Show more images in Tensorboard - Tensorflow object detection

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tensorflow object detection num_visualizations

I am using Tensorflow's object detection framework. Training and evaluation jobs are going well, but in tensorboard I am only able to see 10 images for the evaluation job. Is there a way to increase this number to look at more images? I tried changing the config file:

eval_config: {
  num_examples: 1000
  max_evals: 50
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "xxx/eval.record"
  }
  label_map_path: "xxx/label_map.pbtxt"
  shuffle: false
  num_readers: 1
}

I thought the max_eval parameter would change this but it doesn't.

This is the command i'm running for the evaluation job:

python ../models/research/object_detection/eval.py \
    --logtostderr \
    --pipeline_config_path=xxx/ssd.config \
    --checkpoint_dir="xxx/train/" \
    --eval_dir="xxx/eval"

It should be the num_visualizations parameter in your eval_config (cf. eval.proto code).

Eval issues only 1 image in TensorBoard · Issue #5067 · tensorflow , Evaluation only shows 1 image in Tensorboard, see this image: /tensorflow-1-9​-object-detection-model-main-py-only-evaluates-one-image in displaying multiple test images with inferred bounding boxes (and don't need  Using the TensorFlow Image Summary API, you can easily log tensors and arbitrary images and view them in TensorBoard. This can be extremely helpful to sample and examine your input data, or to visualize layer weights and generated tensors. You can also log diagnostic data as images that can be helpful in the course of your model development.

I've been able to get this to work in Tensorboard 1.11.0 by editing the object_detection/protos/eval.proto file, then re-running protoc (see the Tensorflow docs). For example, this line in eval.proto would enable 100 examples (instead of the default 10):

optional uint32 num_visualizations = 1 [default=100];

This probably has an impact on system memory, browser performance, eval performance, etc.. so use with caution.

tensorboard does not show any images in object detection api , tensorboard does not show any images in object detection api during training #​4046 (as opposed to using a stock example script provided in TensorFlow):no As for training stage, since there are too many training images,  arasharchor changed the title tensorboard does not show any images in object detection api tensorboard does not show any images in object detection api during training Apr 21, 2018 tensorflowbutler assigned qlzh727 Apr 22, 2018

Probably the easiest way is to add command line argument --samples_per_plugin

Full example

tensorboard --logdir . --samples_per_plugin=images=100

https://github.com/tensorflow/tensorboard/issues/1012

Displaying image data in TensorBoard, Overview; Setup; Visualizing a single image; Visualizing multiple images you can easily log tensors and arbitrary images and view them in TensorBoard. We need to manually create labels for all the images in our dataset when working with object detection problems. To solve this problem, we propose an automatic way for annotating the image by a

mAP scores on tensorboard (Tensorflow Object Detection API) are , I recommend several checks to make sure you get reasonable mAP@IoU scores for object detection API: Try varying the Intersection over  What Is Object Detection? Object Detection is the process of finding real-world object instances like cars, bikes, TVs, flowers, and humans in still images or videos.

TensorFlow Object Detection API tutorial, First thing first, clone the TensorFlow object detection repository, and I hope you have installed TensorFlow. git clone The goal is to label the image and generate train.csv and test.csv files. To detect nodules we are using 6 co-​ordinates as show below: tensorboard --logdir=eval/#TO visualize the training results Image Recognition with Raspberry Pi Using TensorFlow Object Detection Model. Image Recognition with Raspberry Pi Using TensorFlow Object Detection Model. Skip navigation Show more Show less.

Object detection Part 5, Tensorflow's Object Detection API and its ability to handle large volumes of The Open Images dataset is comprehensive and large, but many of its classes are Describing how to get Tensorboard setup is outside of the scope of this example, We'll show you why we do this in our apply function later. import tensorflow_hub as hub Pick an object detection module and apply on the downloaded image. Modules: More images. Perform inference on some additional

Comments
  • It works for me on several servers. I use the following version of tensorboard==1.14.0