@@ -66,18 +66,14 @@ We also integrated a function for taking a live camera image which comes from th
The Code concept works as follwing:
For the 3D error detection we us CNNs combined with diffrent datasets
We are suing 2+1 diffrent dataset. One is the unchaged datset from kaagle. The second one is the kaggle dataset miex trough domainshifting with our own recorded data. Then we also have one where we used transofrmation to simulate a bad camera image.
We are using 2+1 diffrent dataset. One is the unchaged datset from kaagle. The second one is the kaggle dataset miex trough domainshifting with our own recorded data. Then we also have one where we used transofrmation to simulate a bad camera image.
We use 2 fiels for testing the datasets
um die 3 ddruck ffhelr yu erkken nutyen wir nn die wir mit train mdoel .pz traineiren
We use 2 fiels for testing the datasets. One is for using a datasest filled with images [TEST_MODEL_with_DATA_SET.py](./TEST_MODEL_with_DATA_SET.py) the other one is for using a video or stream [TEST_MODEL_with_VIDEO.py](./TEST_MODEL_with_VIDEO.py).
2 dairttne yum testen der datesets
test mdoel mit viedo yum testen
The final file ist [ERROR_PRINT_DETECTION.py](./ERROR_PRINT_DETECTION.py.py)
3d print datasets fur domainshifts
We also have multipile files for training the datasets.
[TRAIN_MODEL.py](./TRAIN_MODEL.py) functions as a tempalte for implemteing your own model. Then we have [TRAIN_MODEL_IMP_R50.py](./TRAIN_MODEL_IMP_R50.py) which is used with the datset which uses the image transforamiton for simulating a bad camera. Furthermore we heve [TRAIN_MODEL_MIX_R18.py](./TRAIN_MODEL_MIX_R18.py) which is used with the domainshifted dataset and also of course [TRAIN_MODEL_NORMAL_R18.py](./TRAIN_MODEL_NORMAL_R18.py) which is used with the unedited Kaagle Dataset.
The final file ist [ERROR_PRINT_DETECTION.py](./ERROR_PRINT_DETECTION.py.py)
