I am trying to plot a continuous graph for the evaluation of my model. Tensorboard (v2.4.1) succeed to plot the different losses for each step. Nevertheless it only plots the last step for the evaluation and I only have a dot on my evaluation curves. Here is my tensorboard view : Tensorboard show only the ..
When I run this line on anaconda prompt = python export_inference_graph.py –input_type image_tensor –pipeline_config_path training/ssd_mobilenet_v1_pets.config –trained_checkpoint_prefix training/model.ckpt-7747 –output_directory new_graph This error occurs= Original stack trace for ‘save/RestoreV2’: File "export_inference_graph.py", line 206, in <module> tf.app.run() File "/tensorflow-1.15.2/python3.7/tensorflow_core/python/platform/app.py", line 40, in run _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef) File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 303, in run _run_main(main, args) File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 251, ..
I train an object object detection model, based on pre-trained model from TF2 Object Detection efficientdet _d2_coco17_tpu-32. https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md I changed pipeline.config as needed to this process (I did it many time before, on eff_d1 or ssd models from tf2 object detection zoo). I succseful trained this model on batch size 2 and 10K steps. ..
After training a RetinaNet model from TensorFlow’s Official Object Detection models in the ModelZoo (https://github.com/tensorflow/models/tree/master/official/vision/detection), it outputs the following files: checkpoint ctl_step_0.ckpt-1.data-00000-of-00001 ctl_step_0.ckpt-1.index params.yaml Along with a folder for eval, eval_test, and eval_train for tensorboard. However, the README doesn’t provide any instructions for inference. The main.py file also doesn’t include inference capabilities, only train and ..
I am working on an object detector using Tensorflow Object Detection API whereby I downloaded a model from the model zoo repository, specifically ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8 to train my custom dataset on which contains 2500 images split into 70:30 training and testing ratio. I have edited the pipeline.config file accordingly following a tutorial, where I added the ..
enter image description here this i’m getting for training and testing accuracy enter image description here this is for my confusion matrix Source: Python..
run_detector This function will take in the object detection model detector and the path to a sample image, then use this model to detect objects. This time, run_dtector also calls draw_boxes to draw the predicted bounding boxes. Error in the following line: run_detector(detector, downloaded_image_path) Source: Python-3x..
I am trying object detection. EfficientDet which was introduced was google in 2018 and faster RCNN was introduced in 2015. In general which is a more accurate model for object detection? (If we don’t consider the speed) EfficientDet is more accurate according to TensorFlow Model Zoo but in general, FasterRCNN is more accurate because they ..
Whenever I’m calling my detection function of the Tensorflow Object Detection API, I’m getting all this logging: WARNING:tensorflow:AutoGraph could not transform <bound method SSDResNetV1FpnKerasFeatureExtractor.preprocess of <object_detection.models.ssd_resnet_v1_fpn_keras_feature_extractor.SSDResNet50V1FpnKerasFeatureExtractor object at 0x0000028E9E5AB6A0>> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and ..
I am trying to do a real-time object detection from a video. My model is working fine, but in my final stage I want to print the coordinates of the predicted bounding boxes. Now that I am doing it in a video, I want to print these coordinates continuously. This is the code where the ..