Hi
I have a most probably a beginer question abour loading the data in pycharm and later on in google colab from an Dataset from clearML.
I used from page: None and from the youtube video the following example:
# Read the data
data_path = Dataset.get(dataset_name="Fashion MNIST", alias="Fashion MNIST").get_local_copy()
fashion_mnist_test = pd.read_csv(f"{data_path}/fashion-mnist_test.csv")
X_test = np.array(fashion_mnist_test.iloc[:,1:])
y_test = np.array(fashion_mnist_test.iloc[:,0])
dtest = xgb.DMatrix(X_test, label=y_test)
However I need for a yolov8 (Object detection with arround 20k jpgs and .txt files) the data.yaml file:
model = YOLO("yolov8n.pt")
data = r"C:\Users\junke\.clearml\cache\storage_manager\datasets\ds_1019154914df4346a316c6e63a7237c9\data.yaml"
#data_path = Dataset.get(dataset_name="002_Datenset_MASAM_for_fintuning", alias="002_Datenset_MASAM_for_fintuning").get_local_copy()
def main():
results = model.train(data=data,
epochs=80,
at the moment I found not a way how I could allocate the data.yaml file in the Dateset.
Thank you for your support and help:-)
Hi @<1651395720067944448:profile|GiddyHedgehong81>
However I need for a yolov8 (Object detection with arround 20k jpgs and .txt files) the data.yaml file:
Just add the entire folder with your files to a dataset, then get it in your code
Add files (you can do that from CLI for example): None
clearml-data add --files my_folder_with_files
Then from code: None
data_path = Dataset.get(dataset_name="my dataset", alias="training dataset").get_local_copy()
# now all my files are in `data_path`
