object_detection

Training Faster RCNN model using LVM-Med (R50)

1. Activate conda environment

conda activate lvm_med 

2. Convert dataset to Coco format

We illustrate LVM-Med ResNet-50 for VinDr dataset, which detects 14 different regions in X-ray images. You can download the dataset from this link VinDr and put the folder vinbigdata into the folder object_detection. To build the dataset, after downloading the dataset, you can refer to the script convert_to_coco.py inside the folder object_detection and run it.

python convert_to_coco.py # Note, please check links inside the code in lines 146 and 158 to build the dataset correctly

3. Set train, valid, test folders

Edit base_config_track.py at:

• Lines 11, 12 for training set
• Lines 60, 61 for valid set
• Lines 65, 66 for test set
• Lines 86 for folder store models.

4. Train model and test

bash command.sh

5. Train from current epochs:

CUDA_VISIBLE_DEVICES=5 python finetune_with_path_modify_test_eval.py --experiment-name 'lvm-med-r50' --weight-path ../lvm_med_weights/lvmmed_resnet.torch --batch-size 16 --optim adam --clip 1 --lr 0.0001 --epochs 40 --labeled-dataset-percent 1.0 --resume