Rfdetr Basketball Player Ball Referee Detection

--- library_name: transformers license: apache-2.0 base_model: Roboflow/rf-detr-nano tags: - generated_from_trainer model-index: - name: rfdetr-basketball-player-ball-referee-detection results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rfdetr-basketball-player-ball-referee-detection This model is a fine-tuned version of [Roboflow/rf-detr-nano](https://huggingface.co/Roboflow/rf-detr-nano) on an labeled basketball object detection dataset from Roboflow. * Roboflow Dataset - https://universe.roboflow.com/roboflow-jvuqo/basketball-player-detection-3-ycjdo/dataset/18 * Same Dataset as above but exported to HF - https://huggingface.co/datasets/koppolusameer/basketball-coco-20260416 It achieves the following results on the evaluation set: - Loss: 11.6183 - Map: 0.4681 - Map 50: 0.7416 - Map 75: 0.5072 - Map Small: 0.3566 - Map Medium: 0.6316 - Map Large: -1.0 - Mar 1: 0.1966 - Mar 10: 0.5356 - Mar 100: 0.5675 - Mar Small: 0.4527 - Mar Medium: 0.7196 - Mar Large: -1.0 - Map Ball: 0.1805 - Map Player: 0.6228 - Map Referee: 0.601 ## Model description The model is the RF-DETR Nano Model (https://huggingface.co/Roboflow/rf-detr-seg-nano). ## Intended uses & limitations Model is finetuned to detect players, ball, and referee in basketball broadcast images. Below are a few limitations * The ball is only a few pixels in size and often gets mixed with the background and confuses the model (as described by mAP results) * Images are mostly from Celtics games and hence there may be a lack of training data diversity ## Training and evaluation data * Train, Validation, and Test Splits can be found here - https://huggingface.co/datasets/koppolusameer/basketball-coco-20260416 ## Training procedure * Model was trained for 10 epochs and with the same train, valid, and test split as in the dataset * Training data was randomly augmented before finalizing the train set, where the augmentations done on an image could include at least one of * Horizontal flipping * Brightness and Contrast changes * Saturation changes * Blurring * Shifting, scaling, and Rotation ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2...