Train YOLOv10 on Custom Dataset with Ultralytics

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In this video 📝 we are going to take a look at how you can train a custom YOLOv10 model on your own dataset with Ultralytics. We are going to use the free GPU on google colab, export the dataset from roboflow after labelling and then train it in a few lines code with Ultralytics.

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Timestamps:
00:00 Introduction
00:37 Export Dataset
02:20 Train Model
06:27 Traning Results
08:22 Inference with Custom Model
09:52 Outro

Tags:
#yolov10 #objectdetection #customYOLOv10

[NicolaiAI]

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[fajrihanggi]

i alrd follow step by step but this eror said
ScannerError: while scanning a simple key
in "<unicode string>", line 11, column 1:

^
could not find expected ':'
in "<unicode string>", line 12, column 1:

i dont even know where is line 12 and how i check it

[sagnumb]

Just saw some reddit post about ultralytics yolov10, I'm not going to train my custom datasets using their wrapper due to the shady license.

[arisalikbaev]

Good time of the day! Has anyone encountered this issue with DetMetrics curves TODO while training your model on yolov10:
AttributeError: 'DetMetrics' object has no attribute 'curves_results'. See valid attributes below.
How to deal with that?

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[SlfgjkAldfjgf]

How to set the augmentation paramters?

# Load the pretrained YOLOv8 model
model = YOLO(model_path) # Use the .pt file for pretrained weights

# Define custom augmentation parameters
augmentation_params = {
'degrees': 19, # Rotation in degrees
'translate': 0.1, # Translation as a fraction of image size
'scale': 0.5, # Scaling factor
'shear': 2.0, # Shear angle in degrees
'perspective': 0.0, # Perspective transformation
'flipud': 0.5, # Vertical flip probability
'fliplr': 0.5, # Horizontal flip probability
'mosaic': 1.0, # Mosaic augmentation probability
'mixup': 0.2, # Mixup augmentation probability
'hsv_h': 0.015, # HSV hue augmentation
'hsv_s': 0.7, # HSV saturation augmentation
'hsv_v': 0.4, # HSV value augmentation
}

# Additional training parameters
training_params = {
'data': dataset_path, # Path to your dataset
'imgsz': 640, # Image size
'augment': True, # Enable augmentation
'patience': 10, # Early stopping patience
'save_period': 1, # Save the model after every epoch
'save': True, # Enable saving of model checkpoints
'resume': True, # Resume training from the last checkpoint
'project': save_dir, # Directory to save the project
**augmentation_params # Include custom augmentation parameters
}

# Train the model with custom parameters



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[ignis.valorant]

Nice video as always, super helpful! I want to ask whether it is possible to use SAHI as a part of the training instead of the final inferences. My objects are kinda small, so I assume that using SAHI in the training phase can help a lot. I tried using tiles but it ended up being too slow to train unfortunately. Any advice is greatly appreciated

[Ultralytics]

Excellent overview of YOLOv10 with Ultralytics!

[shashankgsharma]

can you do fintuning of LLMs also like LLaVA-NeXT? It would be great!

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