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+demo/comparison/test_priconne.png filter=lfs diff=lfs merge=lfs -text
+demo/comparison/test_uma.png filter=lfs diff=lfs merge=lfs -text
+demo/comparison/yolov8_animeface/test_priconne.png filter=lfs diff=lfs merge=lfs -text
+demo/comparison/yolov8_animeface/test_uma.png filter=lfs diff=lfs merge=lfs -text

README.md

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----
-license: agpl-3.0
----
+# yolov8_animeface
+Anime Face Detection using YOLOv8
+
+## Dataset
+Dataset was made created with 10 000 images manually annotated from safebooru. Split was train 70, val 20, test 10.
+
+## Performance
+
+This model is based on yolov8x6. It has been trained on the said dataset for 300 epoch at 1280px*1280px. It took ~110 hours to train on a RTX A4000.
+
+On my dataset, the model performs particularly well with the default parameters.
+
+```
+Images  Instances    Box(P        R      mAP50     mAP50-95):
+1002       1562      0.957      0.924      0.955      0.534
+Speed: 1.3ms preprocess, 81.9ms inference, 0.0ms loss, 0.8ms postprocess per image
+```
+
+While it doesn't provide a huge mAP50-95, its predictions are always correct on the files I've tested. Confidence could be higher, but the model is very precise.
+
+![](./demo/val/confusion_matrix.png "Confusion Matrix")
+*Confusion matrix of the model, generated on the test set*
+
+![](./demo/val/PR_curve.png "PR curve")
+*PR curve of the model, generated on the test set*
+
+Other metrics are available in the `./demo/val` subfolder.
+
+## Demo
+
+Manual annotation             |  yolov8_animeface
+:-------------------------:|:-------------------------:
+![](./demo/val/val_batch1_labels.jpg "1st batch labels")  |  ![](./demo/val/val_batch1_pred.jpg "Prediction made by yolov8_animeface")
+![](./demo/val/val_batch2_labels.jpg "2nd batch labels")  |  ![](./demo/val/val_batch2_pred.jpg "Prediction made by yolov8_animeface")
+
+
+## Comparison with an existing model
+
+While we can argue about the comparison between two models from different generations, I believe it is interesting to compare this model with [zymk9's model](https://github.com/zymk9/yolov5_anime), based on yolov5. They both have the same purpose and were trained in the same fashion.
+
+On the same dataset with the same parameters (conf=0.001 & iou=0.6), yolov8x6-animeface produces better metrics than the one on yolov5x.
+
+yolov8-animeface:
+```
+Images  Instances    Box(P        R      mAP50     mAP50-95):
+1002       1562      0.956      0.919      0.953      0.532
+Speed: 1.2ms preprocess, 81.2ms inference, 0.0ms loss, 1.0ms postprocess per image
+```
+
+yolov5-anime:
+
+```
+Images  Instances    Box(P        R      mAP50     mAP50-95):
+1003       1566      0.778      0.685      0.633      0.232
+Speed: 1.5ms pre-process, 85.5ms inference, 1.4ms NMS per image at shape (32, 3, 1280, 1280)
+```
+
+yolov5-anime provides better results when images are resized at 640px, but it still is inferior to yolov8-animeface with the same parameters.
+Surprisingly enough, yolov5 is way more confident that yolov8. However, it also has way more false positives.
+
+
+Yolov8_animeface             |  Yolov5_anime
+:-------------------------:|:-------------------------:
+![](./demo/comparison/yolov8_animeface/test_priconne.png "Test on Priconne with yolov8")  |  ![](./demo/comparison/yolov5_anime/test_priconne.jpg "Test on Priconne with yolov5")
+![](./demo/comparison/yolov8_animeface/test_uma.png "Test on Uma with yolov8")  |  ![](./demo/comparison/yolov5_anime/test_uma.jpg "Test on Uma with yolov5")
+
+
+## Conclusion
+
+While this model performs greatly on my dataset, my metrics can differ from yours. One lighter model might be released on a later occasion.
+
+Based on YOLOv8 by
+Jocher, G., Chaurasia, A., & Qiu, J. (2023). Ultralytics YOLO (Version 8.0.0) [Computer software]. https://github.com/ultralytics/ultralytics

demo/comparison/credits.txt

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+Yolov5 model used created by zymk9 can be found here https://github.com/zymk9/yolov5_anime
+
+Picture 1 used for comparison: test_priconne.png, credits to CyGames.
+
+Picture 2 used for comparison: test_uma.png, credits to CyGames.

yolov8x6_animeface.pt

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