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View Code? Open in Web Editor NEWSample scripts for the Bosch Small Traffic Lights Dataset
Home Page: https://hci.iwr.uni-heidelberg.de/benchmarks
License: MIT License
Sample scripts for the Bosch Small Traffic Lights Dataset
Home Page: https://hci.iwr.uni-heidelberg.de/benchmarks
License: MIT License
You are getting an error with OSError: [Errno Could not open image path] D:\net\pal-soc1.us.bosch.com\ifs\data\Shared_Exports\deep_learning_data\traffic_lights\university_run1\24068.png
Hi, I tried using the SSD config file to train. However, my training loss is almost constant at 0.346 with continuous spikes in between. After nearly ~150 steps, my loss continuous increases. I have tried tuning the parameters but no change in results unfortunately. My learning rate is also quite low. Is there anything I'm missing or should be looking into?
Thanks!
Line 24:
annotation = ET.Element("annotaion")
should be changed to:
annotation = ET.Element("annotation")
I don't know whether this causes any problems or not.
Hi,
I was unable to figure out what TF version is recommended for use. Could you please clarify? Alternatively, if you could add this (and any other library versions) to requirements.txt
please.
Thanks!
Hello,
I am trying to download the training dataset but I am encountering an error while doing so.
I've registered my email in the form and follow the given link to download these files:
I couldn't download the 1, 2 and 3 as I encountered these problems:
However, I succeed to download them on Windows with Internet Explorer.
I hope it's the right place to report this issue as I didn't find emails on the dataset page.
Best regards
Using a script bosch_to_pascal.py I have xml files without information about labels and bounding boxes
Hi, many thanks for putting this dataset together and also for providing detailed benchmarks and scripts. Any chance we can obtain your saved models as well (NAS-A, SSD)? - thx!
Hello, Thanks for your great work. However, the dataset that I down load can't be opened. All the images can be open. Is there something wrong with the dataset? Thanks!
请问这个数据集的标注为什么不是整数,我使用的rgb的图像需要针对这一点做什么额外的处理吗?
Hi, thanks for sharing your dataset. Could you please provide additional details about the acquisition system? In particular, do you know the value of the focal length of the camera?
Hi, you have multiple classes then your score_converter should be SOFTMAX? Why did you use SIGMOID?
Hi, all
Following the 'Tensorflow object detection example' RAEDME, I got an error like this when compiling the to_tfrecords.py file
Traceback (most recent call last):
File "D:\Udacity\Self-driving_car\projects\Term3\P3\models\research\bstld\tf_object_detection\to_tfrecords.py", line 195, in
create_datasets(config)
File "D:\Udacity\Self-driving_car\projects\Term3\P3\models\research\bstld\tf_object_detection\to_tfrecords.py", line 149, in create_datasets
train_labels, valid_labels = split_train_labels(train_labels)
File "D:\Udacity\Self-driving_car\projects\Term3\P3\models\research\bstld\tf_object_detection\to_tfrecords.py", line 124, in split_train_labels
train_videos = list(map(lambda x: x['path'].split('/')[-2], train_labels))
File "D:\Udacity\Self-driving_car\projects\Term3\P3\models\research\bstld\tf_object_detection\to_tfrecords.py", line 124, in
train_videos = list(map(lambda x: x['path'].split('/')[-2], train_labels))
IndexError: list index out of range
I am using the updated label files in the label_files folder. Is there any way to solve this? Many thanks~
Hi, When I trained the bosch traffic light dataset with faster rcnn, I got loss_box=0. Does it mean that the label box of the object in the dataset is too small for faster rcnn to detect?
Hello,
I downloaded the RGB dataset files from the link provided in the ReadMe. However they were all downloaded as binaries. Looking at the code I see that the input to any of the scripts is a yaml file which I assume would be inside the dataset folders. How can I open/convert to another format?
Thanks!
Hello
I am a student trying to recreate a research paper for which I need to use bstld datset trained SSD architecture with Mobilenet_v1. However inference.py lacks instructions on how to use it to get a pre-trained model.
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