Pre-downloaded model weights for Ayase modules that require non-HuggingFace downloads.
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3 excerptsPre-downloaded model weights for Ayase modules that require non-HuggingFace downloads.
ayase-models/ ├── dover/ # DOVER video quality assessment (ICCV 2023) │ ├── DOVER.pth # 229 MB — S-Lab License 1.0 │ └── convnext_tiny_1k_224_ema.pth # 110 MB — MIT ├── i2v_similarity/ # Image-to-Video similarity metrics │ ├── ViT-B-32.safetensors # 338 MB — MIT (OpenAI CLIP) │ ├── dinov2_vitb14_pretrain.pth # 331 MB — Apache 2.0 (Meta) │ └── alex.pth # 6 KB — BSD-2 (LPIPS) ├── dino_face_identity/ # DINOv2 face-crop identity indicator │ └── dinov2_vitb14_pretrain.pth # 331 MB — Apache 2.0 (Meta) ├── dreamsim/ # DreamSim perceptual similarity — base DINO backbone │ └── dino_vitbase16_pretrain.pth # 327 MB — Apache 2.0 (Meta, DINO) ├── advanced_flow/ # RAFT optical flow (ECCV 2020) │ ├── raft_large_C_T_SKHT_V2-ff5fadd5.pth # 21 MB — BSD-3 │ └── raft_small_C_T_V2-01064c6d.pth # 3.9 MB — BSD-3 ├── fast_vqa/ # FAST-VQA quality assessment (ECCV 2022) │ ├── FAST_VQA_3D_1_1.pth # 121 MB — MIT │ ├── FAST_VQA_B_1_4.pth # 121 MB — MIT │ └── FAST_VQA_M_1_4.pth # 105 MB — MIT ├── aesthetic_scoring/ # LAION aesthetic predictor │ └── sac+logos+ava1-l14-linearMSE.pth # 3.5 MB — MIT ├── video_memorability/ # Video memorability estimation │ └── dinov2_vits14_pretrain.pth # 84 MB — Apache 2.0 (Meta) ├── spectral/ # Spectral analysis │ └── dinov2_vits14_pretrain.pth # 84 MB — Apache 2.0 (Meta) ├── trajan/ # Point tracking (CoTracker2) │ └── cotracker2.pth # 194 MB — Apache 2.0 (Meta) ├── depth_map_quality/ # Monocular depth estimation │ ├── dpt_swin2_tiny_256.pt # 164 MB — MIT (Intel ISL MiDaS) │ └── midas_v21_small_256.pt # 82 MB — MIT (Intel ISL MiDaS) ├── depth_consistency/ # Temporal depth consistency │ ├── dpt_swin2_tiny_256.pt # 164 MB — MIT │ └── midas_v21_small_256.pt # 82 MB — MIT ├── motion_smoothness/ # RIFE motion smoothness │ └── flownet.pkl # Motion interpolation network ├── brightvq/ # BrightRate / BrightVQ HDR no-reference quality │ ├── brightrate_brightvq.pt # 161 MB — BrightRate regressor │ ├── CONTRIQUE_checkpoint25.tar # 107 MB — CONTRIQUE feature extractor │ ├── frames_modelparameters.mat # 8 KB — NIQE/HDR stats params │ ├── ViT-B-32.safetensors # 338 MB — MIT (OpenAI CLIP) │ ├── ViT-L-14.safetensors # 890 MB — MIT (OpenAI CLIP) │ ├── CLIP-IQA+_learned_prompts-603f3273.pth # 17 KB — CLIP-IQA+ prompts │ ├── CLIPIQA+_RN50_512-89f5d940.pth # 309 KB — CLIP-IQA+ RN50 weights │ └── CLIPIQA+_ViTL14_512-e66488f2.pth # 463 KB — CLIP-IQA+ ViT-L/14 weights ├── rqvqa/ # RQ-VQA rich quality-aware VQA (CVPR 2024 NTIRE) │ ├── LIQE.pt # 337 MB — LIQE feature extractor │ └── Swin_b_384_in22k_SlowFast_Fast_LSVQ.pth # 345 MB — Swin-B + SlowFast backbone ├── song_eval/ # SongEval song aesthetic evaluation │ └── model.safetensors # 96 MB — Apache 2.0 (ASLP-lab) ├── nisqa/ # NISQA non-intrusive speech quality (Interspeech 2021) │ └── nisqa.tar # 1 MB — MIT (TU-Berlin / Gabriel Mittag) ├── imagebind/ # ImageBind multimodal embedding (CVPR 2023) │ └── imagebind_huge.pth # 4.5 GB — CC-BY-NC 4.0 (Meta FAIR) ├── synchformer/ # Synchformer audio-visual sync (ICASSP 2024) │ └── 24-01-04T16-39-21.pt # 1.1 GB — MIT (Vladimir Iashin) ├── lip_sync/ # SyncNet lip-sync — LSE-C / LSE-D (reference-free) │ └── syncnet_v2.model # 55 MB — SyncNet v2 (ACCV 2016, research-only) ├── rtmpose_fidelity/ # RTMPose pose/gesture plausibility (rtmlib) │ ├── yolox_m.onnx # 101 MB — Apache 2.0 (OpenMMLab, YOLOX-m Human-Art) │ └── rtmpose_m.onnx # 54 MB — Apache 2.0 (OpenMMLab, RTMPose-m body) ├── gamival/ # GAMIVAL gaming-video quality │ ├── subjectiveDemo2_DMOS_Final.model │ └── subjectiveDemo2_DMOS_Final.npy ├── mc360iqa/ # 360-degree image quality regressors │ ├── CVIQ.pkl │ └── OIQA.pkl ├── mdvqa/ # MD-VQA fusion heads │ ├── LSVQ_rp0.pth │ └── MDVQA_LSVQ_TaoLive.pth ├── p1204/models/ # ITU-T P.1204 random-forest models │ ├── p1204_3/ │ └── p1204_3_dev/ ├── provqa/ # ProVQA checkpoint │ └── net_g_26400.pth ├── sama/ # SAMA spatial quality checkpoint │ └── SAMA-baseline_val-ltest_s_dev_v0.0.pth ├── serfiq/ # SER-FIQ runtime archive │ └── serfiq_model.zip ├── simplevqa/ # SimpleVQA checkpoint │ └── Swin_b_384_in22k_SlowFast_Fast_LSVQ.pth ├── stablevqa/ # StableVQA checkpoint │ └── stablevqa_checkpoint.pth ├── trajan/ # Trajan tracking models │ └── track_autoencoder_ckpt.npz ├── vfips/ # VFIPS perceptual metric │ ├── model.pytorch │ └── test_dmos.pkl ├── zoomvqa/ # ZoomVQA image/video checkpoints │ ├── iqa_best_29epoch_checkpoint.pth.tar │ └── vqa_best_29e_val-vqpve_s.pth ├── mj_video/ # MJ-Video evaluator source │ └── source-cc1d2c9587a620e9ebd3599ae4cdd21b5fd7c87a.zip ├── worldmodelbench/ # WorldModelBench evaluator runtime │ ├── worldmodelbench.json │ ├── vila-source-0f1426e8da9181e6e6653e10bc15f62d515fa2f6.zip │ └── s2wrapper-source-9c008a37540e761f53574b488979db6e49a64312.zip └── vbench2/ # Official VBench 2.0 runtime and checkpoints ├── source-45e79ec14e69a2187202c675d2dbce1a71843d53.zip ├── cotracker/source-82e02e8029753ad4ef13cf06be7f4fc5facdda4d.zip ├── raft/models.zip ├── retinaface/retinaface_resnet50_2020-07-20-f168fae3c.zip ├── arcface/resnet18_110.pth ├── torchvision/vgg19-dcbb9e9d.pth └── instance_anomaly_detector/model/ Total: ~11.8 GBLICENSE.mdPre-downloaded model weights for Ayase modules that require non-HuggingFace downloads.
AkaneTendo25/ayase-models