object-detection-api

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 in Tensorflow object detection API, we usually do this for each test image: output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image_np_expanded}) # pdb.set_trace() # all outputs are float32 numpy arrays, so convert types as appropriate output_dict['num_detections'] = int(output_dict['num_detections'][0]) output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.int64) output_dict['detection_boxes'] = output_dict['detection_boxes'][0] output_dict['detection

问题 After struggling with compatibility issues between Tensorflow 2.00 and the object detection API, I downgraded to Tensorflow 1.15 to be able to train my own model. after completing the training I modified the jupyter notebook included in the Tensorflow object detection API repo to test on my own images but I keep getting this error: Traceback (most recent call last): File "object_detection_tutorial_converted.py", line 254, in <module> show_inference(detection_model, image_path) File "object

问题 I googled around a bit but I only found questions about enabling data augmentation. I followed this tutorial but with my own dataset (only one class). I already performed data augmentation on my dataset so I deleted the responsible lines from the pipeline.config. Now my pipeline looks like this model { ssd { num_classes: 1 image_resizer { fixed_shape_resizer { height: 640 width: 640 } } feature_extractor { type: "ssd_resnet50_v1_fpn_keras" depth_multiplier: 1.0 min_depth: 16 conv_hyperparams

问题 TF Object Detection API grabs all GPU memory by default, so it's difficult to tell how much I can further increase my batch size. Typically I just continue to increase it until I get a CUDA OOM error. PyTorch on the other hand doesn't grab all GPU memory by default, so it's easy to see what percentage I have left to work with, without all the trial and error. Is there a better way to determine batch size with the TF Object Detection API that I'm missing? Something like an allow-growth flag

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 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