Hello everyone, I have 2 questions and thank you for your interest.
1- Object detection with Model Garden | TensorFlow Core Does this tutorial contain transfer learning? If your answers yes, Can you explain how do you understand that.
2- I just follow this tutorial with different data set which is pothole data.
Validation metrics are very bad. How can I increase validation metrics. I need more than 0.5 AP.
I use 30000 step to train model.
Model and model parameters are:
exp_config = exp_factory.get_exp_config(‘retinanet_resnetfpn_coco’)
batch_size = 32
num_classes = 1
HEIGHT, WIDTH = 640, 640
IMG_SIZE = [HEIGHT, WIDTH, 3]
Backbone config.
exp_config.task.freeze_backbone = True
exp_config.task.annotation_file = ‘’
Model config.
exp_config.task.model.input_size = IMG_SIZE
exp_config.task.model.num_classes = num_classes + 1
exp_config.task.model.detection_generator.tflite_post_processing.max_classes_per_detection = exp_config.task.model.num_classes
Training data config.
exp_config.task.train_data.input_path = train_data_input_path
exp_config.task.train_data.dtype = ‘float32’
exp_config.task.train_data.global_batch_size = batch_size
exp_config.task.train_data.parser.aug_scale_max = 1.0
exp_config.task.train_data.parser.aug_scale_min = 1.0
Validation data config.
exp_config.task.validation_data.input_path = valid_data_input_path
exp_config.task.validation_data.dtype = ‘float32’
exp_config.task.validation_data.global_batch_size = batch_size
Result Metrics are:
eval | step: 30000 | steps/sec: 5.4 | eval time: 18.4 sec | output:
{‘AP’: 0.10134392,
‘AP50’: 0.29438367,
‘AP75’: 0.03953968,
‘APl’: 0.550495,
‘APm’: 0.26847038,
‘APs’: 0.050420657,
‘ARl’: 0.55,
‘ARm’: 0.36355934,
‘ARmax1’: 0.107679464,
‘ARmax10’: 0.18247078,
‘ARmax100’: 0.21385643,
‘ARs’: 0.17557411,
‘box_loss’: 0.009063716,
‘cls_loss’: 0.60130537,
‘model_loss’: 1.054491,
‘steps_per_second’: 5.423086661586954,
‘total_loss’: 5.3274546,
‘validation_loss’: 5.3274546}