Comments (5)
π Hello @smallMantou, thank you for your interest in Ultralytics YOLOv8 π! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
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Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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For recognizing different colors of small targets like droplets, YOLOv8 would be a better choice due to its improved architecture, which generally offers better performance, especially for tasks involving small or detailed object detection.
You should focus on training your model with a well-labeled dataset where droplets are accurately annotated with color labels. Here's a simple example of how you might set up your model for training with YOLOv8:
yolo detect train data=droplets.yaml model=yolov8n.yaml epochs=100 imgsz=640
Ensure your droplets.yaml
includes accurate path settings and that each droplet color is a separate class if they need individual recognition. This will help your YOLOv8 model learn to distinguish between different colors more effectively. π¨
Feel free to experiment with different configurations and hyperparameters to optimize detection performance.
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Thank you for your help. Do I need to set the hyperparameter hsv to 0 due to the small color difference between my different droplets.
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@smallMantou hello!
Setting the hsv_h
, hsv_s
, and hsv_v
hyperparameters to 0
will effectively disable color augmentation during training, which might be beneficial if the color distinction between droplets is crucial and subtle. However, it's important to carefully consider this because color augmentation can also help prevent overfitting by providing variety in training data.
You might want to experiment with low values first before turning it off entirely. Hereβs how you can adjust these settings in your .yaml
config file:
hsv_h: 0.1 # adjust the hue by a small percentage
hsv_s: 0.1 # adjust the saturation by a small percentage
hsv_v: 0.1 # adjust the value by a small percentage
Try different values and see which offers the best performance! π
from ultralytics.
For recognizing different colors of small targets like droplets, YOLOv8 would be a better choice due to its improved architecture, which generally offers better performance, especially for tasks involving small or detailed object detection.
You should focus on training your model with a well-labeled dataset where droplets are accurately annotated with color labels. Here's a simple example of how you might set up your model for training with YOLOv8:
yolo detect train data=droplets.yaml model=yolov8n.yaml epochs=100 imgsz=640Ensure your
droplets.yaml
includes accurate path settings and that each droplet color is a separate class if they need individual recognition. This will help your YOLOv8 model learn to distinguish between different colors more effectively. π¨Feel free to experiment with different configurations and hyperparameters to optimize detection performance.
Thanks , Glenn! I have the same problem , Could you tell me which part of architecture improvement can better solve this task than Yolov5. Iβm confused about that.
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Related Issues (20)
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- Predications from my model that aren't in my dataset. Am I using the wrong methods to test my model? HOT 6
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- How much data to pass for YOLO if we have the same object in our dataset we want to detect HOT 1
- Yolov8 obb training label bbox show wrong HOT 3
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- # TODO CoreML Segment and Pose model pipelining HOT 9
- Integrate new NN module HOT 4
- Will CoreML Conversion Support be Available for YOLOv10 Custom Models? HOT 1
- zh HOT 4
- non-normalized or out of bounds coordinates HOT 4
- yolov8_obb val appear large error predict boxes HOT 2
- How to train one yolo segment model with 2 class seg label and 1 class detect (box) label? HOT 2
- Load custom data HOT 6
- Segment errors occur during training on linux HOT 2
- Confusion Matrix process_batch function HOT 3
- How can I get FLOPs when I changed the model HOT 7
- Errors during changing the feature extractor HOT 3
- MixUp augmentation problem HOT 4
- Applying YOLOv8 Model on Multiple Streams: How to Implement? HOT 2
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