I have trained a model based on Tensorflow. This model is supposed to work on the mobile phone but I have got a problem when converting froze graph (pb) to deep learning container(dlc). I have to set the input size to be constant. This cause that model can't work with any input size.
I am trying to find a way that resizes input shapes of a DLC model without initializing model with "snpe-tensorflow-to-dlc --input_dims 1,512,512,3" because this way is consuming.
Actually, I want to resize input shapes in dlc model. can anybody help me?
Usually deployment solutions work with fixed input shapes because they assume some widely acknowledged usage model - resize all picture of the same certain size and do inference. And due to this usage model, developers of deployment solutions do not prioritize model loading time while they usually prioritize the inference time. The same happens in SNPE, in OpenVINO, in TFLite, etc.
To illustrate the times, here is some results from Snapdragon 820. To load Inception v3 to CPU takes 715ms, to load model to DSP takes 3 seconds. Inference on CPU takes 1 sec, inference on DSP takes 100ms. You see that loading time on DSP is bigger than on CPU, but inference time is much much better.
At the same time, usually it is allowed to change a shape before loading of the model assuming that all input pictures will have different size (but again, same for all pictures) than shapes for which model was trained. For SNPE it is SNPEBuilder::setInputDimensions
If model allow to do reshape and if no bugs in SNPE implementation, the model can be reshaped and loaded.
Not sure if your usage model fits to the vision described in the first paragraph. At the same time, to have a benefit from different input size you need to develop special topology that unlikely be supported by SNPE. If you take just regular SSD and reshape it to different size and measure accuracy on validation set, the most likely you get the best result on shpaes where model was trained.
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I am building an iPhone app with a vision processing component that could use image segmantation, especially if it can run at 30fps. I am starting the exploration process with this repo, which uses the 8bit model from Apple. The inference times I get on an iPhone 7 are on the order of 200ms on 513x513 images.
I am used to image models scaling linearly with the number of pixels, so I assumed if I changed the size of the input image to 320x240 (the size we eventually expect to use) then I would get a speed up factor of 513x513/(320x240), for inference times around 60ms. However, the inference time is still on the order of 200ms.
Further detail: I created the 320x240 model by starting with the Deep Lab mobilenetv2_coco_voc_trainaug from the Deep Lab Model Zoo and then used coremltools to convert it to a CoreML model with input size 320x240 and quantized to 8 bits using kmeans.
Does anyone have any insight into why I am not seeing a speedup? Is there some way to achieve the throughput I was expecting?
I'm new in everithing about CNN and tensorflow. Im training a pretrained ssd-mobilenev1-pets.config to detect columns of buildings, about one day but the loss is between 2-1 and doesnt decrease since 10 hours ago.
I realized that my input images are 128x128 and SSD resize de image to 300*300.
Does the size of the input images affect the training?
If that is the case, should I retrain the network with larger input images? or what would be another option to decrease the loss? my train dataset has 660 images and test 166 I dont Know if there are enough images
I really aprecciate your help ....
Loss values of ssd_mobilenet can be different from faster_rcnn. From EdjeElectronics' TensorFlow Object Detection Tutorial:
For my training on the Faster-RCNN-Inception-V2 model, it started at
about 3.0 and quickly dropped below 0.8. I recommend allowing your
model to train until the loss consistently drops below 0.05, which
will take about 40,000 steps, or about 2 hours (depending on how
powerful your CPU and GPU are). Note: The loss numbers will be
different if a different model is used. MobileNet-SSD starts with a
loss of about 20, and should be trained until the loss is consistently
under 2.
For more information: https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10#6-run-the-training
The SSD Mobilnet architecture demands additional training to suffice
the loss accuracy values of the R-CNN model, however, offers
practicality, scalability, and easy accessibility on smaller devices
which reveals the SSD model as a promising candidate for further
assessment (Fleury and Fleury, 2018).
For more information: Fleury, D. & Fleury, A. (2018). Implementation of Regional-CNN and SSD machine learning object detection architectures for the real time analysis of blood borne pathogens in dark field microscopy. MDPI AG.
I would recommend you to take 15%-20% images for testing which cover all the variety present in training data. As you said you have 650+ images for training and 150+ for testing. That is roughly 25% of testing images. It looks like you have enough images to start with. I know the more, the merrier but make sure your model also has sufficient data to learn from!
Resizing the images does not contribute to the loss. It makes sure there is consistency across all images for the model to recognize them without bias. The loss has nothing to do with image resizing as long as every image is resized identically.
You have to make stops and recover checkpoints again and again if you want your model to be perfectly fit. Usually, you can get away with good accuracy by re-training the ssd mobilenet until the loss consistently becomes under 1.Ideally we want the loss to be as lower as possible but we want to make sure the model is not over-fitting. It is all about trial and error. (Loss between 0.5 and 1 seems to be doing the job well but again it all depends on you.)
The reason I think your model is underperforming is due to the fact that you have variety of testing data and not enough training data to suffice.
The model has not been given enough knowledge in training data to make the model learn for new variety of testing data. (For example : Your test data has some images of new angles of buildings which are not sufficiently present in training data). In that case, I recommend you to put variety of all images in training data and then picking images to test making sure you still have sufficient training data of new postures. That's why I recommend you to take 15%-20% test data.
I am using Tensorflow Object Detection API for fine-tuning, using my own data. The goal is to detect 2 classes of objects. I am using the pre-trained faster_rcnn_resnet101_coco model.
The various detection box precision and recall measures are generally increasing (see screenshots below) and are fairly high:
The box classifier losses are decreasing. HOWEVER, the RPN losses are increasing (see screenshots below) -- It looks that the model is having a hard time distinguishing foregrounds from backgrounds (hence, the increasing RPN losses), but once the model is able to identify and locate the right foreground, it classifies well (hence, the decreasing box classifier losses)? I think this can be observed in the model's performance on test images: the false positive rate (on images that do not contain any of the two classes of target objects) is rather high. On the other hand, on images that do contain those target objects, the model does a fantastic job in accurately identifying and locating those objects.
So my question is essentially: what are some of the things I could try to help make sure RPN losses are also decreasing.
I'm training a model to detect meteors within a picture of the night sky and I have a fairly small dataset with about 85 images and each image is annotated with a bounding box. I'm using the transfer learning technique starting with the ssd_mobilenet_v1_coco_11_06_2017 checkpoint and Tensorflow 1.4. I'm resizing images to 600x600pixels during training. I'm using data augmentation in the pipeline configuration to randomly flip the images horizontally, vertically and rotate 90 deg. After 5000 steps, the model converges to a loss of about 0.3 and will detect meteors but it seems to matter where in the image the meteor is located. Do I have to train the model by giving examples of every possible location? I've attached a sample of a detection run where I tiled a meteor over the entire image and received various levels of detection (filtered to 50%). How can I improve this?detected meteors in image example
It could very well be your data and I think you are making a prudent move by improving the heterogeneity of your dataset, BUT it could also be your choice of model.
It is worth noting that ssd_mobilenet_v1_coco has the lowest COCO mAP relative to the other models in the TensorFlow Object Detection API model zoo. You aren't trying to detect a COCO object, but the mAP numbers are a reasonable aproximation for generic model accuracy.
At the highest possible level, the choice of model is largely a tradeoff between speed/accuracy. The model you chose, ssd_mobilenet_v1_coco, favors speed over accuracy. Consequently, I would reccomend you try one of the Faster RCNN models (e.g., faster_rcnn_inception_v2_coco) before you spend a signifigant amount of time preprocessing images.
I'm attempting to train a faster-rccn model for small digit detection. I'm using the newly released tensorflow object detection API and so far have been fine tuning a pre-trained faster_rcnn_resnet101_coco from the zoo. All my training attempts have resulted in models with high precision but low recall. Out of the ~120 objects (digits) on each image only ~20 objects are ever detected, but when detected the classification is accurate. (Also, I am able to train a simple convnet from scratch on my cropped images with high accuracy so the problem is in the detection aspect of the model.) Each digit is on average 60x30 in the original images (and probably about half that size after the image is resized before being fed into the model.) Here is an example image with detected boxes of what I'm seeing:
What is odd to me is how it is able to correctly detect neighboring digits but completely miss the rest that are very similar in terms of pixel dimensions.
I have tried adjusting the hyperparameters around anchor box generation and first_stage_max_proposals but nothing has improved the results so far. Here is an example config file I have used. What other hyperparameters should I try adjusting? Any other suggestions on how to diagnose the problem? Should I be looking into other architectures or does my task look doable with faster-rccn and/or SSD?
In the end the immediate problem was that I was not using the visualizer correctly. By updating the parameters for visualize_boxes_and_labels_on_image_array as described by Johnathan in the comments I was able to see that that I am at least detecting more boxes than I had thought.
I check your config gile, you are decreasing the resolution of your image to 1024. The region of your digit will not contain a lot of pixel and you are loosing some information. What I suggest is to train the model with an another dataset (smaller images). You can for example crop the images in 4 four area.
If you have a good GPU increase the max dimension in the image_resizer, but I guess you will run out of memory