I am new to Machine Learning and TensorFlow so I'm sorry and please correct me if my understanding is wrong. I have this project, developing a real time traffic-light detection with TensorFlow.
I've been working with pre-trained TensorFlow models such as SSD Mobilenet and Faster R-CNN Resnet. However, the expected accuracy result have not yet reached. I already considered to add some more data to the dataset (my dataset contains +/-1000 images), but because it is more work to add more data (since I have to do another data taking and label all images), which could take days. I want to consider another option.
Is there any way to modify TensorFlow models architecture so I could optimize and make it focused for only traffic light detection? I've been looking through TensorFlow models folder and could not find in which file these model architectures defined.
Any help will be appreciated. Thank you
What you need is fine-tuning a pretrained model on your traffic light dataset. Given you already have about 1000 images for just one class, this is a descent dataset.
In order to perform fine-tuning, there are some important steps to do.
First you need to transform your data into tfrecord format. Follow this tutorial to generate tfrecord files. This is actually a difficult step.
Create a label_map.pbtxt for your model, since it is only traffic light, what you need is this. Here are sample label_map files.
item {
name: "traffic-light"
id: 1
display_name: "traffic-light"
}
Then you need to prepare a pipeline config file for the model. Since you want to have a real-time detector, I suggest you use SSD-mobilenet models. Some sample config files are available here. You can take one of this sample configs and modify some fields to get the pipeline config for your model.
Suppose you choose ssd_mobilenet, then you can modify this config file, ssd_mobilenet_v2_coco.config. Specifically you need to modify these fields:
num_classes: you need to change from 90 to 1 since you only have traffic lights to detect.
input_path: (in both train_input_reader and eval_input_reader), point this to the tfrecord file you created.
label_map_path: (in both train_input_reader and eval_input_reader), point this to the label_map file you created.
fine_tune_checkpoint: set this path to a downloaded pretrained ssd-mobilenet model.
Depending on your training results, you may need to further adjust some of the fields in the config file, but after the training your model will focus only on traffic light class and will likely have a high accuracy.
All the specific tutorials can be found on the repo site. If you have any further questions, you can ask on stackoverflow with tag: object-detection-api and a lot people would help.
Related
I am re-training the SSD MobileNet with 900 images from the Berkeley Deep Drive dataset, and eval towards 100 images from that dataset.
The problem is that after about 24 hours of training, the totalloss seems unable to go below 2.0:
And the corresponding mAP score is quite unstable:
In fact, I have actually tried to train for about 48 hours, and the TotoalLoss just cannot go below 2.0, something ranging from 2.5~3.0. And during that time, mAP is even lower..
So here is my question, given my situation (I really don't need any "high-precision" model, as you can see, I pick 900 images for training and would like to simply do a PoC model training/predication and that's it), when should I stop the training and obtain a reasonably performed model?
indeed for detection you need to finetune the network, since you are using SSD, there are already some sources out there:
https://gluon-cv.mxnet.io/build/examples_detection/finetune_detection.html (This one specifically for an SSD Model, uses mxnet but you can use the same with TF)
You can watch a very nice finetuning intro here
This repo has a nice fine tuning option enabled as long as you write your dataloader, check it out here
In general your error can be attributed to many factors, the learning rate you are using, the characteristics of the images themselves (are they normalized?) If the ssd network you are using was trained with normalized data and you don't normalize to retrain then you'll get stuck while learning. Also what learning rate are they using?
From the model zoo I can see that for SSD there are models trained on COCO
And models trained on Open Images:
If for example you are using ssd_inception_v2_coco, there is a truncated_normal_initializer in the input layers, so take that into consideration, also make sure the input sizes are the same that the ones you provide to the model.
You can get very good detections even with little data if you also include many augmentations and take into account the rest of the things I mentioned, more details on your code would help to see where the problem lies.
Open CV provides a simple API to detect and extract faces from given images. ( I do not think it works perfectly fine though because I experienced that it cuts frames from the input pictures that have nothing to do with face images. )
I wonder if tensorflow API can be used for face detection. I failed finding relevant information but hoping that maybe an experienced person in the field can guide me on this subject. Can tensorflow's object detection API be used for face detection as well in the same way as Open CV does? (I mean, you just call the API function and it gives you the face image from the given input image.)
You can, but some work is needed.
First, take a look at the object detection README. There are some useful articles you should follow. Specifically: (1) Configuring an object detection pipeline, (3) Preparing inputs and (3) Running locally. You should start with an existing architecture with a pre-trained model. Pretrained models can be found in Model Zoo, and their corresponding configuration files can be found here.
The most common pre-trained models in Model Zoo are on COCO dataset. Unfortunately this dataset doesn't contain face as a class (but does contain person).
Instead, you can start with a pre-trained model on Open Images, such as faster_rcnn_inception_resnet_v2_atrous_oid, which does contain face as a class.
Note that this model is larger and slower than common architectures used on COCO dataset, such as SSDLite over MobileNetV1/V2. This is because Open Images has a lot more classes than COCO, and therefore a well working model need to be much more expressive in order to be able to distinguish between the large amount of classes and localizing them correctly.
Since you only want face detection, you can try the following two options:
If you're okay with a slower model which will probably result in better performance, start with faster_rcnn_inception_resnet_v2_atrous_oid, and you can only slightly fine-tune the model on the single class of face.
If you want a faster model, you should probably start with something like SSDLite-MobileNetV2 pre-trained on COCO, but then fine-tune it on the class of face from a different dataset, such as your own or the face subset of Open Images.
Note that the fact that the pre-trained model isn't trained on faces doesn't mean you can't fine-tune it to be, but rather that it might take more fine-tuning than a pre-trained model which was pre-trained on faces as well.
just increase the shape of the input, I tried and it's work much better
I am building a new tensorflow model based off of SSD V1 coco model in order to perform real time object detection in a video but i m trying to find if there is a way to build a model where I can add a new class to the existing model so that my model has all those 90 classes available in SSD MOBILENET COCO v1 model and also contains the new classes that i want to classify.
For example, I have created training data for two classes: man, woman
Now, I built a new tensorflow model that identifies a man and/or woman in a video. However, my model does not have the other 90 classes present in original SSD Mobilenet model. I am looking for a way to concatenate both models or pass more than one model to my code to detect the objects.
If you have any questions or if I am not clear, please feel free to probe me further.
The only way i find is you need to get dataset of SSD Mobilenet model on which it was trained.
Make sure all the images are present in one directory and annotations in another directory.
We should have a corresponding annotation file for each image file
ex: myimage.jpg and myimage.xml
If all the images of your customed dataset are of same formate with SSD Mobilenet model then annotate it with a tool called LabelImg.
Add that images and annotated files to respective images and annotations directory where we have already saved SSD Mobilenet.
Try regenerate new TFrecord and continue with remaining procedure on it.
You can use transfer learning with Tensorflow API.
Transfer learning allows you to load re-trained network and modify the fully connected layer by introducing your classes.
There is full description for this in the following references:
Codelab
A good explanation here
Tensorflow API here for more details
Also you can use google cloud platform for better and faster results:
I wish this helps you.
I don't think there is a way you can add your classes to the existing 90 classes without using the dataset it is previously trained with. Your only way is to use that dataset plus your own and retrain the model.
I am using a tensorflow framework and I have noticed that there are major variances in the size of the tensorflow model files.
For example the framework provides 2 models:
one of pretrained model to be used with fine tuning for example
and one which contains an untrained version.
They both have a size of 172.539 kb
When I apply fine tuning in my model with some minor changes in the graph (there is a module in framework for that) and save my model the size remains essentially the same: 178.525 kb.
First, I am bit surprised that my fine-tuned model is somewhat bigger since I change just the last layer from 21 to 14 classes so I would expect a somewhat smaller model file size but since the difference is so little I didn't pay attention.
But when I trained the same model using the same model file (the pretrained one I mean) and saved the model in disk the file size is quite different: 340.097 kb. By the term train I mean I allow the network to modify all parameter not just the parameters of the last layer.
The model that is being implemented is a variation of resnet for semantic image segmentation (if can someone deduct the expected model file size from the model itself).
So, my questions are why I have such a variance in the model file sizes and how come my saved fine-tuned model is larger than the original model? Is there a way to include/exclude parameters in the model to be saved?
P.S.1 Some information that might be handy:
I am using tensorflow v2 model saving while I think the framework files use v1. I am not sure how to identify this besides the fact that the former produces 3 files.
The framework is called tensorflow-deeplab-resnet and can be found here and the models are here.
P.S.2
I am not sure stack overflow it 's the right place for this question either.
That is because, when training models and saving them, Tensorflow will also save the gradients of your ops.
So allowing training on the last layer will increase the size of your saved model a little. And allowing training on the whole model will essentially double the size of the save file because each op will have its gradients saved.
I have a question related to this one:
TensorFlow in production for real time predictions in high traffic app - how to use?
I want to setup TensorFlow Serving to do inference as a service for our other application. I see how TensorFlow Serving helps me to do that. Additionally, it mentions a continuous training pipeline, which probably is related to the possibility that TensorFlow Serving can serve with multiple versions of a trained model. But what I am not sure is how to retrain your model as you get new data. The other post mentions the idea to run retraining with cron jobs. However, I am not sure if automatic retraining is a good idea. What architecture would you propose for a continuous retraining pipeline with a system continuously facing new, labelled data?
Edit: It is a supervised learning case. The question is would you automatically retrain your model after n new datapoints came in or would you retrain during the downtime of the customer automatically or just retrain manually?
You probably want to use some kind of semi-supervised training. There's fairly extensive research in that area.
A crude, but expedient way, which works well, is to use the current best models that you have to label the new, incoming data. Models are typically able to produce a score (hopefully a logprob). You can use that score to only train on the data that fits well.
That is an approach that we have used in speech recognition and is an excellent baseline.