I am trying to do my own object detection using my own dataset. I started my first machine learning program from google tensorflow object detection api, the link is here:eager_few_shot_od_training_tf2_colab.ipynb
In the colab tutorial, the author use javascript label the images, the result like this:
gt_boxes = [
np.array([[0.436, 0.591, 0.629, 0.712]], dtype=np.float32),
np.array([[0.539, 0.583, 0.73, 0.71]], dtype=np.float32),
np.array([[0.464, 0.414, 0.626, 0.548]], dtype=np.float32),
np.array([[0.313, 0.308, 0.648, 0.526]], dtype=np.float32),
np.array([[0.256, 0.444, 0.484, 0.629]], dtype=np.float32)
]
When I run my own program, I use labelimg replace to javascript, but the dataset is not compatible.
Now I have two questions, the first one is what is the dataset type in colab tutorial? coco, yolo, voc, or any other? the second is how transform dataset between labelimg data and colab tutorial data? My target is using labelimg to label data then substitute in colab tutorial.
The "data type" are just ratio values based on the height and width of the image. So the coordinates are just ratio values for where to start and end the bounding box. Since each image is going to be preprocessed, that is, it's dimensions are changed when fed into the model (batch,height,width,channel) the bounding box coordinates must have the correct ratio as the image might change dimensions from it's original size.
Like for the example, the model expects images to be 640x640. So if you provide an image of 800x600 it has to be resized. Now if the model gave back the coordinates [100,100,150,150] for an 640x640, clearly that would not be the same for 800x600 images.
However, to get this data format you should use PascalVOC when using labelImg.
The typical way to do this is to create TFRecord files and decode them in your training script order to create datasets. However, you are free to choose whatever method you like Tensorflow dataset in order to train your model.
Hope this answered your questions.
Is there a way to export/save/load a previously trained autokeras model? I understand I can use the following code to save/load the underlying tensorflow best model:
model = reg.export_model()
model.save(MODEL_FILEPATH, save_format="tf")
best_model = load_model(MODEL_FILEPATH, custom_objects=ak.CUSTOM_OBJECTS)
However, in practice that wouldn't work, since my data has been fitted by autokeras, which takes care of data preparation and scaling. I don't think I have access to what autokeras is doing to the input data (X) before actually fitting, so I can't actually use the exported tensorflow best model to predict labels for new samples with un-prepared and unscaled features.
Am I missing something major here?
Also I noticed that there are some binaries in the autokeras temporary dir. That dir seems to be generated automatically. Is there a way to use that dir to load the previously-fit autokeras "super" model?
Just using import pickle will do the job - https://github.com/keras-team/autokeras/issues/1081#issuecomment-645508111 :
I have trained a model for object detection using tensorflow object detection repository.
Now while evaluating the default way is to read from a record file using tf.contrib.slim.parallel_reader
I want to read the images directly using file system and get the prediction for the image. How do I do that?
have a look on this https://github.com/tensorflow/models/blob/master/object_detection/object_detection_tutorial.ipynb, here a frozen model is used, but you can use normal ckpt with minor changed logic to use image instead of tfrecord.
step1: Load the model using ckpt file
step2: get the model end points to run predictions; similar to the link above except removing the :0
step3: use placeholder to feed the numpy array (your images)
Apart from this you can convert the trained model into a frozen model convert to frozen modeland follow the same step as described in the above link.
Now I'm using fb torch library from github fb torch resnet
It's my first time to use torch and lua, so Im encountering some problems.
My goal is to save the feature vector of specific layer (last avg pooling of resnet) into a one file with the class of the input image. All input images are from cifar-10 db.
The file format that i want to get is like belows
image1.txt := class index of image and feature vector of image 1 of cifar-10
image2.txt := class index of image and feature vector of image 2 of cifar-10
// and so on through all images of cifar-10
Now I have seen some sample code of that github extract-features.lua
Because it's my first time for lua, I feel so hard to understand this code and to modify to the way i want. And i don't want my data to save into t7 file format.
How can i access only one specific layer from network in torch via lua? (last average pooling)
How can i access values of the layer and classification result index?
How can read all each images from cifar-10 db file(t7 batch)?
Sorry for too many questions. But im feeling hard using torch because of pool amouns of community threads and posting of torch.. please understand me.
How can i access only one specific layer from network in torch via lua? (last average pooling)
To access each layer you just have to load the model and get it using an integer number. If you do print model you will be able to see in which position the last average pooling is.
model = torch.load(path_to_model):cuda()
avg_pooling_layer = model:get(position_of_the_avg_pooling_layer)
How can i access values of the layer and classification result index?
I do not quite understand what you mean by this. If you want to see the output or the weights from a specific layer. (following the code above) You need to get these elements from the layer table. Again, to see which ones are the possible elements to get use print avg_pooling_layer
weights = avg_pooling_layer.weight -- get the weights of the layer
output = avg_pooling_layer.output -- get the output of the layer
How can read all each images from cifar-10 db file(t7 batch)?
To read the images from a t7 file use the torch function torch.load. (used before to load the model).
cifar_10 = torch.load("path_to_cifar-10.t7")
Once loaded you could have the training and test set in subtables or functions. Again, print the table and visualize which values are the ones you need to get.
Hope this helps!
everyone, I am new to caffe. Currently, I try to use the trained GoogleNet which was downloaded from model zoo to classify some images. However, the network's output seem to be a vector rather than real label(like dog, cat).
Where can I find the label-map between trained model like googleNet's output to their real class label?
Thanks.
If you got caffe from git you should find in data/ilsvrc12 folder a shell script get_ilsvrc_aux.sh.
This script should download several files used for ilsvrc (sub set of imagenet used for the large scale image recognition challenge) training.
The most interesting file (for you) that will be downloaded is synset_words.txt, this file has 1000 lines, one line per class identified by the net.
The format of the line is
nXXXXXXXX description of class