object-detection-api

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception

问题 I am just wondering what is the difference between the "classification" and "detection" fine tuning types for the available checkpoints in the object detection API. Are they both eligible to train novel classes? The checkpoints obtained from such training, can be further trained with the very same pipeline.config, or does it need to have a different fine tuning type? EDIT to make the question clearer, one can take as reference the ckpts mentioned in https://github.com/tensorflow/models/blob

问题 I am just wondering what is the difference between the "classification" and "detection" fine tuning types for the available checkpoints in the object detection API. Are they both eligible to train novel classes? The checkpoints obtained from such training, can be further trained with the very same pipeline.config, or does it need to have a different fine tuning type? EDIT to make the question clearer, one can take as reference the ckpts mentioned in https://github.com/tensorflow/models/blob

问题 I am using Tensorflow's object detection API, with my custom dataset. I am currently training "ssd_mobilenet_v1_coco" Everytime I try, training starts but training stops silently and randomly without error message. (Using COMMAND below, Command prompt shows the number of steps to some extent.) It is seems that GPU(CUDA) also stops. I've already tried changing batch_size("64" shows best score)and "ssd_mobilenet_v2_coco" Is this parameter(like "sample_1_of_n_eval_examples=1") or GPU problem? OS

问题 I am using Tensorflow's object detection API, with my custom dataset. I am currently training "ssd_mobilenet_v1_coco" Everytime I try, training starts but training stops silently and randomly without error message. (Using COMMAND below, Command prompt shows the number of steps to some extent.) It is seems that GPU(CUDA) also stops. I've already tried changing batch_size("64" shows best score)and "ssd_mobilenet_v2_coco" Is this parameter(like "sample_1_of_n_eval_examples=1") or GPU problem? OS

问题 I have a couple of own tfrecord file made by myself. They are working perfectly in tf1, I used them in several projects. However if i want to use them in Tensorflow Object Detection API with tf2 (running the model_main_tf2.py script), I see the following in tensorboard: tensorboard images tab It totally masses up the images. (Running the /work/tfapi/research/object_detection/model_main.py script or even legacy_train and they looks fine) Is tf2 using different kind of encoding in tfrecords? Or

问题 I'm using Tensorflow object detection API and working on pretrainedd ssd-mobilenet model. is there a way to extact the last global pooling of the mobilenet for each bbox as a feature vector? I can't find the name of the operation holding this info. I've been able to extract detection labels and bboxes based on the example on github: image_tensor = detection_graph.get_tensor_by_name( 'image_tensor:0' ) # Each box represents a part of the image where a particular object was detected. detection

来源： https://stackoverflow.com/questions/55279744/number-of-images-generated-using-data-augmentation-with-object-detection

来源： https://stackoverflow.com/questions/63848585/how-can-i-use-the-model-ckpt-files-from-tensorflow-training-on-google-cloud-for