAN LEAFBIRD
+BROWN NOODY
+BULWERS PHEASANT
+CASPIAN TERN
+CHESTNUT WINGED CUCKOO
+CHINESE POND HERON
+COMMON IORA
+COPPERSMITH BARBET
+CRESTED SERPENT EAGLE
+CRESTED WOOD PARTRIDGE
+DOUBLE EYED FIG PARROT
+DUSKY LORY
+FIERY MINIVET
+FOREST WAGTAIL
+GLOSSY IBIS
+GREAT ARGUS
+GREEN BROADBILL
+GREY HEADED FISH EAGLE
+INDIGO FLYCATCHER
+JAVA SPARROW
+LESSER ADJUTANT
+MAGPIE GOOSE
+MALEO
+MASKED LAPWING
+NICOBAR PIGEON
+NOISY FRIARBIRD
+ORANGE BREASTED TROGON
+ORIENTAL BAY OWL
+OSPREY
+PEREGRINE FALCON
+POMARINE JAEGER
+RED BEARDED BEE EATER
+ROCK DOVE
+RUDY KINGFISHER
+SAMATRAN THRUSH
+SPOON BILED SANDPIPER
+SPOTTED WHISTLING DUCK
+STORK BILLED KINGFISHER
+VICTORIA CROWNED PIGEON
+VIOLET CUCKOO
+WHITE BREASTED WATERHEN
+WHITE BROWED CRAKE
+WILSONS BIRD OF PARADISE
+ZEBRA DOVE

mobilenetv3large.keras

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+size 15780088

mobilenetv3small.keras

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vgg16.keras

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