Trackio logbook, compact outputs, and artifact Bucket for reproducing Olaf-World (arXiv 2602.10104, OpenReview 5TiuerrwR8).
Niels Rogge
nielsr
AI & ML interests
Mainly interested in diving into complex Github repos and making AI easier and more accessible to everyone
Recent Activity
new activity about 13 hours ago
poet70/vla-sft-code-dreamzero:Replace incorrect model card with Genie Sim 3.0 card updated a Space about 15 hours ago
pwc-archive/README new activity about 15 hours ago
pwc-archive/papers-with-abstracts:Declare dataset license as CC BY-SA 4.0Organizations
![Unofficial Mistral Community [deprecated]'s profile picture](https://cdn-avatars.huggingface.co/v1/production/uploads/6141a88b3a0ec78603c9e784/HAdAzcs1CDB9OxgxT2W9K.png)
SigLIP improves upon CLIP with a sigmoid loss. Both English-only and multilingual checkpoints are released.
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Sigmoid Loss for Language Image Pre-Training
Paper • 2303.15343 • Published • 12 -
google/siglip-base-patch16-224
Zero-Shot Image Classification • 0.2B • Updated • 1.4M • 88 -
google/siglip-base-patch16-256
Zero-Shot Image Classification • 0.2B • Updated • 24.5k • 7 -
google/siglip-base-patch16-384
Zero-Shot Image Classification • 0.2B • Updated • 70.1k • 11
DPT 3.1 (MiDaS) models, leveraging state-of-the-art vision backbones such as BEiT and Swinv2
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Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
Paper • 1907.01341 • Published • 1 -
Intel/dpt-beit-large-512
Depth Estimation • 0.3B • Updated • 524 • 9 -
Intel/dpt-beit-large-384
Depth Estimation • 0.3B • Updated • 168 -
Intel/dpt-beit-base-384
Depth Estimation • 0.1B • Updated • 3.78k • 1
Collection of image captioning models
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Salesforce/blip-image-captioning-large
Image-to-Text • 0.5B • Updated • 486k • 1.49k -
microsoft/git-large-coco
Image-to-Text • 0.4B • Updated • 3.63k • 106 -
Salesforce/instructblip-vicuna-7b
Image-Text-to-Text • 8B • Updated • 6.31k • 102 -
Salesforce/blip2-flan-t5-xxl
Image-Text-to-Text • 12B • Updated • 890 • 94
DPT 3.0 (MiDaS) models, leveraging ViT and ViT-hybrid backbones
Depth Anything models, which are monocular depth estimation models trained on 62 million images
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Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 64 -
LiheYoung/depth-anything-large-hf
Depth Estimation • 0.3B • Updated • 319k • 65 -
LiheYoung/depth-anything-base-hf
Depth Estimation • 97.5M • Updated • 47.9k • 12 -
LiheYoung/depth-anything-small-hf
Depth Estimation • 24.8M • Updated • 57.5k • 36
Trackio logbook, compact outputs, and artifact Bucket for reproducing Olaf-World (arXiv 2602.10104, OpenReview 5TiuerrwR8).
Collection of image captioning models
-
Salesforce/blip-image-captioning-large
Image-to-Text • 0.5B • Updated • 486k • 1.49k -
microsoft/git-large-coco
Image-to-Text • 0.4B • Updated • 3.63k • 106 -
Salesforce/instructblip-vicuna-7b
Image-Text-to-Text • 8B • Updated • 6.31k • 102 -
Salesforce/blip2-flan-t5-xxl
Image-Text-to-Text • 12B • Updated • 890 • 94
SigLIP improves upon CLIP with a sigmoid loss. Both English-only and multilingual checkpoints are released.
-
Sigmoid Loss for Language Image Pre-Training
Paper • 2303.15343 • Published • 12 -
google/siglip-base-patch16-224
Zero-Shot Image Classification • 0.2B • Updated • 1.4M • 88 -
google/siglip-base-patch16-256
Zero-Shot Image Classification • 0.2B • Updated • 24.5k • 7 -
google/siglip-base-patch16-384
Zero-Shot Image Classification • 0.2B • Updated • 70.1k • 11
DPT 3.0 (MiDaS) models, leveraging ViT and ViT-hybrid backbones
DPT 3.1 (MiDaS) models, leveraging state-of-the-art vision backbones such as BEiT and Swinv2
-
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
Paper • 1907.01341 • Published • 1 -
Intel/dpt-beit-large-512
Depth Estimation • 0.3B • Updated • 524 • 9 -
Intel/dpt-beit-large-384
Depth Estimation • 0.3B • Updated • 168 -
Intel/dpt-beit-base-384
Depth Estimation • 0.1B • Updated • 3.78k • 1
Depth Anything models, which are monocular depth estimation models trained on 62 million images
-
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 64 -
LiheYoung/depth-anything-large-hf
Depth Estimation • 0.3B • Updated • 319k • 65 -
LiheYoung/depth-anything-base-hf
Depth Estimation • 97.5M • Updated • 47.9k • 12 -
LiheYoung/depth-anything-small-hf
Depth Estimation • 24.8M • Updated • 57.5k • 36