Tune hyper-parameters for training a machine learning model

Training dataset : 588 images
Validation dataset : 76 images

The config file looks like:

> "train_data_loader": {
    "type": "DataLoader",
    "args": {
        "batch_size": 1,
        "shuffle": true,
        "drop_last": true,
        "num_workers": 4,
        "pin_memory": true
    }
},
"val_data_loader": {
      "type": "DataLoader",
      "args": {
          "batch_size": 1,
          "shuffle": false,
          "drop_last": false,
          "num_workers": 4,
          "pin_memory": true
      }
  },

"optimizer": {
      "type": "Adam",
      "args": {
          "lr": 0.007,
          "weight_decay": 0.15,
          "amsgrad": true
      }
},
"lr_scheduler": {
    "type": "StepLR",
    "args": {
        "step_size": 30,
        "gamma": 0.1
    }
},

"trainer": {
    "epochs": 50,
    "gl_loss_lambda": 0.015,
    "crf_loss_lambda": 1,
    "cn_loss_lambda": 10000,
    "log_step_interval": 20,
    "val_step_interval": 9999,

What could be the best set of parameters or a descent learning rate to start with?
Currently i get poor results and due to early stopping it is not improved.
Any suggestions ?