YOLOv8 Baseline {Train & Inference}
I have made my currently solution public which includes the training and inference pipeline for the competition using recently launched YOLOv8 models.
Here is the GitHub Repo Link, follow the instructions in Readme to run the baseline.
https://github.com/Nischaydnk/hf-shipdetection_baseline
Current settings:
- Architecture: Yolov8n
- Split: 200 images train, 56 images validation
- Cv MAP score --> 0.259, LB --> 0.252
Seeing current high Leaderboard scores, I think larger Image size could enhance the score. I will make few experiments and update the results in this thread.
Also added some cool slider functions in the Notebook to visualize train/test predictions 🤓🤓
Also, thanks for hosting interesting challenge @abhishek and Data-Driven Science Team.
Thank you for participating in this competition @NischayDnk and also for making your solution public – that's great!
Hey
@NischayDnk
I used the following procedure to create the dataset
https://www.kaggle.com/code/pathikghugare/data-prep-ship-detection
created a yaml file like this :
train: /kaggle/input/ship-detection-hugging-face/ship-detection/train
val: /kaggle/input/ship-detection-hugging-face/ship-detection/test
nc: 1
names: ['ship']
then I simply trained the model
from ultralytics import YOLO
from YOLOv8_tools.wandb_callback import callbacks
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="/kaggle/working/data.yaml", epochs=50, patience=10, batch=4, imgsz=1280, save_period=5, project="ship-detection", name="yolov8n", exist_ok=True) # train the model
but in the validation stage I get the following:
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 15.5G 3.589 12.62 3.314 7 1280: 100%|██████████| 4/4 [00:22<00:00, 5.74s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 48/48 [01:33<00:00, 1.96s/it]
all 383 766 0 0 0 0
i.e. P, R and mAP score is 0
I suspect that something is wrong with the code that prepared the data, but I can't figure out where I went wrong; could you please take a look?