lzccccc / 3d-bounding-box-estimation-for-autonomous-driving Goto Github PK
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3d bounding box estimation from monocular image based on 2d bounding box
Hi, thanks for uploading this code!
How did you measure the performance?
作者您好:
很不错的复现。有个问题想问下,就是你实现角度location loss是不是与原文当中的实现不太一样呢,因为我最近也在复现这篇论文.忘赐教 谢谢!
Hello i have one question,
which kind of data do we need for the camera calibration data ? because if we want to compute other 3d bounding box especially for other cameras, which camera calibration data do we need to get good results ? or just better results.
Kind Regards.
I have problem with preparing KITTI data set. I download the data set you mention and the box_3d, label_02 and calib_02 file is missing can you please give me the proper link with these folders.
Thank you
My training stopped at about epoch 50 and the programs says "early stopping". What does this mean?
Could you specify all the data and corresponding links needed for training process ? Thank you
If I use "VGG", should I download a pre-trained VGG model?
I trained the model, it is working fine for the Kitti dataset images. I went through your prediction code and found you are using ground truth labels for generating predictions. You are creating an image patch using the ground truth labels and feeding that patch to the trained model instead of just raw images. So, what if I do not have ground truth labels which is normal for testing images or videos?
I was able to setup and run the training, but if I use VGG as backbone, the training log looked like below:
ETA: 3:15:50 - loss: inf - dimensions_loss: inf - orientation_loss: 1.74
ETA: 2:11:50 - loss: nan - dimensions_loss: nan - orientation_loss: 1.69
TA: 1:39:53 - loss: nan - dimensions_loss: nan - orientation_loss: 1.34
MobileNetV2 works fine. But the performance is not as good as VGG. I really need to use VGG.
I am running the training now, but do not know where the model was saved. Anyone can help?
Hi, I was just wondering about the license for the project since it doesn't seem to have one. Are people ok to use the repo?
Hello lzccccc good morning
I want to know if using pretrained weights from imagenet to train usage is something like this one?
We need to set the trainable layer to false or not?
Because I train without any pre-trained weights from imagenet give a bad result
inputs = layers.Input(shape=(cfg().norm_h, cfg().norm_w, 3))
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(inputs)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
x = layers.GlobalAveragePooling2D()(x)
model1 = tf.keras.Model([inputs], [x], name='vgg16_1')
model1.load_weights('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')
model1.summary()
for layer in model1.layers[:]:
layer.trainable = False
for layer in model1.layers:
print(layer, layer.trainable)
Hi Author, I am confuse about the labels_02, calib_02 files in the folder
kitti_dateset/
├── 2011_09_26
│ └── 2011_09_26_drive_0084_sync
│ ├── box_3d <- predicted data
│ ├── calib_02
│ ├── calib_cam_to_cam.txt
│ ├── calib_velo_to_cam.txt
│ ├── image_02
│ ├── label_02
│ └── tracklet_labels.xml
i dont find this the kitti raw data. can you please explain where do you get this data from?
@lzccccc Hi,you said the oritentation loss is changed to the correct form.So is different from the paper? I check the code you uploaded , I don't think there is any difference between the paper . Did I make some mistake or misunderstand you ?
Hi,
firstly, excellent repo, very nicely written code, love it!
But I am confused a bit with the math.
The model outputs 2 orientation values, a sin and a cosine of the local orientation alpha, as predictions.
But while looking at visualization3Dbox.py
, it appears that it used the P2
provided by the calib data.
So how will plotting happen for an unseen image, when P2
for it isn't available?
Let me know if I failed to explain myself. Thanks!
the result is tested when IoU = 0.5 or IoU = 0.7?
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