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.
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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.
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).
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.
With TensorFlow, I want to train an object detection model with my own images based on ssd_inception_v2_coco model. The problem I have is that all my pictures are black and white. What performance can I expect? Should I try to colorize my B&W pictures first? Or at the opposite, should I try to retrain base network with images "uncolorized"? Are there general guidelines for B&W processing of images for deep learning object detection?
I wouldn't go through the trouble of colorizing if you are planning on using a pretrained model. I would expect that explicitly colorizing your images as a pre-processing step would help very little (if at all) since in theory the features that a colorizing network learns can also be learned by the detection network.
If you are planning on pretraining your detection network that was trained on an RGB dataset, make sure you either (i) replace the first convolution in the network with a convolutional layer that expects a single-channel input, or (ii) pad your image with two all-zero channels.
You may get slightly worse detection performance simply because you lose two thirds of the image's pixel information when using BW instead of RGB.
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