I have followed this tutorial to retrain MobileNet SSD V1 using Tensorflow GPU as described and got 0.5 loss after training using GPU (below more info about config) and got model.ckpt. This is the command I used for Training: python ../models/research/object_detection/legacy/train.py –logtostderr –train_dir=./data/ –pipeline_config_path=./ssd_mobilenet_v1_pets.config And this is the command for freezing (generate pb file): python ../models/research/object_detection/export_inference_graph.py –input_type image_tensor –pipeline_config_path ./ssd_mobilenet_v1_pets.config
Tag: object-detection
Import error: module object detection not found
When i try run code into Jupyter notebook i getting Import error(attached image). I add paths to PYTHON_PATH and add %PYTHON_PATH% in system PATH, but i still get thos error Answer If you are using Anaconda, you must know that it ignores PYTHONPATH!. Use the following commands: conda develop ~/models/research/ conda develop ~/models/research/slim/ here is why you need to do
Mask R-CNN for object detection and segmentation [Train for a custom dataset]
I’m doing a research on “Mask R-CNN for Object Detection and Segmentation”. So I have read the original research paper which presents Mask R-CNN for object detection, and also I found few implementations of Mask R-CNN, here and here (by Facebook AI research team called detectron). But they all have used coco datasets for testing. But I’m quite a bit
How to reduce the number of training steps in Tensorflow’s Object Detection API?
I am following Dat Trans example to train my own Object Detector with TensorFlow’s Object Detector API. I successfully started to train the custom objects. I am using CPU to train the model but it takes around 3 hour to complete 100 training steps. I suppose i have to change some parameter in .config. I tried to convert .ckpt to