Hi community!

I am facing the problem, that I am getting an overall mAP for two classes in a custom trained YoloV3 model from 0,518%. The first class I try to predict is cars (mAP = 1,036%), the scond class I try to predict is trucks (mAP = 0%). I train the model with ~500 extracted frames from a video showing multiple cars and trucks driving on a highway. The images are splitted 80/20 into train and test images. For training I am using a RTX3060 12Gb, CUDA11.4, Cudnn 8201 and Tensorflow 2.5.1. While the training I am facing no errors. All .dll were successfully opened.

Befor trainig i created YoloCustom.txt (which shows "truck" and "car" as expected), YoloCustomTest.txt and YoloCustomTrain.txt using XML_to_YOLOv3.py which workes without showing any errors. After this step I configured the configs.py as the following:

`YOLO_TYPE = "yolov3" # yolov4 or yolov3 YOLO_FRAMEWORK = "tf" # "tf" or "trt" YOLO_V3_WEIGHTS = "model_data/yolov3.weights" YOLO_TRT_QUANTIZE_MODE = "INT8" # INT8, FP16, FP32 YOLO_CUSTOM_WEIGHTS = False # "checkpoints/yolov3_custom" # used in evaluate_mAP.py and custom model detection, if not using leave False

YOLO_CUSTOM_WEIGHTS also used with TensorRT and custom model detection

YOLO_COCO_CLASSES = "model_data/coco/coco.names" YOLO_STRIDES = [8, 16, 32] YOLO_IOU_LOSS_THRESH = 0.5 YOLO_ANCHOR_PER_SCALE = 3 YOLO_MAX_BBOX_PER_SCALE = 100 YOLO_INPUT_SIZE = 416 if YOLO_TYPE == "yolov4": YOLO_ANCHORS = [[[12, 16], [19, 36], [40, 28]], [[36, 75], [76, 55], [72, 146]], [[142,110], [192, 243], [459, 401]]] if YOLO_TYPE == "yolov3": YOLO_ANCHORS = [[[10, 13], [16, 30], [33, 23]], [[30, 61], [62, 45], [59, 119]], [[116, 90], [156, 198], [373, 326]]]

Train options TRAIN_YOLO_TINY = False TRAIN_SAVE_BEST_ONLY = True # saves only best model according validation loss (True recommended) TRAIN_SAVE_CHECKPOINT = False # saves all best validated checkpoints in training process (may require a lot disk space) (False recommended) TRAIN_CLASSES = "model_data/YoloCustom.txt" TRAIN_ANNOT_PATH = "model_data/YoloCustomTrain.txt" TRAIN_LOGDIR = "log" TRAIN_CHECKPOINTS_FOLDER = "checkpoints" TRAIN_MODEL_NAME = f"{YOLO_TYPE}_custom" TRAIN_LOAD_IMAGES_TO_RAM = True # With True faster training, but need more RAM TRAIN_BATCH_SIZE = 4 TRAIN_INPUT_SIZE = 416 TRAIN_DATA_AUG = True TRAIN_TRANSFER = True TRAIN_FROM_CHECKPOINT = False # "checkpoints/yolov3_custom" TRAIN_LR_INIT = 1e-4 TRAIN_LR_END = 1e-6 TRAIN_WARMUP_EPOCHS = 2 TRAIN_EPOCHS = 200

TEST options TEST_ANNOT_PATH = "model_data/YoloCustomTest.txt" TEST_BATCH_SIZE = 4 TEST_INPUT_SIZE = 416 TEST_DATA_AUG = False TEST_DECTECTED_IMAGE_PATH = "" TEST_SCORE_THRESHOLD = 0.3 TEST_IOU_THRESHOLD = 0.45

if TRAIN_YOLO_TINY: YOLO_STRIDES = [16, 32]

YOLO_ANCHORS = [[[23, 27], [37, 58], [81, 82]], # this line can be uncommented for default coco weights

YOLO_ANCHORS            = [[[10, 14],  [23, 27],   [37, 58]],
                           [[81,  82], [135, 169], [344, 319]]]`

With the configured configs.py I used train.py not changing anything with "python train.py".

Evaluating the model results in the following: grafik

Have I done anything wrong or forgot any step? Am I training the model with not enough images?

pythonlessons / TensorFlow-2.x-YOLOv3

Low mAP in custom trained model #164

YOLO_CUSTOM_WEIGHTS also used with TensorRT and custom model detection

YOLO_ANCHORS = [[[23, 27], [37, 58], [81, 82]], # this line can be uncommented for default coco weights