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https://github.com › Fifa Tryon

https://github.com/hasibzunair/fifa-tryon/releases/download/v1.0-models/vgg19-dcbb9e9d.pth

GitHub - hasibzunair/fifa-tryon: [BMVC'2022] Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On vgg19-dcbb9e9d.pth

pth 861MB

https://github.com › UX Decoder › DINOv

https://github.com/UX-Decoder/DINOv/releases/download/checkpoint/model_swinL.pth

GitHub - UX-Decoder/DINOv: [CVPR 2024] Official implementation of the paper "Visual In-context Learning" model

pth 756.6MB

https://github.com › Megvii BaseDetection › YOLOX

https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_x.pth

GitHub - Megvii-BaseDetection/YOLOX: YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/ github

pth 1.1GB

https://github.com/naver-ai/vidt/releases/download/v0.1-vidt/vidt_base_50.pth

GitHub - naver-ai/vidt Github

pth 688.2MB

https://github.com/naver-ai/vidt/releases/download/v0.1-vidt/vidt_small_50.pth

GitHub - naver-ai/vidt Github

pth 756.7MB

https://github.com › Megvii BaseDetection › YOLOX

https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth

GitHub - Megvii-BaseDetection/YOLOX: YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/ github

pth 1.1GB

https://github.com/naver-ai/vidt/releases/download/v0.1-vidt-plus/vidt_plus_base_det300.pth

GitHub - naver-ai/vidt Github

pth 711.3MB

https://github.com/naver-ai/vidt/releases/download/v0.1-vidt-plus/vidt_plus_small_det300.pth

GitHub - naver-ai/vidt Github

pth 711.3MB

https://github.com/naver-ai/vidt/releases/download/v0.1-vidt-plus-optimized/vidt_plus_swin_small_optimized.pth

GitHub - naver-ai/vidt ViDT+ (Swin-small)

pth 614.4MB

https://github.com › MegEngine › OMNet

https://github.com/megvii-research/OMNet/releases/download/V1.0.0/val_model_best.pth

GitHub - MegEngine/OMNet: OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration, ICCV 2021, MegEngine implementation. Github Release

pth 502.3MB

https://github.com/IDEA-Research/detrex-storage/releases/download/maskdino-v0.1.0/maskdino_r50_50ep_100q_celoss_hid1024_3s_semantic_ade20k_48.7miou.pth

GitHub - IDEA-Research/MaskDINO: [CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation" model

pth 502MB

https://github.com/IDEA-Research/detrex-storage/releases/download/maskdino-v0.1.0/maskdino_r50_50ep_100q_celoss_hid1024_3s_semantic_cityscapes_79.8miou.pth

GitHub - IDEA-Research/MaskDINO: [CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation" model

pth 502.4MB

https://github.com/IDEA-Research/detrex-storage/releases/download/maskdino-v0.1.0/maskdino_r50_50ep_300q_hid1024_3sd1_instance_maskenhanced_mask46.1ap_box51.5ap.pth

GitHub - IDEA-Research/MaskDINO: [CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation" model

pth 596.7MB