I'm trying to train a model that can detect rotated rects. I've found the Nvidia ODTK, but I find nothing to indicate if I can train it with only one class (category). The default number of categories is 80 and now I'm not sure if it will work well with only one.
Try using the --classes argument. See https://github.com/NVIDIA/retinanet-examples/blob/main/TRAINING.md#other-arguments
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I am using a Teachable Machine model which i trained to recognize some specific objects, the issue with it, however, is that it does not recognize when there is nothing, basically it always assumes that one of the objects is there. One potential solution I am considering is combining two models like the YOLO V2 Tflite model in the same app. Would this be even possible/efficient? If it is what would be the best way to do it?
If anyone knows a solution to get teachable machine to recognize when the object is not present that would probably be a much better solution.
Your problem can be solved making a model ensemble: Train a classifier that learns to know if your specific objects are not in the visual space, and then use your detection model.
However, I really recommend you to upload your model to an online service and consume it via an API. As I know tflite package just supports well MobileNet based models.
I had the same problem, just create another class called whatever you want(for example none) and put some non-related images in it, then train the model.
Now whenever there is nothing in the field, it should output none.
I've followed an object detection tutorial from pythonprogramming.net to recognize a small robot (my custom object) based on the ssd_mobilenet_v1_coco model.
I've about 450 labelled images of my robot.
I used the official sample config for ssd_mobilenet_v1_coco, and only made the necessary changes like num_class = 1, and reduced the batch size to 7, and trained until I had a loss that was consistently between 1 and 2 (about 10000 epochs).
The problem is, the model detects everything it used to know from its pre-trained state as my small robot. So it identifies everything as being a robot even though they aren't.
I faced this issue before. And fixed it by adding images contains pre-trained objects as negative examples. Another way to fix it is training longer. If you do both that will fix the problem i think. And try increasing your dataset by the way (i was training with 6000 images).
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 use (https://github.com/matterport/Mask_RCNN) MASKRCNN for object detection and everything works fine. Is there a way to detect only certain objects? There are more than 80 classes of which I only need 2 pieces (like car and person). I would like to have the remaining classes not detected. How can they be removed?
I guess you are using the default pre-trained coco model for detection which comes with 80 classes. You can train your own model.
First of all you have to use VIA (VGG image annotator) to label the classes you want to predict.
Once that is done, you have to make some changes to the code of the model. For example, if you are using the file "balloon.py", you have to add classes, make some changes in load_mask() function and few other parts of the codes. After that you can start training your model, then use for detection and segmentation.
Yes, and for VIA, try using the version 1.0.0 as the format of the .json file is slightly changed in in the updated versions, which generally makes them incompatible with training on your custom data sets.
Check one example here
I am training a pre built tensorflow based model for custom object detection.
I want to detect only 1 type of object. I have taken lot of images from different angles and in different light conditions. I am training on K80 Nvidia GPU. Everything is working and when I train I can see the loss function falling to 0.3. But the loss values drops very quickly to under 1 when I start training. I am using SSD mobile Net as the base configuration for the model. When I try to test the model, it just draws a big square on the input image, rather than detecting the desired object in the image. Basically, it fails to detect the object.
I tried to train the model with a different set of images of mac n chesse which had lot of variations. Then the model worked fine and detected images of mac n chesse in the input image. But when I have pictures of single object then the model fails to detect. Please help me understand what I am doing wrong here
The issue was with my training dataset. I was not properly cropping the object from the original image. Also I needed around 300 images to properly train the model. SSD worked well after giving a well cropped images.