Merge pull request larymak#217 from Ankita1811/main

Vehicle Detection

Author: larymak

Vehicle-Detection/LICENSE

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+COPYRIGHT
+
+All contributions by deeplearning.ai (Kian Katanforoosh, Younes Bensouda Mourri, Andrew Ng):
+Copyright (c) 2017, deeplearning.ai (Kian Katanforoosh, Younes Bensouda Mourri, Andrew Ng).
+All rights reserved.
+
+This work incorporates contributions due to Allan Zelener released under an MIT License, reproduced below:
+
+----------------------------------------------------
+All contributions by Allan Zelener:
+Copyright (c) 2017, Allan Zelener.
+All rights reserved.
+
+All other contributions:
+Copyright (c) 2017, the respective contributors.
+All rights reserved.
+
+Each contributor holds copyright over their respective contributions.
+The project versioning (Git) records all such contribution source information.
+
+LICENSE
+
+The MIT License (MIT)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+----------------------------------------------------
+

Vehicle-Detection/README.md

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+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

Vehicle-Detection/Vehicle_detection.ipynb

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+car

Vehicle-Detection/__pycache__/yolo_utils.cpython-37.pyc

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+0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828

Vehicle-Detection/images/0001.jpg

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+"""Darknet19 Model Defined in Keras."""
+import functools
+from functools import partial
+
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers.advanced_activations import LeakyReLU
+from keras.layers.normalization import BatchNormalization
+from keras.models import Model
+from keras.regularizers import l2
+
+from ..utils import compose
+
+# Partial wrapper for Convolution2D with static default argument.
+_DarknetConv2D = partial(Conv2D, padding='same')
+
+
+@functools.wraps(Conv2D)
+def DarknetConv2D(*args, **kwargs):
+    """Wrapper to set Darknet weight regularizer for Convolution2D."""
+    darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
+    darknet_conv_kwargs.update(kwargs)
+    return _DarknetConv2D(*args, **darknet_conv_kwargs)
+
+
+def DarknetConv2D_BN_Leaky(*args, **kwargs):
+    """Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
+    no_bias_kwargs = {'use_bias': False}
+    no_bias_kwargs.update(kwargs)
+    return compose(
+        DarknetConv2D(*args, **no_bias_kwargs),
+        BatchNormalization(),
+        LeakyReLU(alpha=0.1))
+
+
+def bottleneck_block(outer_filters, bottleneck_filters):
+    """Bottleneck block of 3x3, 1x1, 3x3 convolutions."""
+    return compose(
+        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)),
+        DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
+        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))
+
+
+def bottleneck_x2_block(outer_filters, bottleneck_filters):
+    """Bottleneck block of 3x3, 1x1, 3x3, 1x1, 3x3 convolutions."""
+    return compose(
+        bottleneck_block(outer_filters, bottleneck_filters),
+        DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
+        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))
+
+
+def darknet_body():
+    """Generate first 18 conv layers of Darknet-19."""
+    return compose(
+        DarknetConv2D_BN_Leaky(32, (3, 3)),
+        MaxPooling2D(),
+        DarknetConv2D_BN_Leaky(64, (3, 3)),
+        MaxPooling2D(),
+        bottleneck_block(128, 64),
+        MaxPooling2D(),
+        bottleneck_block(256, 128),
+        MaxPooling2D(),
+        bottleneck_x2_block(512, 256),
+        MaxPooling2D(),
+        bottleneck_x2_block(1024, 512))
+
+
+def darknet19(inputs):
+    """Generate Darknet-19 model for Imagenet classification."""
+    body = darknet_body()(inputs)
+    logits = DarknetConv2D(1000, (1, 1), activation='softmax')(body)
+    return Model(inputs, logits)