This implementation using darknet's pytorch implementation to detect objects and classify them as per the type of grip used by the human hand to interact with the object.

• Copy create_list_images.py inside data/python create_list_images.py && mv list.txt ../train.txt

• Inside train/ run the following command to create list of absolute file paths

python create_list_images.py && mv list.txt ../train.txt 

• Repeat same for validation/

python create_list_images.py && mv list.txt ../val.txt 

• Edit training/config/coco.data and set train and val path as path of the created text files under data

• Install the environment.yml file

conda env create -f environment.yml

• Run train.py under training -

usage: train.py [-h] [--epochs EPOCHS] [--image_folder IMAGE_FOLDER] [--batch_size BATCH_SIZE] [--model_config_path MODEL_CONFIG_PATH] [--data_config_path DATA_CONFIG_PATH] [--weights_path WEIGHTS_PATH] [--class_path CLASS_PATH] [--conf_thres CONF_THRES] [--nms_thres NMS_THRES] [--n_cpu N_CPU] [--img_size IMG_SIZE] [--checkpoint_interval CHECKPOINT_INTERVAL] [--checkpoint_dir CHECKPOINT_DIR] [--use_cuda USE_CUDA]

optional arguments: -h, --help show this help message and exit --epochs EPOCHS number of epochs --image_folder IMAGE_FOLDER path to dataset --batch_size BATCH_SIZE size of each image batch --model_config_path MODEL_CONFIG_PATH path to model config file --data_config_path DATA_CONFIG_PATH path to data config file --weights_path WEIGHTS_PATH path to weights file --class_path CLASS_PATH path to class label file --conf_thres CONF_THRES object confidence threshold --nms_thres NMS_THRES iou thresshold for non-maximum suppression --n_cpu N_CPU number of cpu threads to use during batch generation --img_size IMG_SIZE size of each image dimension --checkpoint_interval CHECKPOINT_INTERVAL interval between saving model weights --checkpoint_dir CHECKPOINT_DIR directory where model checkpoints are saved --use_cuda USE_CUDA whether to use cuda if available

• Example command to ignore depreciation warnings, savinf errors in error.txt and log in log.txt

python -u -W  ignore train.py --epochs 200 --checkpoint_interval 5 --batch_size 4 --weights_path config/yolov3.weights --n_cpu 4 2>error.txt | tee log1.txt 

• Your weights after each checkpoint will be stored under checkpoints/

python test.py

1. Put images to detect inside testing/toDetect/ directory (Some images giving good output already present)
2. run test.py
3. your predictions will be saved under prections/

pytorch darknet training

pytorch darknet testing

Open Images Download Kit