SSD Basic Detection

Training Command Reference

Run the following command to train the model:

python3 model_train.py --train_dataset_path={train_dataset_path} --train_output_path=./output_dir --pretrained_ckpt_path=./pre_trained_ckpt --epoch_size=5 --batch_size=2 --input_width=2048 --input_height=1536 --init_lr=0.001 --device_num=0

The model training process is random. Use the evaluated accuracy. Figure 1 shows the training accuracy.

Figure 1 Training accuracy results for SSD basic detection

Figure 2 shows the log information after the model training is complete.

Figure 2 Completion of SSD basic detection training

After the model training is complete, the CKPT, OM, and AIR model files are generated in the output directory specified by the --train_output_path parameter.

Evaluation Command Reference

Run the following command to evaluate the model:

python3 model_eval.py --eval_dataset_path={eval_dataset_path} --eval_ckpt_path=./output_dir --eval_output_path=./eval_result --min_score=0.01 --nms_threshold=0.2

Use the checkpoint output during the training to evaluate the model accuracy. Figure 3 shows the results.

Figure 3 Evaluation results for SSD basic detection

The folders and files shown in Figure 4 are generated in the evaluation directory. The ok_images, ng_fp_images, and ng_fn_images folders store the image evaluation results, and the statistics.csv file stores the corresponding accuracy results.

Figure 4 Evaluation directory for SSD basic detection

Parent topic: SSD Detection