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DonkeyCar

使用Colab来加速Donkey Car训练 (Tensorflow GPU)

colab

本notebook助你快速训练你的Donkey car或者自动驾驶小车模型。

参考并改进了@sachindroid8的notebook，先向前人致敬！

先说说使用Colab进行训练的优缺点

  1. 效率

使用Colab需要梯子，也要把数据传给google，上传时间因人而异，我上传大概花来2-3分钟，剩下就是执行代码和训练。第一执行代码需要搞懂什么原理，后面执行基本不用花什么时间，然而使用GPU训练时间，我有6k图片，训练30-40个Epoch左右提前结束，训练时间少于1分钟！每个Epoch只需1-2秒。

然后使用我的Macbook Pro训练，因为没有GPU的缘故，每个Epoc需要30-50秒，训练下来，接近40分钟到1小时才能完成。

  1. 便利性

如果没有梯子，当然Colab不是一个选择，我知道百度也提供一些有限度免费的服务器，以后可以再做测试，但有梯子的话，Google Colab是最好的选择。下面便开始教你怎样开始训练 

载入我的Notebook

打开使用Google_Colab来Donkey_Car

安装 TensorFlow 1.14.0

TensorFlow 2.x和Donkey Car 3.x现在还有兼容性问题，暂时不推荐使用(2019.8.17)In [1]:

!pip install tensorflow-gpu==1.14.0

Collecting tensorflow-gpu==1.14.0
  Downloading https://files.pythonhosted.org/packages/76/04/43153bfdfcf6c9a4c38ecdb971ca9a75b9a791bb69a764d652c359aca504/tensorflow_gpu-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (377.0MB)
     |████████████████████████████████| 377.0MB 86kB/s 
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.15.0)
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.7.1)
Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.11.2)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.8.0)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.0.8)
Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.33.4)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (3.7.1)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.12.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.1.7)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.2.2)
Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.16.4)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.14.0) (2.8.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (3.1.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (0.15.5)
Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (41.0.1)
Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.14.0

检查GPU是否有效

如果显示”Found GPU at: / device: GPU: 0“，则GPU可以正常使用

如果没有以上输出，需要检查Runtime （运行类型）是否选择了GPU硬件加速器In [2]:

device_name = tf.test.gpu_device_name()

if device_name != '/device:GPU:0':

raise SystemError('GPU device not found')

print('Found GPU at: {}'.format(device_name))

Found GPU at: /device:GPU:0

克隆Donkey respository

In [3]:

!git clone https://github.com/autorope/donkeycar.git donkey

Cloning into 'donkey'...
remote: Enumerating objects: 120, done.
remote: Counting objects: 100% (120/120), done.
remote: Compressing objects: 100% (76/76), done.
remote: Total 10558 (delta 65), reused 73 (delta 31), pack-reused 10438
Receiving objects: 100% (10558/10558), 58.74 MiB | 46.70 MiB/s, done.
Resolving deltas: 100% (6528/6528), done.

安装 Donkey car

In [4]:

Obtaining file:///content/donkey
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (1.16.4)
Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.3.0)
Requirement already satisfied: docopt in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.6.2)
Requirement already satisfied: tornado in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.5.3)
Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.21.0)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.8.0)
Requirement already satisfied: moviepy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.2.3.5)
Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.24.2)
Requirement already satisfied: PrettyTable in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.7.2)
Collecting paho-mqtt (from donkeycar==3.1.0)
  Downloading https://files.pythonhosted.org/packages/25/63/db25e62979c2a716a74950c9ed658dce431b5cb01fde29eb6cba9489a904/paho-mqtt-1.4.0.tar.gz (88kB)
     |████████████████████████████████| 92kB 4.2MB/s 
Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->donkeycar==3.1.0) (0.46)
Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (1.24.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (2019.6.16)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (3.0.4)
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Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py->donkeycar==3.1.0) (1.12.0)
Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (4.4.0)
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Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (2.4.1)
Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2018.9)
Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2.5.3)
Building wheels for collected packages: paho-mqtt
  Building wheel for paho-mqtt (setup.py) ... done
  Created wheel for paho-mqtt: filename=paho_mqtt-1.4.0-cp36-none-any.whl size=48333 sha256=9a67d0c95fae2b9495c20980895d9569ef53859cbc22bb57fc2466d7a581af7b
  Stored in directory: /root/.cache/pip/wheels/82/e5/de/d90d0f397648a1b58ffeea1b5742ac8c77f71fd43b550fa5a5
Successfully built paho-mqtt
Installing collected packages: paho-mqtt, donkeycar
  Running setup.py develop for donkeycar
Successfully installed donkeycar paho-mqtt-1.4.0

创建项目

我使用了mycar作为项目名称，你可以改名，但改名后需要相应修改后面但代码In [5]:

!donkey createcar --path /content/mycar

using donkey v3.1.0 ...
Creating car folder: /content/mycar
making dir  /content/mycar
Creating data & model folders.
making dir  /content/mycar/models
making dir  /content/mycar/data
making dir  /content/mycar/logs
Copying car application template: complete
Copying car config defaults. Adjust these before starting your car.
Copying train script. Adjust these before starting your car.
Copying my car config overrides
Donkey setup complete.

准备数据: 上传data.zip并解压

现在你需要把pi采集回来的data目录上需要训练的目录打包，保存成data.zip.

在pi的data目录上运行:

$ zip -r data.zip tub_3_19-08-17/

然后拷贝会电脑，准备上传到Colab

上传data.zip到Colab

运行下面代码，会出现一个上传按钮，点击上传刚才打包的data.zipIn [7]:

from google.colab import files

if(os.path.exists("/content/data.zip")):

os.remove("/content/data.zip")

if(os.path.exists("/content/mycar/data/data.zip")):

os.remove("/content/mycar/data/data.zip")

uploaded = files.upload()

WORK_FOLDER = "/content/mycar/data/"

if(os.path.exists(WORK_FOLDER) == False):

!mv /content/data.zip /content/mycar/data/

清理已经上传的文件

你需要确保content/mycar/data目录下有tub目录，目录里面有图片和对应的json文件

data.zip就不用保留了In [0]:

!rm /content/mycar/data/data.zip

训练模型

In [21]:

!python /content/mycar/manage.py train --model /content/mycar/models/mypilot.h5

using donkey v3.1.0 ...
loading config file: /content/mycar/config.py
loading personal config over-rides

config loaded
WARNING: Logging before flag parsing goes to stderr.
W0818 04:53:15.944086 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0818 04:53:15.944333 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2019-08-18 04:53:15.954857: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-08-18 04:53:15.959505: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-08-18 04:53:16.083690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.084227: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2455100 executing computations on platform CUDA. Devices:
2019-08-18 04:53:16.084262: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2019-08-18 04:53:16.086194: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-08-18 04:53:16.086460: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x65e6380 executing computations on platform Host. Devices:
2019-08-18 04:53:16.086508: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-08-18 04:53:16.086686: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.087039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
2019-08-18 04:53:16.087321: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.088548: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:16.089621: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-08-18 04:53:16.089935: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-08-18 04:53:16.091399: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-08-18 04:53:16.092394: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-08-18 04:53:16.095470: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-08-18 04:53:16.095606: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096009: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-08-18 04:53:16.096401: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.097166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-18 04:53:16.097190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-08-18 04:53:16.097201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-08-18 04:53:16.097482: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.097878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.098226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14089 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)
"get_model_by_type" model Type is: linear
W0818 04:53:16.444941 140399927523200 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
training with model type <class 'donkeycar.parts.keras.KerasLinear'>
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
img_in (InputLayer)             [(None, 120, 160, 3) 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 58, 78, 24)   1824        img_in[0][0]                     
__________________________________________________________________________________________________
dropout (Dropout)               (None, 58, 78, 24)   0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 27, 37, 32)   19232       dropout[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 27, 37, 32)   0           conv2d_2[0][0]                   
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 12, 17, 64)   51264       dropout_1[0][0]                  
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 12, 17, 64)   0           conv2d_3[0][0]                   
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 10, 15, 64)   36928       dropout_2[0][0]                  
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 10, 15, 64)   0           conv2d_4[0][0]                   
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 8, 13, 64)    36928       dropout_3[0][0]                  
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 8, 13, 64)    0           conv2d_5[0][0]                   
__________________________________________________________________________________________________
flattened (Flatten)             (None, 6656)         0           dropout_4[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 100)          665700      flattened[0][0]                  
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 100)          0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 50)           5050        dropout_5[0][0]                  
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 50)           0           dense_1[0][0]                    
__________________________________________________________________________________________________
n_outputs0 (Dense)              (None, 1)            51          dropout_6[0][0]                  
__________________________________________________________________________________________________
n_outputs1 (Dense)              (None, 1)            51          dropout_6[0][0]                  
==================================================================================================
Total params: 817,028
Trainable params: 817,028
Non-trainable params: 0
__________________________________________________________________________________________________
None
found 0 pickles writing json records and images in tub /content/mycar/data/tub_3_19-08-17
/content/mycar/data/tub_3_19-08-17
collating 5799 records ...
train: 4639, val: 1160
total records: 5799
steps_per_epoch 36
Epoch 1/100
2019-08-18 04:53:19.663907: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:19.984999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
35/36 [============================>.] - ETA: 0s - loss: 0.1681 - n_outputs0_loss: 0.1555 - n_outputs1_loss: 0.0126
Epoch 00001: val_loss improved from inf to 0.14482, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 8s 213ms/step - loss: 0.1677 - n_outputs0_loss: 0.1553 - n_outputs1_loss: 0.0124 - val_loss: 0.1448 - val_n_outputs0_loss: 0.1409 - val_n_outputs1_loss: 0.0039
Epoch 2/100
34/36 [===========================>..] - ETA: 0s - loss: 0.1278 - n_outputs0_loss: 0.1234 - n_outputs1_loss: 0.0044
Epoch 00002: val_loss improved from 0.14482 to 0.08814, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.1257 - n_outputs0_loss: 0.1214 - n_outputs1_loss: 0.0043 - val_loss: 0.0881 - val_n_outputs0_loss: 0.0870 - val_n_outputs1_loss: 0.0012
Epoch 3/100
35/36 [============================>.] - ETA: 0s - loss: 0.0943 - n_outputs0_loss: 0.0910 - n_outputs1_loss: 0.0033
Epoch 00003: val_loss improved from 0.08814 to 0.07490, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0938 - n_outputs0_loss: 0.0905 - n_outputs1_loss: 0.0033 - val_loss: 0.0749 - val_n_outputs0_loss: 0.0738 - val_n_outputs1_loss: 0.0011
Epoch 4/100
35/36 [============================>.] - ETA: 0s - loss: 0.0840 - n_outputs0_loss: 0.0813 - n_outputs1_loss: 0.0027
Epoch 00004: val_loss improved from 0.07490 to 0.07108, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 51ms/step - loss: 0.0835 - n_outputs0_loss: 0.0808 - n_outputs1_loss: 0.0027 - val_loss: 0.0711 - val_n_outputs0_loss: 0.0702 - val_n_outputs1_loss: 8.9668e-04
Epoch 5/100
35/36 [============================>.] - ETA: 0s - loss: 0.0767 - n_outputs0_loss: 0.0741 - n_outputs1_loss: 0.0026
Epoch 00005: val_loss did not improve from 0.07108
36/36 [==============================] - 2s 50ms/step - loss: 0.0765 - n_outputs0_loss: 0.0739 - n_outputs1_loss: 0.0026 - val_loss: 0.0722 - val_n_outputs0_loss: 0.0717 - val_n_outputs1_loss: 5.4629e-04
Epoch 6/100
35/36 [============================>.] - ETA: 0s - loss: 0.0750 - n_outputs0_loss: 0.0727 - n_outputs1_loss: 0.0023
Epoch 00006: val_loss improved from 0.07108 to 0.06483, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0749 - n_outputs0_loss: 0.0725 - n_outputs1_loss: 0.0024 - val_loss: 0.0648 - val_n_outputs0_loss: 0.0641 - val_n_outputs1_loss: 7.5336e-04
Epoch 7/100
35/36 [============================>.] - ETA: 0s - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0022
Epoch 00007: val_loss improved from 0.06483 to 0.06324, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0021 - val_loss: 0.0632 - val_n_outputs0_loss: 0.0626 - val_n_outputs1_loss: 5.9058e-04
Epoch 8/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00008: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0644 - val_n_outputs0_loss: 0.0637 - val_n_outputs1_loss: 7.8261e-04
Epoch 9/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00009: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 50ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0653 - val_n_outputs0_loss: 0.0646 - val_n_outputs1_loss: 6.6657e-04
Epoch 10/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0651 - n_outputs0_loss: 0.0633 - n_outputs1_loss: 0.0018
Epoch 00010: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0657 - n_outputs0_loss: 0.0639 - n_outputs1_loss: 0.0018 - val_loss: 0.0699 - val_n_outputs0_loss: 0.0692 - val_n_outputs1_loss: 6.9729e-04
Epoch 11/100
35/36 [============================>.] - ETA: 0s - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018
Epoch 00011: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018 - val_loss: 0.0633 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 0.0014
Epoch 12/100
35/36 [============================>.] - ETA: 0s - loss: 0.0643 - n_outputs0_loss: 0.0626 - n_outputs1_loss: 0.0016
Epoch 00012: val_loss improved from 0.06324 to 0.06034, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0644 - n_outputs0_loss: 0.0628 - n_outputs1_loss: 0.0017 - val_loss: 0.0603 - val_n_outputs0_loss: 0.0598 - val_n_outputs1_loss: 4.9525e-04
Epoch 13/100
35/36 [============================>.] - ETA: 0s - loss: 0.0611 - n_outputs0_loss: 0.0597 - n_outputs1_loss: 0.0014
Epoch 00013: val_loss improved from 0.06034 to 0.05636, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0613 - n_outputs0_loss: 0.0599 - n_outputs1_loss: 0.0014 - val_loss: 0.0564 - val_n_outputs0_loss: 0.0557 - val_n_outputs1_loss: 6.3083e-04
Epoch 14/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0593 - n_outputs0_loss: 0.0580 - n_outputs1_loss: 0.0013
Epoch 00014: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0590 - n_outputs0_loss: 0.0577 - n_outputs1_loss: 0.0013 - val_loss: 0.0580 - val_n_outputs0_loss: 0.0575 - val_n_outputs1_loss: 5.4033e-04
Epoch 15/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0552 - n_outputs0_loss: 0.0540 - n_outputs1_loss: 0.0012
Epoch 00015: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0555 - n_outputs0_loss: 0.0542 - n_outputs1_loss: 0.0012 - val_loss: 0.0565 - val_n_outputs0_loss: 0.0559 - val_n_outputs1_loss: 5.6679e-04
Epoch 16/100
35/36 [============================>.] - ETA: 0s - loss: 0.0538 - n_outputs0_loss: 0.0527 - n_outputs1_loss: 0.0012
Epoch 00016: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 50ms/step - loss: 0.0538 - n_outputs0_loss: 0.0526 - n_outputs1_loss: 0.0012 - val_loss: 0.0624 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 5.0014e-04
Epoch 17/100
35/36 [============================>.] - ETA: 0s - loss: 0.0536 - n_outputs0_loss: 0.0525 - n_outputs1_loss: 0.0011
Epoch 00017: val_loss improved from 0.05636 to 0.05615, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.0534 - n_outputs0_loss: 0.0523 - n_outputs1_loss: 0.0011 - val_loss: 0.0561 - val_n_outputs0_loss: 0.0556 - val_n_outputs1_loss: 5.2067e-04
Epoch 18/100
35/36 [============================>.] - ETA: 0s - loss: 0.0502 - n_outputs0_loss: 0.0492 - n_outputs1_loss: 0.0011
Epoch 00018: val_loss improved from 0.05615 to 0.05594, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0501 - n_outputs0_loss: 0.0490 - n_outputs1_loss: 0.0011 - val_loss: 0.0559 - val_n_outputs0_loss: 0.0555 - val_n_outputs1_loss: 4.4089e-04
Epoch 00018: early stopping
Training completed in 0:00:40.


----------- Best Eval Loss :0.055939 ---------
<Figure size 640x480 with 1 Axes>

显示训练结果的Loss曲线

In [26]:

import matplotlib.pyplot as plt

list_of_png = glob.glob('/content/mycar/models/*png')

latest_png = max(list_of_png, key=os.path.getctime)

image = cv2.imread(latest_png)

/content/mycar/models/mypilot.h5_loss_acc_0.055939.png

Out[26]:

<matplotlib.image.AxesImage at 0x7f867c896be0>

robocarstore/173807730625008222

将训练好的模型放回 Donkey Car.

一旦模型训练完毕,你可以在 mycar/models中找到训练好的模型
1.下载mypilot文件到你到PC或Mac

  1. 再从PC或Mac拷贝模型到你到pi

最后用Autopilot mode来驾驶Donkey car

Enjoy！

Use Colab to Accelerate Donkey Car Training (Tensorflow GPU)

colab

This notebook helps you quickly train your Donkey car or autonomous car model.

Reference and improved the notebook of @sachindroid8, thanks to the previous generation!

The Pros and Cons of Using Colab for Training

  1. Efficiency

Using Colab requires a VPN, and also needs to upload data to Google, the upload time varies from person to person, I uploaded it for about 2-3 minutes, the rest is executing the code and training. The first execution of the code needs to understand what the principle is, and the subsequent execution basically does not take much time, but the training time using GPU, I have 6k images, training about 30-40 epochs early, the training time is less than 1 minute! Each Epoch only needs 1-2 seconds.

Then using my Macbook Pro training, because of the lack of GPU, each Epoch needs 30-50 seconds, training down, it takes about 40 minutes to 1 hour to complete.

  1. Convenience

If you don't have a VPN, of course, Colab is not a choice, I know Baidu also provides some limited free servers, which can be tested later, but with a VPN, Google Colab is the best choice. Below is how to start training

Load My Notebook

Open the Using Google_Colab to Donkey_Car

Install TensorFlow 1.14.0

TensorFlow 2.x and Donkey Car 3.x still have compatibility issues, so it is not recommended to use them temporarily (2019.8.17)In [1]:

!pip install tensorflow-gpu==1.14.0

Collecting tensorflow-gpu==1.14.0
  Downloading https://files.pythonhosted.org/packages/76/04/43153bfdfcf6c9a4c38ecdb971ca9a75b9a791bb69a764d652c359aca504/tensorflow_gpu-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (377.0MB)
     |████████████████████████████████| 377.0MB 86kB/s 
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.15.0)
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.7.1)
Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.11.2)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.8.0)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.0.8)
Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.33.4)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (3.7.1)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.12.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.1.7)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.2.2)
Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.16.4)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.14.0) (2.8.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (3.1.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (0.15.5)
Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (41.0.1)
Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.14.0

Check if GPU is working

If you see “Found GPU at: / device: GPU: 0”, then the GPU is working normally

If you don't see the above output, you need to check if the Runtime (Run Type) has selected the GPU hardware acceleratorIn [2]:

device_name = tf.test.gpu_device_name()

if device_name != '/device:GPU:0':

raise SystemError('GPU device not found')

print('Found GPU at: {}'.format(device_name))

Found GPU at: /device:GPU:0

克隆Donkey respository

In [3]:

!git clone https://github.com/autorope/donkeycar.git donkey

Cloning into 'donkey'...
remote: Enumerating objects: 120, done.
remote: Counting objects: 100% (120/120), done.
remote: Compressing objects: 100% (76/76), done.
remote: Total 10558 (delta 65), reused 73 (delta 31), pack-reused 10438
Receiving objects: 100% (10558/10558), 58.74 MiB | 46.70 MiB/s, done.
Resolving deltas: 100% (6528/6528), done.

Install Donkey car

In [4]:

Obtaining file:///content/donkey
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (1.16.4)
Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.3.0)
Requirement already satisfied: docopt in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.6.2)
Requirement already satisfied: tornado in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.5.3)
Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.21.0)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.8.0)
Requirement already satisfied: moviepy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.2.3.5)
Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.24.2)
Requirement already satisfied: PrettyTable in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.7.2)
Collecting paho-mqtt (from donkeycar==3.1.0)
  Downloading https://files.pythonhosted.org/packages/25/63/db25e62979c2a716a74950c9ed658dce431b5cb01fde29eb6cba9489a904/paho-mqtt-1.4.0.tar.gz (88kB)
     |████████████████████████████████| 92kB 4.2MB/s 
Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->donkeycar==3.1.0) (0.46)
Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (1.24.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (2019.6.16)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (3.0.4)
Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (2.8)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py->donkeycar==3.1.0) (1.12.0)
Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (4.4.0)
Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (4.28.1)
Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (2.4.1)
Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2018.9)
Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2.5.3)
Building wheels for collected packages: paho-mqtt
  Building wheel for paho-mqtt (setup.py) ... done
  Created wheel for paho-mqtt: filename=paho_mqtt-1.4.0-cp36-none-any.whl size=48333 sha256=9a67d0c95fae2b9495c20980895d9569ef53859cbc22bb57fc2466d7a581af7b
  Stored in directory: /root/.cache/pip/wheels/82/e5/de/d90d0f397648a1b58ffeea1b5742ac8c77f71fd43b550fa5a5
Successfully built paho-mqtt
Installing collected packages: paho-mqtt, donkeycar
  Running setup.py develop for donkeycar
Successfully installed donkeycar paho-mqtt-1.4.0

Create a project

I used mycar as the project name, you can change it, but you need to modify the code after renamingIn [5]:

!donkey createcar --path /content/mycar

using donkey v3.1.0 ...
Creating car folder: /content/mycar
making dir  /content/mycar
Creating data & model folders.
making dir  /content/mycar/models
making dir  /content/mycar/data
making dir  /content/mycar/logs
Copying car application template: complete
Copying car config defaults. Adjust these before starting your car.
Copying train script. Adjust these before starting your car.
Copying my car config overrides
Donkey setup complete.

Prepare data: Upload data.zip and unzip

Now you need to package the data directory that you collected from pi, save it as data.zip.

Run the following command on the pi data directory:

$ zip -r data.zip tub_3_19-08-17/

Then copy it back to your computer, ready to upload to Colab

Upload data.zip to Colab

Run the following code, you will see an upload button, click to upload the data.zip you just packagedIn [7]:

from google.colab import files

if(os.path.exists("/content/data.zip")):

os.remove("/content/data.zip")

if(os.path.exists("/content/mycar/data/data.zip")):

os.remove("/content/mycar/data/data.zip")

uploaded = files.upload()

WORK_FOLDER = "/content/mycar/data/"

if(os.path.exists(WORK_FOLDER) == False):

!mv /content/data.zip /content/mycar/data/

Clean up the uploaded files

You need to ensure that the content/mycar/data directory has a tub directory, and the directory contains images and corresponding json files

data.zip is not neededIn [0]:

!rm /content/mycar/data/data.zip

Train the model

In [21]:

!python /content/mycar/manage.py train --model /content/mycar/models/mypilot.h5

using donkey v3.1.0 ...
loading config file: /content/mycar/config.py
loading personal config over-rides

config loaded
WARNING: Logging before flag parsing goes to stderr.
W0818 04:53:15.944086 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0818 04:53:15.944333 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2019-08-18 04:53:15.954857: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-08-18 04:53:15.959505: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-08-18 04:53:16.083690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.084227: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2455100 executing computations on platform CUDA. Devices:
2019-08-18 04:53:16.084262: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2019-08-18 04:53:16.086194: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-08-18 04:53:16.086460: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x65e6380 executing computations on platform Host. Devices:
2019-08-18 04:53:16.086508: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-08-18 04:53:16.086686: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.087039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
2019-08-18 04:53:16.087321: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.088548: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:16.089621: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-08-18 04:53:16.089935: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-08-18 04:53:16.091399: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-08-18 04:53:16.092394: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-08-18 04:53:16.095470: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-08-18 04:53:16.095606: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096009: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-08-18 04:53:16.096401: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.097166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-18 04:53:16.097190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-08-18 04:53:16.097201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-08-18 04:53:16.097482: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.097878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.098226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14089 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)
"get_model_by_type" model Type is: linear
W0818 04:53:16.444941 140399927523200 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
training with model type <class 'donkeycar.parts.keras.KerasLinear'>
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
img_in (InputLayer)             [(None, 120, 160, 3) 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 58, 78, 24)   1824        img_in[0][0]                     
__________________________________________________________________________________________________
dropout (Dropout)               (None, 58, 78, 24)   0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 27, 37, 32)   19232       dropout[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 27, 37, 32)   0           conv2d_2[0][0]                   
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 12, 17, 64)   51264       dropout_1[0][0]                  
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 12, 17, 64)   0           conv2d_3[0][0]                   
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 10, 15, 64)   36928       dropout_2[0][0]                  
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 10, 15, 64)   0           conv2d_4[0][0]                   
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 8, 13, 64)    36928       dropout_3[0][0]                  
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 8, 13, 64)    0           conv2d_5[0][0]                   
__________________________________________________________________________________________________
flattened (Flatten)             (None, 6656)         0           dropout_4[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 100)          665700      flattened[0][0]                  
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 100)          0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 50)           5050        dropout_5[0][0]                  
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 50)           0           dense_1[0][0]                    
__________________________________________________________________________________________________
n_outputs0 (Dense)              (None, 1)            51          dropout_6[0][0]                  
__________________________________________________________________________________________________
n_outputs1 (Dense)              (None, 1)            51          dropout_6[0][0]                  
==================================================================================================
Total params: 817,028
Trainable params: 817,028
Non-trainable params: 0
__________________________________________________________________________________________________
None
found 0 pickles writing json records and images in tub /content/mycar/data/tub_3_19-08-17
/content/mycar/data/tub_3_19-08-17
collating 5799 records ...
train: 4639, val: 1160
total records: 5799
steps_per_epoch 36
Epoch 1/100
2019-08-18 04:53:19.663907: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:19.984999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
35/36 [============================>.] - ETA: 0s - loss: 0.1681 - n_outputs0_loss: 0.1555 - n_outputs1_loss: 0.0126
Epoch 00001: val_loss improved from inf to 0.14482, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 8s 213ms/step - loss: 0.1677 - n_outputs0_loss: 0.1553 - n_outputs1_loss: 0.0124 - val_loss: 0.1448 - val_n_outputs0_loss: 0.1409 - val_n_outputs1_loss: 0.0039
Epoch 2/100
34/36 [===========================>..] - ETA: 0s - loss: 0.1278 - n_outputs0_loss: 0.1234 - n_outputs1_loss: 0.0044
Epoch 00002: val_loss improved from 0.14482 to 0.08814, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.1257 - n_outputs0_loss: 0.1214 - n_outputs1_loss: 0.0043 - val_loss: 0.0881 - val_n_outputs0_loss: 0.0870 - val_n_outputs1_loss: 0.0012
Epoch 3/100
35/36 [============================>.] - ETA: 0s - loss: 0.0943 - n_outputs0_loss: 0.0910 - n_outputs1_loss: 0.0033
Epoch 00003: val_loss improved from 0.08814 to 0.07490, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0938 - n_outputs0_loss: 0.0905 - n_outputs1_loss: 0.0033 - val_loss: 0.0749 - val_n_outputs0_loss: 0.0738 - val_n_outputs1_loss: 0.0011
Epoch 4/100
35/36 [============================>.] - ETA: 0s - loss: 0.0840 - n_outputs0_loss: 0.0813 - n_outputs1_loss: 0.0027
Epoch 00004: val_loss improved from 0.07490 to 0.07108, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 51ms/step - loss: 0.0835 - n_outputs0_loss: 0.0808 - n_outputs1_loss: 0.0027 - val_loss: 0.0711 - val_n_outputs0_loss: 0.0702 - val_n_outputs1_loss: 8.9668e-04
Epoch 5/100
35/36 [============================>.] - ETA: 0s - loss: 0.0767 - n_outputs0_loss: 0.0741 - n_outputs1_loss: 0.0026
Epoch 00005: val_loss did not improve from 0.07108
36/36 [==============================] - 2s 50ms/step - loss: 0.0765 - n_outputs0_loss: 0.0739 - n_outputs1_loss: 0.0026 - val_loss: 0.0722 - val_n_outputs0_loss: 0.0717 - val_n_outputs1_loss: 5.4629e-04
Epoch 6/100
35/36 [============================>.] - ETA: 0s - loss: 0.0750 - n_outputs0_loss: 0.0727 - n_outputs1_loss: 0.0023
Epoch 00006: val_loss improved from 0.07108 to 0.06483, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0749 - n_outputs0_loss: 0.0725 - n_outputs1_loss: 0.0024 - val_loss: 0.0648 - val_n_outputs0_loss: 0.0641 - val_n_outputs1_loss: 7.5336e-04
Epoch 7/100
35/36 [============================>.] - ETA: 0s - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0022
Epoch 00007: val_loss improved from 0.06483 to 0.06324, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0021 - val_loss: 0.0632 - val_n_outputs0_loss: 0.0626 - val_n_outputs1_loss: 5.9058e-04
Epoch 8/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00008: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0644 - val_n_outputs0_loss: 0.0637 - val_n_outputs1_loss: 7.8261e-04
Epoch 9/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00009: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 50ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0653 - val_n_outputs0_loss: 0.0646 - val_n_outputs1_loss: 6.6657e-04
Epoch 10/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0651 - n_outputs0_loss: 0.0633 - n_outputs1_loss: 0.0018
Epoch 00010: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0657 - n_outputs0_loss: 0.0639 - n_outputs1_loss: 0.0018 - val_loss: 0.0699 - val_n_outputs0_loss: 0.0692 - val_n_outputs1_loss: 6.9729e-04
Epoch 11/100
35/36 [============================>.] - ETA: 0s - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018
Epoch 00011: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018 - val_loss: 0.0633 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 0.0014
Epoch 12/100
35/36 [============================>.] - ETA: 0s - loss: 0.0643 - n_outputs0_loss: 0.0626 - n_outputs1_loss: 0.0016
Epoch 00012: val_loss improved from 0.06324 to 0.06034, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0644 - n_outputs0_loss: 0.0628 - n_outputs1_loss: 0.0017 - val_loss: 0.0603 - val_n_outputs0_loss: 0.0598 - val_n_outputs1_loss: 4.9525e-04
Epoch 13/100
35/36 [============================>.] - ETA: 0s - loss: 0.0611 - n_outputs0_loss: 0.0597 - n_outputs1_loss: 0.0014
Epoch 00013: val_loss improved from 0.06034 to 0.05636, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0613 - n_outputs0_loss: 0.0599 - n_outputs1_loss: 0.0014 - val_loss: 0.0564 - val_n_outputs0_loss: 0.0557 - val_n_outputs1_loss: 6.3083e-04
Epoch 14/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0593 - n_outputs0_loss: 0.0580 - n_outputs1_loss: 0.0013
Epoch 00014: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0590 - n_outputs0_loss: 0.0577 - n_outputs1_loss: 0.0013 - val_loss: 0.0580 - val_n_outputs0_loss: 0.0575 - val_n_outputs1_loss: 5.4033e-04
Epoch 15/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0552 - n_outputs0_loss: 0.0540 - n_outputs1_loss: 0.0012
Epoch 00015: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0555 - n_outputs0_loss: 0.0542 - n_outputs1_loss: 0.0012 - val_loss: 0.0565 - val_n_outputs0_loss: 0.0559 - val_n_outputs1_loss: 5.6679e-04
Epoch 16/100
35/36 [============================>.] - ETA: 0s - loss: 0.0538 - n_outputs0_loss: 0.0527 - n_outputs1_loss: 0.0012
Epoch 00016: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 50ms/step - loss: 0.0538 - n_outputs0_loss: 0.0526 - n_outputs1_loss: 0.0012 - val_loss: 0.0624 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 5.0014e-04
Epoch 17/100
35/36 [============================>.] - ETA: 0s - loss: 0.0536 - n_outputs0_loss: 0.0525 - n_outputs1_loss: 0.0011
Epoch 00017: val_loss improved from 0.05636 to 0.05615, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.0534 - n_outputs0_loss: 0.0523 - n_outputs1_loss: 0.0011 - val_loss: 0.0561 - val_n_outputs0_loss: 0.0556 - val_n_outputs1_loss: 5.2067e-04
Epoch 18/100
35/36 [============================>.] - ETA: 0s - loss: 0.0502 - n_outputs0_loss: 0.0492 - n_outputs1_loss: 0.0011
Epoch 00018: val_loss improved from 0.05615 to 0.05594, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0501 - n_outputs0_loss: 0.0490 - n_outputs1_loss: 0.0011 - val_loss: 0.0559 - val_n_outputs0_loss: 0.0555 - val_n_outputs1_loss: 4.4089e-04
Epoch 00018: early stopping
Training completed in 0:00:40.


----------- Best Eval Loss :0.055939 ---------
<Figure size 640x480 with 1 Axes>

Display the Loss curve of the training result

In [26]:

import matplotlib.pyplot as plt

list_of_png = glob.glob('/content/mycar/models/*png')

latest_png = max(list_of_png, key=os.path.getctime)

image = cv2.imread(latest_png)

/content/mycar/models/mypilot.h5_loss_acc_0.055939.png

Out[26]:

<matplotlib.image.AxesImage at 0x7f867c896be0>

robocarstore/173807730625008222

Put the trained model back into Donkey Car.

Once the model is trained, you can find the trained model in the mycar/models directory
1. Download the mypilot file to your PC or Mac

  1. Copy the model from your PC or Mac to your pi

Finally, use the Autopilot mode to drive Donkey car

Enjoy!

给大家介绍一下！Donkey Car

Donkey Car

Donkey Car，是一款开源的小型DIY自驾平台，这可能是你最佳的AI入门学习平台。Donkey是用Python写的高级自动驾驶库，它的特点是让你可以快速学习机器学习，快速实验，综合应用机器视觉，机器学习工具(例如：tornado, keras, tensorflow, opencv, ….)

什么是Donkey Car？

Donkey Car是一个基于Python的开源项目，旨在帮助用户构建和训练自动驾驶汽车。它的设计初衷是让用户能够在家中或实验室中轻松地进行自动驾驶技术的实验。Donkey Car的硬件部分通常由一个小型遥控车、Raspberry Pi、摄像头和电池组成。软件部分则是一个强大的Python库，支持多种机器学习框架。

为什么选择Donkey Car？

  1. 易于上手：Donkey Car的设计非常直观，即使是没有编程经验的人也可以快速上手。它提供了详细的文档和教程，帮助用户一步步完成从硬件组装到软件配置的全过程。

  2. 社区支持：作为一个开源项目，Donkey Car拥有一个活跃的社区。无论你遇到什么问题，都可以在社区中找到答案或寻求帮助。

  3. 灵活性：Donkey Car支持多种机器学习框架，如TensorFlow和Keras，用户可以根据自己的需求选择合适的工具进行实验。

  4. 成本低廉：相比于其他自动驾驶平台，Donkey Car的成本相对较低，非常适合个人爱好者和教育机构使用。

如何开始？

要开始使用Donkey Car，你需要准备以下材料：

  • 一辆小型遥控车
  • Raspberry Pi
  • 摄像头
  • 电池
  • 3D打印的车架（可选）

接下来，你可以按照官方文档的指导进行硬件组装和软件安装。Donkey Car的官方GitHub页面提供了详细的安装步骤和示例代码，帮助你快速搭建自己的自动驾驶汽车。

结论

Donkey Car是一个非常适合AI初学者的项目。通过这个平台，你可以学习到自动驾驶的基本原理，掌握机器学习和计算机视觉的基础知识。无论你是想要进入AI领域，还是只是对自动驾驶技术感兴趣，Donkey Car都将是一个理想的起点。

希望这篇文章能帮助你更好地了解Donkey Car，并激发你对自动驾驶技术的兴趣。快来加入Donkey Car社区，开始你的AI学习之旅吧！

Donkey Car详解(1) – 组装

Donkey Car

概述

Donkey Car是2016年10月由美国人Adam Conway(Twitter)主导的一个人工智能自动驾驶模型车项目，由于近几年来人工智能技术和自动驾驶技术逐渐兴起，这个自动驾驶项目也受到越来越多爱好者关注和加入，逐渐形成了DonkeyCar社群。

本教程目标是帮助DonkeyCar初学者快速上手，如果有问题或建议，欢迎拍砖。

选择车架

最初的Donkey Car是通过改装“Exceed”牌1/16四驱玩具车而成，官方文档推荐一下四个型号：

以上四款小车的电子驱动模块都是一样的，它们但区别在于轮胎和安装顶盖的方式。需要提醒的是Desert Monster，Short Course Truck和Blaze需要增加一个适配器来固定Donkey Car主板和把手，可以在玩具店购买或者自己打印。因为这些小车都是标准配置，基本上是即插即用的。这些小车都有有刷电机版本和无刷电机版本，作者说使用有刷电机版的车架更容易训练，因为有刷电机更适合在粗糙的路面驾驶，也更便宜。

后来玩具们开始改装1/10的玩具车，1/10的车子变得更大，升级成Doneky Car专业版。当然专业版的Donkey Car性能更好，价格也更贵，以下是支持的型号：

  • HobbyKing Trooper (非专业版) 链接
  • HobbyKing Mission-D 链接
  • Tamaya TT01或者它的山寨版，这款容易购买，但需要散件安装，有一定难度。

因为完专业版的DonkeyCar的人比普通版的少，因为专业版存在文档不足的问题，如果你要开始完Donkey Car Pro，你应该具备一定的动手能力和更多的耐性。

所需零件

根据不同主板的支持，Donkey Car有两个版本，一款是使用树莓派(Raspberry Pi)的Donkey Car，另一款是使用Jetson Nano的Donkey Car。两个版本除了主板不同，所需的配件和软件的安装也是不一样的。

选项1 – 你可以通过Donkey Car官方渠道购买

如果你在美国，可以到原作者的Donkey store商店购买。

如果你在亚洲，你可以选择香港的Robocar Store，或者中国大陆的Robocar Store China购买。

官方套件或整车包括

零件大约成本
车架（来自上面的玩具车）大约¥600
电池及充电器大约¥120
树莓派 3b+大约¥300
128G SD卡大约¥120
其他配件大约¥600 – ¥875

选项2 – 你可以通过零件清单购买

如果你需要自己购买零部件，你可以参考作者的链接。值得提醒的是，如果你要自己组装，是需要通过3D打印机打印Donkey Car的零部件。如果你没有3D打印机，可以到红菜3D选购一台。

具体要准备的打印零部件，可以参考官方文档，链接。

Jetson Nano升级

如果你想你小车跑得更快，你可以选择具有GPU的Jetson Nano作为主板，除了需要换主板意外，你还需要另外准备一个Jetson Nano的主板适配器还有Jetson Nano的网卡和Jetson Nano适用的摄像头(可参考这篇文章)

JetsonNano适配器

Jetson Nano 适配器

JesonNano扩展板

注意Jetson Nano的pin口布局和树莓派相反，使用其他扩展板时要注意。

电池

我们强烈建议使用聚合物锂电池，因为聚合物锂电池的能量密度更高，而且电压下降曲线更平滑，因而电压更稳定。

Battery

硬件

如果你从官方商店购买Donkey Car，你可以跳过前两步。

第一步 打印零部件

如果你没有3D打印机，可以在我们的商店购买打印好的零部件，或者寻找第三方打印。如果使用 PLA打印，层高设置可以选择2mm，填充率30%，打印一套零部件，估计需要2天时间左右，这是模型的下载链接。

第二步 清理零部件

所有的3D打印件都需要做一点清理，包括重新钻孔，清理拉丝等。

robocarstore/17380757722502808

robocarstore/17380757862515729

第三步 安装底板和手提支架

安装步骤是相当简单的，你只需要使用M3螺丝把底板和手提把手连接起来。你要确保螺丝上紧了，因为你会使用手提支架提起小车。

第四步 连接舵机控制器

你可以把电路板安装上底板上再做连线，连线入下图所示。

robocarstore/173807580225062010

robocarstore/173807581725157711

这是树莓派的布局图，你需要连接3.3v、两个I2C pin (SDA和SCL) 和 Ground(接地)

第五步 将树莓派安装到底板上

在这个时候，你可以把已经烧录好的SD卡插入树莓派卡槽。然后把树莓派和舵机连通过螺丝安装到底板上。这时需要使用M2.6的螺丝。

（加图片）

第六步 安装摄像头

先把摄像头的数据线连接好，然后安装到手提支架到前方，如下图：

注意树莓派到安装方向，如果摄像头前有薄膜保护，记得要把保护膜撕掉以免影响训练效果。

第七步 安装到车架上

使用R型扣把底板固定在车架上，然后连接上电源线。

robocarstore/173807583625096212

恭喜你，你的小车安装完成！

要驱动你的小车，你就需要继续参考下一篇文章软件安装部分

Introducing Donkey Car!

Donkey Car

Donkey Car is an open-source, small-scale DIY autonomous driving platform, and it might be your best entry-level AI learning platform. Donkey is a high-level autonomous driving library written in Python, characterized by its ability to help you quickly learn machine learning, experiment rapidly, and integrate applications of computer vision and machine learning tools (such as tornado, keras, tensorflow, opencv, etc.).

What is Donkey Car?

Donkey Car is a Python-based open-source project designed to help users build and train autonomous vehicles. It is intended to allow users to easily experiment with autonomous driving technology at home or in a lab. The hardware part of Donkey Car typically consists of a small remote-controlled car, a Raspberry Pi, a camera, and a battery. The software part is a powerful Python library that supports multiple machine learning frameworks.

Why Choose Donkey Car?

  1. Easy to Get Started: Donkey Car is designed to be very intuitive, allowing even those without programming experience to get started quickly. It provides detailed documentation and tutorials to guide users through the entire process from hardware assembly to software configuration.

  2. Community Support: As an open-source project, Donkey Car has an active community. Whatever issues you encounter, you can find answers or seek help within the community.

  3. Flexibility: Donkey Car supports multiple machine learning frameworks, such as TensorFlow and Keras, allowing users to choose the right tools for their experiments based on their needs.

  4. Cost-Effective: Compared to other autonomous driving platforms, Donkey Car is relatively low-cost, making it ideal for hobbyists and educational institutions.

How to Get Started?

To start using Donkey Car, you need to prepare the following materials:

  • A small remote-controlled car
  • Raspberry Pi
  • Camera
  • Battery
  • 3D printed car frame (optional)

Next, you can follow the official documentation to assemble the hardware and install the software. Donkey Car's official GitHub page provides detailed installation steps and example code to help you quickly build your own autonomous vehicle.

Conclusion

Donkey Car is a project well-suited for AI beginners. Through this platform, you can learn the basic principles of autonomous driving and master the fundamentals of machine learning and computer vision. Whether you want to enter the AI field or are simply interested in autonomous driving technology, Donkey Car is an ideal starting point.

I hope this article helps you better understand Donkey Car and inspires your interest in autonomous driving technology. Join the Donkey Car community and start your AI learning journey today!

Donkey Car Detailed Explanation (1) – Assembly

Donkey Car

Overview

Donkey Car is an AI autonomous driving model car project led by Adam Conway (Twitter) since October 2016. With the rise of AI and autonomous driving technologies in recent years, this project has attracted more enthusiasts, forming the DonkeyCar community.

This tutorial aims to help DonkeyCar beginners get started quickly. If you have any questions or suggestions, feel free to provide feedback.

Choosing a Chassis

The original Donkey Car was modified from the "Exceed" brand 1/16 four-wheel-drive toy car. The official documentation recommends the following models:

These cars have the same electronic drive modules, differing only in tires and body mounting methods. Note that the Desert Monster, Short Course Truck, and Blaze require an adapter to secure the Donkey Car motherboard and handle, which can be purchased or 3D printed. These cars are standard configurations, mostly plug-and-play. They come in brushed and brushless motor versions; the author suggests using the brushed motor version as it's easier to train, better for rough surfaces, and cheaper.

Later, enthusiasts began modifying 1/10 scale toy cars, making them larger and upgrading to the Donkey Car Pro version. The Pro version offers better performance but at a higher cost. Supported models include:

  • HobbyKing Trooper (non-Pro) Link
  • HobbyKing Mission-D Link
  • Tamaya TT01 or its clones, which are easy to buy but require assembly, posing some difficulty.

Fewer people use the Pro version due to documentation gaps, so if you start with Donkey Car Pro, you should have good hands-on skills and patience.

Required Parts

Donkey Car has two versions based on the mainboard: one using Raspberry Pi and the other using Jetson Nano. Apart from the mainboard, the required parts and software installation differ.

Option 1 – Purchase through Donkey Car official channels

If you're in the US, you can buy from the original author's Donkey store.

In Asia, you can choose Hong Kong's Robocar Store or mainland China's Robocar Store China.

Official kits or complete cars include:

PartApproximate Cost
Chassis (from the toy cars above)Approx. ¥600
Battery and ChargerApprox. ¥120
Raspberry Pi 3b+Approx. ¥300
128G SD CardApprox. ¥120
Other AccessoriesApprox. ¥600 – ¥875

Option 2 – Purchase parts individually

If you need to buy parts individually, refer to the author's links. Note that if you assemble it yourself, you'll need to 3D print Donkey Car parts. If you don't have a 3D printer, consider buying one from Beets3D.

For specific printed parts, refer to the official documentation, link.

Jetson Nano Upgrade

For faster performance, choose Jetson Nano with a GPU as the mainboard. Besides changing the mainboard, you'll need a Jetson Nano adapter, network card, and compatible camera (see this article).

JetsonNano Adapter

Jetson Nano Adapter

JetsonNano Expansion Board

Note that Jetson Nano's pin layout is opposite to Raspberry Pi's, so be careful when using other expansion boards.

Battery

We strongly recommend using lithium polymer batteries for their higher energy density and smoother voltage drop curve, providing more stable voltage.

Battery

Hardware

If you purchased Donkey Car from the official store, you can skip the first two steps.

Step 1: Print Parts

If you don't have a 3D printer, buy printed parts from our store or find a third-party printer. For PLA printing, set layer height to 2mm and infill to 30%. Printing a set of parts takes about 2 days. Here's the model download link.

Step 2: Clean Parts

All 3D printed parts need some cleaning, including re-drilling holes and removing strings.

robocarstore/17380757722502808

robocarstore/17380757862515729

Step 3: Install Baseplate and Handle

The installation is straightforward; use M3 screws to connect the baseplate and handle. Ensure the screws are tight, as you'll use the handle to lift the car.

Step 4: Connect Servo Controller

Install the circuit board on the baseplate before wiring, as shown below.

robocarstore/173807580225062010

robocarstore/173807581725157711

This is the Raspberry Pi layout. Connect 3.3v, two I2C pins (SDA and SCL), and Ground.

Step 5: Install Raspberry Pi on Baseplate

At this point, insert the pre-burned SD card into the Raspberry Pi slot. Then, mount the Raspberry Pi and servo controller on the baseplate using screws. Use M2.6 screws.

(Add image)

Step 6: Install Camera

Connect the camera cable, then mount it on the front of the handle, as shown:

Ensure the Raspberry Pi is installed correctly. If the camera has a protective film, remove it to avoid affecting training results.

Step 7: Mount on Chassis

Use R-clips to secure the baseplate to the chassis, then connect the power cable.

robocarstore/173807583625096212

Congratulations, your car is assembled!

To drive your car, continue with the next article on software installation.

Introducing Donkey Car!

Donkey Car

Donkey Car is an open-source, small-scale DIY autonomous driving platform, and it might be your best entry-level AI learning platform. Donkey is a high-level autonomous driving library written in Python, characterized by its ability to help you quickly learn machine learning, experiment rapidly, and integrate applications of computer vision and machine learning tools (such as tornado, keras, tensorflow, opencv, etc.).

What is Donkey Car?

Donkey Car is a Python-based open-source project designed to help users build and train autonomous vehicles. It is intended to allow users to easily experiment with autonomous driving technology at home or in a lab. The hardware part of Donkey Car typically consists of a small remote-controlled car, a Raspberry Pi, a camera, and a battery. The software part is a powerful Python library that supports multiple machine learning frameworks.

Why Choose Donkey Car?

  1. Easy to Get Started: Donkey Car is designed to be very intuitive, allowing even those without programming experience to get started quickly. It provides detailed documentation and tutorials to guide users through the entire process from hardware assembly to software configuration.

  2. Community Support: As an open-source project, Donkey Car has an active community. Whatever issues you encounter, you can find answers or seek help within the community.

  3. Flexibility: Donkey Car supports multiple machine learning frameworks, such as TensorFlow and Keras, allowing users to choose the right tools for their experiments based on their needs.

  4. Cost-Effective: Compared to other autonomous driving platforms, Donkey Car is relatively low-cost, making it ideal for hobbyists and educational institutions.

How to Get Started?

To start using Donkey Car, you need to prepare the following materials:

  • A small remote-controlled car
  • Raspberry Pi
  • Camera
  • Battery
  • 3D printed car frame (optional)

Next, you can follow the official documentation to assemble the hardware and install the software. Donkey Car's official GitHub page provides detailed installation steps and example code to help you quickly build your own autonomous vehicle.

Conclusion

Donkey Car is a project well-suited for AI beginners. Through this platform, you can learn the basic principles of autonomous driving and master the fundamentals of machine learning and computer vision. Whether you want to enter the AI field or are simply interested in autonomous driving technology, Donkey Car is an ideal starting point.

I hope this article helps you better understand Donkey Car and inspires your interest in autonomous driving technology. Join the Donkey Car community and start your AI learning journey today!

Donkey Car Detailed Explanation (1) – Assembly

Donkey Car

Overview

Donkey Car is an AI autonomous driving model car project led by Adam Conway (Twitter) since October 2016. With the rise of AI and autonomous driving technologies in recent years, this project has attracted more enthusiasts, forming the DonkeyCar community.

This tutorial aims to help DonkeyCar beginners get started quickly. If you have any questions or suggestions, feel free to provide feedback.

Choosing a Chassis

The original Donkey Car was modified from the "Exceed" brand 1/16 four-wheel-drive toy car. The official documentation recommends the following models:

These cars have the same electronic drive modules, differing only in tires and body mounting methods. Note that the Desert Monster, Short Course Truck, and Blaze require an adapter to secure the Donkey Car motherboard and handle, which can be purchased or 3D printed. These cars are standard configurations, mostly plug-and-play. They come in brushed and brushless motor versions; the author suggests using the brushed motor version as it's easier to train, better for rough surfaces, and cheaper.

Later, enthusiasts began modifying 1/10 scale toy cars, making them larger and upgrading to the Donkey Car Pro version. The Pro version offers better performance but at a higher cost. Supported models include:

  • HobbyKing Trooper (non-Pro) Link
  • HobbyKing Mission-D Link
  • Tamaya TT01 or its clones, which are easy to buy but require assembly, posing some difficulty.

Fewer people use the Pro version due to documentation gaps, so if you start with Donkey Car Pro, you should have good hands-on skills and patience.

Required Parts

Donkey Car has two versions based on the mainboard: one using Raspberry Pi and the other using Jetson Nano. Apart from the mainboard, the required parts and software installation differ.

Option 1 – Purchase through Donkey Car official channels

If you're in the US, you can buy from the original author's Donkey store.

In Asia, you can choose Hong Kong's Robocar Store or mainland China's Robocar Store China.

Official kits or complete cars include:

PartApproximate Cost
Chassis (from the toy cars above)Approx. ¥600
Battery and ChargerApprox. ¥120
Raspberry Pi 3b+Approx. ¥300
128G SD CardApprox. ¥120
Other AccessoriesApprox. ¥600 – ¥875

Option 2 – Purchase parts individually

If you need to buy parts individually, refer to the author's links. Note that if you assemble it yourself, you'll need to 3D print Donkey Car parts. If you don't have a 3D printer, consider buying one from Beets3D.

For specific printed parts, refer to the official documentation, link.

Jetson Nano Upgrade

For faster performance, choose Jetson Nano with a GPU as the mainboard. Besides changing the mainboard, you'll need a Jetson Nano adapter, network card, and compatible camera (see this article).

JetsonNano Adapter

Jetson Nano Adapter

JetsonNano Expansion Board

Note that Jetson Nano's pin layout is opposite to Raspberry Pi's, so be careful when using other expansion boards.

Battery

We strongly recommend using lithium polymer batteries for their higher energy density and smoother voltage drop curve, providing more stable voltage.

Battery

Hardware

If you purchased Donkey Car from the official store, you can skip the first two steps.

Step 1: Print Parts

If you don't have a 3D printer, buy printed parts from our store or find a third-party printer. For PLA printing, set layer height to 2mm and infill to 30%. Printing a set of parts takes about 2 days. Here's the model download link.

Step 2: Clean Parts

All 3D printed parts need some cleaning, including re-drilling holes and removing strings.

robocarstore/17380757722502808

robocarstore/17380757862515729

Step 3: Install Baseplate and Handle

The installation is straightforward; use M3 screws to connect the baseplate and handle. Ensure the screws are tight, as you'll use the handle to lift the car.

Step 4: Connect Servo Controller

Install the circuit board on the baseplate before wiring, as shown below.

robocarstore/173807580225062010

robocarstore/173807581725157711

This is the Raspberry Pi layout. Connect 3.3v, two I2C pins (SDA and SCL), and Ground.

Step 5: Install Raspberry Pi on Baseplate

At this point, insert the pre-burned SD card into the Raspberry Pi slot. Then, mount the Raspberry Pi and servo controller on the baseplate using screws. Use M2.6 screws.

(Add image)

Step 6: Install Camera

Connect the camera cable, then mount it on the front of the handle, as shown:

Ensure the Raspberry Pi is installed correctly. If the camera has a protective film, remove it to avoid affecting training results.

Step 7: Mount on Chassis

Use R-clips to secure the baseplate to the chassis, then connect the power cable.

robocarstore/173807583625096212

Congratulations, your car is assembled!

To drive your car, continue with the next article on software installation.

给大家介绍一下！Donkey Car

Donkey Car

Donkey Car，是一款开源的小型DIY自驾平台，这可能是你最佳的AI入门学习平台。Donkey是用Python写的高级自动驾驶库，它的特点是让你可以快速学习机器学习，快速实验，综合应用机器视觉，机器学习工具(例如：tornado, keras, tensorflow, opencv, ….)

什么是Donkey Car？

Donkey Car是一个基于Python的开源项目，旨在帮助用户构建和训练自动驾驶汽车。它的设计初衷是让用户能够在家中或实验室中轻松地进行自动驾驶技术的实验。Donkey Car的硬件部分通常由一个小型遥控车、Raspberry Pi、摄像头和电池组成。软件部分则是一个强大的Python库，支持多种机器学习框架。

为什么选择Donkey Car？

  1. 易于上手：Donkey Car的设计非常直观，即使是没有编程经验的人也可以快速上手。它提供了详细的文档和教程，帮助用户一步步完成从硬件组装到软件配置的全过程。

  2. 社区支持：作为一个开源项目，Donkey Car拥有一个活跃的社区。无论你遇到什么问题，都可以在社区中找到答案或寻求帮助。

  3. 灵活性：Donkey Car支持多种机器学习框架，如TensorFlow和Keras，用户可以根据自己的需求选择合适的工具进行实验。

  4. 成本低廉：相比于其他自动驾驶平台，Donkey Car的成本相对较低，非常适合个人爱好者和教育机构使用。

如何开始？

要开始使用Donkey Car，你需要准备以下材料：

  • 一辆小型遥控车
  • Raspberry Pi
  • 摄像头
  • 电池
  • 3D打印的车架（可选）

接下来，你可以按照官方文档的指导进行硬件组装和软件安装。Donkey Car的官方GitHub页面提供了详细的安装步骤和示例代码，帮助你快速搭建自己的自动驾驶汽车。

结论

Donkey Car是一个非常适合AI初学者的项目。通过这个平台，你可以学习到自动驾驶的基本原理，掌握机器学习和计算机视觉的基础知识。无论你是想要进入AI领域，还是只是对自动驾驶技术感兴趣，Donkey Car都将是一个理想的起点。

希望这篇文章能帮助你更好地了解Donkey Car，并激发你对自动驾驶技术的兴趣。快来加入Donkey Car社区，开始你的AI学习之旅吧！

Donkey Car详解(1) – 组装

Donkey Car

概述

Donkey Car是2016年10月由美国人Adam Conway(Twitter)主导的一个人工智能自动驾驶模型车项目，由于近几年来人工智能技术和自动驾驶技术逐渐兴起，这个自动驾驶项目也受到越来越多爱好者关注和加入，逐渐形成了DonkeyCar社群。

本教程目标是帮助DonkeyCar初学者快速上手，如果有问题或建议，欢迎拍砖。

选择车架

最初的Donkey Car是通过改装“Exceed”牌1/16四驱玩具车而成，官方文档推荐一下四个型号：

以上四款小车的电子驱动模块都是一样的，它们但区别在于轮胎和安装顶盖的方式。需要提醒的是Desert Monster，Short Course Truck和Blaze需要增加一个适配器来固定Donkey Car主板和把手，可以在玩具店购买或者自己打印。因为这些小车都是标准配置，基本上是即插即用的。这些小车都有有刷电机版本和无刷电机版本，作者说使用有刷电机版的车架更容易训练，因为有刷电机更适合在粗糙的路面驾驶，也更便宜。

后来玩具们开始改装1/10的玩具车，1/10的车子变得更大，升级成Doneky Car专业版。当然专业版的Donkey Car性能更好，价格也更贵，以下是支持的型号：

  • HobbyKing Trooper (非专业版) 链接
  • HobbyKing Mission-D 链接
  • Tamaya TT01或者它的山寨版，这款容易购买，但需要散件安装，有一定难度。

因为完专业版的DonkeyCar的人比普通版的少，因为专业版存在文档不足的问题，如果你要开始完Donkey Car Pro，你应该具备一定的动手能力和更多的耐性。

所需零件

根据不同主板的支持，Donkey Car有两个版本，一款是使用树莓派(Raspberry Pi)的Donkey Car，另一款是使用Jetson Nano的Donkey Car。两个版本除了主板不同，所需的配件和软件的安装也是不一样的。

选项1 – 你可以通过Donkey Car官方渠道购买

如果你在美国，可以到原作者的Donkey store商店购买。

如果你在亚洲，你可以选择香港的Robocar Store，或者中国大陆的Robocar Store China购买。

官方套件或整车包括

零件大约成本
车架（来自上面的玩具车）大约¥600
电池及充电器大约¥120
树莓派 3b+大约¥300
128G SD卡大约¥120
其他配件大约¥600 – ¥875

选项2 – 你可以通过零件清单购买

如果你需要自己购买零部件，你可以参考作者的链接。值得提醒的是，如果你要自己组装，是需要通过3D打印机打印Donkey Car的零部件。如果你没有3D打印机，可以到红菜3D选购一台。

具体要准备的打印零部件，可以参考官方文档，链接。

Jetson Nano升级

如果你想你小车跑得更快，你可以选择具有GPU的Jetson Nano作为主板，除了需要换主板意外，你还需要另外准备一个Jetson Nano的主板适配器还有Jetson Nano的网卡和Jetson Nano适用的摄像头(可参考这篇文章)

JetsonNano适配器

Jetson Nano 适配器

JesonNano扩展板

注意Jetson Nano的pin口布局和树莓派相反，使用其他扩展板时要注意。

电池

我们强烈建议使用聚合物锂电池，因为聚合物锂电池的能量密度更高，而且电压下降曲线更平滑，因而电压更稳定。

Battery

硬件

如果你从官方商店购买Donkey Car，你可以跳过前两步。

第一步 打印零部件

如果你没有3D打印机，可以在我们的商店购买打印好的零部件，或者寻找第三方打印。如果使用 PLA打印，层高设置可以选择2mm，填充率30%，打印一套零部件，估计需要2天时间左右，这是模型的下载链接。

第二步 清理零部件

所有的3D打印件都需要做一点清理，包括重新钻孔，清理拉丝等。

robocarstore/17380757722502808

robocarstore/17380757862515729

第三步 安装底板和手提支架

安装步骤是相当简单的，你只需要使用M3螺丝把底板和手提把手连接起来。你要确保螺丝上紧了，因为你会使用手提支架提起小车。

第四步 连接舵机控制器

你可以把电路板安装上底板上再做连线，连线入下图所示。

robocarstore/173807580225062010

robocarstore/173807581725157711

这是树莓派的布局图，你需要连接3.3v、两个I2C pin (SDA和SCL) 和 Ground(接地)

第五步 将树莓派安装到底板上

在这个时候，你可以把已经烧录好的SD卡插入树莓派卡槽。然后把树莓派和舵机连通过螺丝安装到底板上。这时需要使用M2.6的螺丝。

（加图片）

第六步 安装摄像头

先把摄像头的数据线连接好，然后安装到手提支架到前方，如下图：

注意树莓派到安装方向，如果摄像头前有薄膜保护，记得要把保护膜撕掉以免影响训练效果。

第七步 安装到车架上

使用R型扣把底板固定在车架上，然后连接上电源线。

robocarstore/173807583625096212

恭喜你，你的小车安装完成！

要驱动你的小车，你就需要继续参考下一篇文章软件安装部分