LibreRFDETRm Cls

--- license: apache-2.0 library_name: libreyolo tags: - image-classification - libreyolo - rfdetr datasets: - imagenette pipeline_tag: image-classification --- # LibreRFDETRm-cls LibreRFDETRM classification model trained on [imagenette](https://github.com/fastai/imagenette) (10-class ImageNet subset). ## Model Details | Property | Value | |----------|-------| | Architecture | LibreRFDETRM | | Task | Image Classification | | Input size | 224×224 | | Classes | 10 | | Top-1 (imagenette val) | 97.45% | | Top-5 (imagenette val) | — | | License | APACHE-2.0 | ## 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 LibreRFDETR model = LibreRFDETR("LibreRFDETRm-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 APACHE-2.0 — 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.

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library libreyolo · Repo LibreYOLO/LibreRFDETRm-cls