LibreYOLO9t Cls

Classes

Usage

from libreyolo import LibreYOLO9

model = LibreYOLO9("LibreYOLO9t-cls.pt", task="classify")
result = model.predict("image.jpg")
print(result)  # top-1 class and confidence

Training

• Dataset: imagenette160 (train: 9,469 images · val: 3,925 images · 10 classes)
• Epochs: 30
• Optimizer: AdamW
• Scheduler: Warm cosine (5 warmup epochs)
• Augmentation: RandomResizedCrop + TrivialAugmentWide + RandomErasing
• Transfer: pretrained backbone (COCO detection weights)

Limitations

These weights are trained on imagenette (10 classes, ~9.5k images), a fast.ai benchmark subset of ImageNet. They serve as a functional demo of the classification pipeline. For production use we recommend fine-tuning on your own dataset. A full ImageNet-1k training run is planned when compute budget allows.

License

MIT — see LICENSE. The imagenette dataset is derived from ImageNet; the original ImageNet terms apply to the training data.

--- license: mit library_name: libreyolo tags: - image-classification - libreyolo - yolo9 datasets: - imagenette pipeline_tag: image-classification --- # LibreYOLO9t-cls LibreYOLO9T classification model trained on [imagenette](https://github.com/fastai/imagenette) (10-class ImageNet subset). ## Model Details | Property | Value | |----------|-------| | Architecture | LibreYOLO9T | | Task | Image Classification | | Input size | 224×224 | | Classes | 10 | | Top-1 (imagenette val) | 94.93% | | Top-5 (imagenette val) | — | | License | MIT | ## Classes | ID | Name | |----|------| | 0 | tench | | 1 | English springer | | 2 | cassette player | | 3 | chain saw | | 4 | church | | 5 | French horn | | 6 | garbage truck | | 7 | gas pump | | 8 | golf ball | | 9 | parachute | ## Usage ```python from libreyolo import LibreYOLO9 model = LibreYOLO9("LibreYOLO9t-cls.pt", task="classify") result = model.predict("image.jpg") print(result) # top-1 class and confidence ``` ## Training - **Dataset**: imagenette160 (train: 9,469 images · val: 3,925 images · 10 classes) - **Epochs**: 30 - **Optimizer**: AdamW - **Scheduler**: Warm cosine (5 warmup epochs) - **Augmentation**: RandomResizedCrop + TrivialAugmentWide + RandomErasing - **Transfer**: pretrained backbone (COCO detection weights) ## Limitations These weights are trained on **imagenette** (10 classes, ~9.5k images), a fast.ai benchmark subset of ImageNet. They serve as a functional demo of the classification pipeline. For production use we recommend fine-tuning on your own dataset. A full ImageNet-1k training run is planned when compute budget allows. ## License MIT — see [LICENSE](https://github.com/LibreYOLO/LibreYOLO/blob/release/LICENSE). The imagenette dataset is derived from ImageNet; the original [ImageNet terms](https://image-net.org/download) apply to the training data.