visdrone-yolov8s
dronefreak基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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visdrone-yolo11-det
EdgeFirst基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗visdrone-yolo26-det
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View details ↗visdrone-yolov8-det
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View details ↗yolo11-det
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View details ↗yolo26-det
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View details ↗yolov5-det
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View details ↗yolov8-det
EdgeFirst基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗litter-detection-yolov8
esapzoi- **Base Model**: yolov8n.pt - **Task**: Object Detection - **Classes**: Litter - **Framework**: PyTorch + Ultralytics YOLO Use this model for litter detection systems, environmental monitoring, and waste management applications.
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View details ↗chard
euler1729基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗caught-in-4k
eymen160基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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boxer
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗deformable-detr-box-supervised
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗deformable-detr-detic
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗detr-resnet-101
facebookDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
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View details ↗detr-resnet-101-dc5
facebookDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
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View details ↗detr-resnet-50
facebookDEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
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View details ↗detr-resnet-50-dc5
facebook基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗yolov5s-v7.0
fcakyon基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗yolov8_animeface
FuyucchiDataset was made created with 10 000 images manually annotated from safebooru. Split was train 70, val 20, test 10. 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. 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.
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View details ↗smart-parking-weights
GaryFer基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗kvasir_capsule_only_bbox
goodcasper基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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