# Video Classification Models: Open-Weight AI Source: https://savrn.com/models/tasks/video-classification Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Models by Task # Video Classification Models 61 open-weight video classification models in the SAVRN Model Hub, with AI at Meta, Multimedia Computing Group-Nanjing University and Microsoft publishing the most. 61Models 18Publishers 9M to 6.4BParameter range 5Licenses ## SAVRN's Take A video classifier takes a clip and returns a label for what it contains, or for the smallest entry on this page, whether a face has been tampered with. Fifty-eight models carry the tag, from YUNJE SEO's 9 million parameter deepfake detector to OpenGVLab's 6.4 billion parameter InternVideo2-Stage2_6B; four are priced in the SAVRN Index. Nanjing University's Multimedia Computing Group pulls 414,669 downloads a month on videomae-base, a self-supervised pretraining checkpoint rather than a finished classifier, and 114,354 on its Kinetics-400 fine-tuned sibling. AI at Meta takes the next two slots with V-JEPA 2 at 182,980 and 137,095, and Microsoft's X-CLIP pair follows at 109,968 and 99,565. Memory is not the constraint. videomae-base wants 0.2 GB at 16-bit, the 1 billion parameter vjepa2-vitg-fpc64-256 wants 2.5 GB, and even InternVideo2 fits in 15.3 GB at 16-bit or 3.8 GB at 4-bit. Every priced entry lands on one MI300X at $1.85 an hour, so the hardware question is frame throughput, not weight storage. X-CLIP shows the variable that matters: patch32 was trained on 8 frames per clip at 224x224, the patch16 zero-shot version on 32. The license split matters more. Thirty of the 58 are MIT and five Apache-2.0, but 21 sit under CC-BY-NC-4.0, and that noncommercial line runs through both VideoMAE checkpoints at the top of the chart. A commercial deployment shortlists Meta (MIT on the vitl checkpoints, Apache-2.0 on the vitg), Microsoft's X-CLIP pair and Google's ViViT, all MIT, then checks whether the checkpoint carries a classification head and which dataset tuned it, Kinetics-400 or Something-Something-V2. SAVRN Research, 2026-09-18 ## Most Downloaded | Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit | | --- | --- | --- | --- | --- | --- | | [videomae-base](https://savrn.com/models/videomae-base) | Multimedia Computing Group-Nanjing University | 94M | cc-by-nc-4.0 | 414.7k | 1x MI300X, $1.85/hr | | [vjepa2-vitl-fpc64-256](https://savrn.com/models/vjepa2-vitl-fpc64-256) | AI at Meta | 326M | mit | 183k | 1x MI300X, $1.85/hr | | [vjepa2-vitg-fpc64-256](https://savrn.com/models/vjepa2-vitg-fpc64-256) | AI at Meta | 1B | apache-2.0 | 137.1k | 1x MI300X, $1.85/hr | | [videomae-base-finetuned-kinetics](https://savrn.com/models/videomae-base-finetuned-kinetics) | Multimedia Computing Group-Nanjing University | 87M | cc-by-nc-4.0 | 114.4k | 1x MI300X, $1.85/hr | | [xclip-base-patch32](https://savrn.com/models/xclip-base-patch32) | Microsoft | 197M | mit | 110k | 1x MI300X, $1.85/hr | | [xclip-base-patch16-zero-shot](https://savrn.com/models/xclip-base-patch16-zero-shot) | Microsoft | 195M | mit | 99.6k | 1x MI300X, $1.85/hr | | [vivit-b-16x2-kinetics400](https://savrn.com/models/vivit-b-16x2-kinetics400) | Google | — | mit | 96.9k | — | | [vjepa2-vitl-fpc16-256-ssv2](https://savrn.com/models/vjepa2-vitl-fpc16-256-ssv2) | AI at Meta | 375M | mit | 39.4k | 1x MI300X, $1.85/hr | | [vjepa2.1-vit-large-384](https://savrn.com/models/vjepa2-1-vit-large-384) | Antonio Apicella | 328M | mit | 31.5k | 1x MI300X, $1.85/hr | | [xclip-base-patch32-16-frames](https://savrn.com/models/xclip-base-patch32-16-frames) | Microsoft | 197M | mit | 23.6k | 1x MI300X, $1.85/hr | ## Licenses | License | Models | Commercial use | | --- | --- | --- | | [mit](https://savrn.com/models/licenses/mit) | 30 | Yes | | [cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) | 23 | Not without separate permission | | [apache-2.0](https://savrn.com/models/licenses/apache-2-0) | 6 | Yes | | other | 1 | Read the license | | not stated | 1 | Not stated | ## Who Publishes Them | Publisher | Models | | --- | --- | | [AI at Meta](https://savrn.com/model-publishers/facebook) | 11 | | [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) | 10 | | [Microsoft](https://savrn.com/model-publishers/microsoft) | 8 | | [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) | 6 | | [OpenGVLab](https://savrn.com/model-publishers/opengvlab) | 5 | | [Google](https://savrn.com/model-publishers/google) | 5 | ## All 61 Models, Page 1 of 2 Model · Video classification ### [videomae-base](https://savrn.com/models/videomae-base) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of… Open weights cc-by-nc-4.0 94M parameters transformers [View model](https://savrn.com/models/videomae-base) Model · Video classification ### [vjepa2-vitl-fpc64-256](https://savrn.com/models/vjepa2-vitl-fpc64-256) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2… Open weights mit 326M parameters transformers [View model](https://savrn.com/models/vjepa2-vitl-fpc64-256) Model · Video classification ### [vjepa2-vitg-fpc64-256](https://savrn.com/models/vjepa2-vitg-fpc64-256) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2… Open weights apache-2.0 1B parameters transformers [View model](https://savrn.com/models/vjepa2-vitg-fpc64-256) Model · Video classification ### [videomae-base-finetuned-kinetics](https://savrn.com/models/videomae-base-finetuned-kinetics) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to… Open weights cc-by-nc-4.0 87M parameters transformers [View model](https://savrn.com/models/videomae-base-finetuned-kinetics) Model · Video classification ### [xclip-base-patch32](https://savrn.com/models/xclip-base-patch32) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 197M parameters 77 tokens transformers [View model](https://savrn.com/models/xclip-base-patch32) Model · Video classification ### [xclip-base-patch16-zero-shot](https://savrn.com/models/xclip-base-patch16-zero-shot) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (base-sized, patch resolution of 16) trained on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 32 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video… Open weights mit 195M parameters 77 tokens transformers [View model](https://savrn.com/models/xclip-base-patch16-zero-shot) Model · Video classification ### [vjepa2-vitl-fpc16-256-ssv2](https://savrn.com/models/vjepa2-vitl-fpc16-256-ssv2) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. This is V-JEPA 2 ViT-L 256 model with video classification head pretrained on Something-Something-V2 dataset. To run V-JEPA 2 model, ensure you have installed the latest transformers Open weights mit 375M parameters transformers [View model](https://savrn.com/models/vjepa2-vitl-fpc16-256-ssv2) Model · Video classification ### [vjepa2.1-vit-large-384](https://savrn.com/models/vjepa2-1-vit-large-384) [Antonio Apicella](https://savrn.com/model-publishers/apiantonio) A HuggingFace-format conversion of Meta AI's V-JEPA 2.1 ViT-L/16 video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the transformers-compatible packaging plus a documented numerical validation against the original implementation. An equivalent community port already exists (Dev-Jahn/vjepa2.1-vitl-fpc64-384). This repository adds an independently reproduced conversion together with the validation results below. Forward outputs match the existing port to all reported digits. The only structural change is that the fused QKV projection of each attention block is split into separate query / key / value… Open weights mit 328M parameters transformers [View model](https://savrn.com/models/vjepa2-1-vit-large-384) Model · Video classification ### [xclip-base-patch32-16-frames](https://savrn.com/models/xclip-base-patch32-16-frames) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 16 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 197M parameters 77 tokens transformers [View model](https://savrn.com/models/xclip-base-patch32-16-frames) Model · Video classification ### [videomae-large-finetuned-kinetics](https://savrn.com/models/videomae-large-finetuned-kinetics) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to… Open weights cc-by-nc-4.0 304M parameters transformers [View model](https://savrn.com/models/videomae-large-finetuned-kinetics) Model · Video classification ### [videomae-large](https://savrn.com/models/videomae-large) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of… Open weights cc-by-nc-4.0 343M parameters transformers [View model](https://savrn.com/models/videomae-large) Model · Video classification ### [ms-eff-gcvit-deepfake-b5-celeb-df-v2](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-celeb-df-v2) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 53M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-celeb-df-v2) Model · Video classification ### [ms-eff-gcvit-deepfake-b0-celeb-df-v2](https://savrn.com/models/ms-eff-gcvit-deepfake-b0-celeb-df-v2) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 9M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b0-celeb-df-v2) Model · Video classification ### [ms-eff-gcvit-deepfake-b0-kodf](https://savrn.com/models/ms-eff-gcvit-deepfake-b0-kodf) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 9M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b0-kodf) Model · Video classification ### [ms-eff-gcvit-deepfake-b5-kodf](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-kodf) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 53M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-kodf) Model · Video classification ### [ms-eff-gcvit-deepfake-b0-ff-plus-plus](https://savrn.com/models/ms-eff-gcvit-deepfake-b0-ff-plus-plus) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 9M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b0-ff-plus-plus) Model · Video classification ### [Cosmos-Embed1-448p-anomaly-detection](https://savrn.com/models/cosmos-embed1-448p-anomaly-detection) [NVIDIA](https://savrn.com/model-publishers/nvidia) Cosmos-Embed1 is a joint video-text embedder tailored for physical AI. It can be used for text-to-video retrieval, inverse video search, semantic deduplication, zero-shot and k-nearest-neighbors (kNN) classification, and as a base model for video curation tasks. It has state-of-the-art (SOTA) performance on autonomous vehicle (AV) and robotics datasets, while maintaining competitive performance in general domains. A fine-tuned variant is also provided for video anomaly detection and classification. This model is ready for commercial use. The Cosmos-Embed1 release includes the following embedders: Note: while each checkpoint was optimized at a specific fixed resolution (and default to… Open weights other 1.2B parameters cosmos [View model](https://savrn.com/models/cosmos-embed1-448p-anomaly-detection) Model · Video classification ### [xclip-large-patch14](https://savrn.com/models/xclip-large-patch14) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (large-sized, patch resolution of 14) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 576M parameters 77 tokens transformers [View model](https://savrn.com/models/xclip-large-patch14) Model · Video classification ### [xclip-base-patch16](https://savrn.com/models/xclip-base-patch16) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 195M parameters 77 tokens transformers [View model](https://savrn.com/models/xclip-base-patch16) Model · Video classification ### [VideoMAEv2-Base](https://savrn.com/models/videomaev2-base) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) VideoMAEv2-Base model pre-trained for 800 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://arxiv.org/abs/2203.12602) by Wang et al. and first released in GitHub. You can use the raw model for video feature extraction. Here is how to use this model to extract a video feature Open weights cc-by-nc-4.0 86M parameters [View model](https://savrn.com/models/videomaev2-base) Model · Video classification ### [ms-eff-gcvit-deepfake-b5-ff-plus-plus](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-ff-plus-plus) [YUNJE SEO](https://savrn.com/model-publishers/koreapeter) Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for deepfake detection. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both local artifacts (textures, blending seams) and global artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both static images and video at the frame level. - Frame-level — one model handles both images and videos (frame-level inference + aggregation). - Cross-domain — robust on both East-Asian (KoDF) and Western (Celeb-DF-v2, FaceForensics++) faces. - Two variants — Fast (b0) for… Open weights mit 53M parameters transformers [View model](https://savrn.com/models/ms-eff-gcvit-deepfake-b5-ff-plus-plus) Model · Video classification ### [vjepa2.1-vit-base-384](https://savrn.com/models/vjepa2-1-vit-base-384) [Antonio Apicella](https://savrn.com/model-publishers/apiantonio) A HuggingFace-format conversion of Meta AI's V-JEPA 2.1 ViT-B/16 video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the transformers-compatible packaging plus a documented numerical validation against the original implementation. An equivalent community port already exists (Dev-Jahn/vjepa2.1-vitb-fpc64-384). This repository adds an independently reproduced conversion together with the validation results below. The only structural change is that the fused QKV projection of each attention block is split into separate query / key / value matrices, following the convention used by transformers. This is a… Open weights mit 110M parameters transformers [View model](https://savrn.com/models/vjepa2-1-vit-base-384) Model · Video classification ### [videomae-base-finetuned-kinetics-violence-nonviolence-tuned](https://savrn.com/models/videomae-base-finetuned-kinetics-violence-nonviolence-tuned) [Limon Alfredo](https://savrn.com/model-publishers/cliffer1) This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - trainingsteps: 348 - Transformers 4.49.0 - Pytorch 2.6.0+cu126 - Datasets 3.3.2 - Tokenizers 0.21.0 Open weights cc-by-nc-4.0 86M parameters transformers [View model](https://savrn.com/models/videomae-base-finetuned-kinetics-violence-nonviolence-tuned) Model · Video classification ### [vjepa2-vitg-fpc64-384](https://savrn.com/models/vjepa2-vitg-fpc64-384) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2… Open weights apache-2.0 1B parameters transformers [View model](https://savrn.com/models/vjepa2-vitg-fpc64-384) Model · Video classification ### [VideoMAEv2-Huge](https://savrn.com/models/videomaev2-huge) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) VideoMAEv2-Huge model pre-trained for 1200 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://arxiv.org/abs/2203.12602) by Wang et al. and first released in GitHub. You can use the raw model for video feature extraction. Here is how to use this model to extract a video feature Open weights cc-by-nc-4.0 632M parameters [View model](https://savrn.com/models/videomaev2-huge) Model · Video classification ### [VideoMAEv2-Large](https://savrn.com/models/videomaev2-large) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) VideoMAEv2-Large model pre-trained for 800 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://arxiv.org/abs/2203.12602) by Wang et al. and first released in GitHub. You can use the raw model for video feature extraction. Here is how to use this model to extract a video feature Open weights cc-by-nc-4.0 304M parameters [View model](https://savrn.com/models/videomaev2-large) Model · Video classification ### [vi-sign-language-videomae-base](https://savrn.com/models/vi-sign-language-videomae-base) [Star Duong](https://savrn.com/model-publishers/star092304) This repository houses a fine-tuned VideoMAE (Base) model optimized for multi-class Vietnamese Sign Language Recognition (VSLR). The model architecture adapts self-supervised video representations to accurately classify short video clips of sign gestures into distinct Vietnamese text labels. The model processes short video sequences by partitioning them into spatiotemporal patches, mapping sequential gestures (such as "Ăn", "Bệnh viện", "Xin lỗi") to their corresponding semantic classes. The training routine was monitored closely across key evaluation metrics to prevent overfitting while maximizing classification accuracy on the validation split. The plot below illustrates the progression… Open weights mit 86M parameters transformers [View model](https://savrn.com/models/vi-sign-language-videomae-base) Model · Video classification ### [vjepa2-vith-fpc64-256](https://savrn.com/models/vjepa2-vith-fpc64-256) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. To run V-JEPA 2 model, ensure you have installed the latest transformers: V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64. To load an image, simply copy the image to the desired number of frames. For more code examples, please refer to the V-JEPA 2… Open weights mit 654M parameters transformers [View model](https://savrn.com/models/vjepa2-vith-fpc64-256) Model · Video classification ### [kandinsky-videomae-large-camera-motion](https://savrn.com/models/kandinsky-videomae-large-camera-motion) [AI Forever](https://savrn.com/model-publishers/ai-forever) VideoMAE model(large) variant that has been finetuned for multi-label video classification (a video can belong to multiple classes simultaneously) for camera motion classification on internal dataset. The model predicts 18 different camera motion 'arcleft', 'arcright', 'dollyin', 'dollyout', 'panleft', 'panright', 'pedestaldown', 'pedestalup', 'rollleft', 'rollright', 'static', 'tiltdown', 'tiltup', 'truckleft', 'truckright', 'undefined', 'zoomin', 'zoomout' and and 3 shot type classes: 'pov', 'shake', 'track'. Model was trained to associate entire video with camera labels, not frame-level motions(!): [input video] -> label/labels (because multilabel) for all video. So, if this camera… Open weights 304M parameters transformers [View model](https://savrn.com/models/kandinsky-videomae-large-camera-motion) Model · Video classification ### [vjepa2-vitg-fpc64-384-ssv2](https://savrn.com/models/vjepa2-vitg-fpc64-384-ssv2) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. This is V-JEPA 2 ViT-g 384 model with video classification head pretrained on Something-Something-V2 dataset. To run V-JEPA 2 model, ensure you have installed the latest transformers Open weights mit 1.1B parameters transformers [View model](https://savrn.com/models/vjepa2-vitg-fpc64-384-ssv2) Model · Video classification ### [vjepa2-vitl-fpc32-256-diving48](https://savrn.com/models/vjepa2-vitl-fpc32-256-diving48) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. This is V-JEPA 2 ViT-L 256 model with video classification head pretrained on Diving 48 dataset. To run V-JEPA 2 model, ensure you have installed the latest transformers Open weights mit 375M parameters transformers [View model](https://savrn.com/models/vjepa2-vitl-fpc32-256-diving48) Model · Video classification ### [videomae-violence-detector](https://savrn.com/models/videomae-violence-detector) [Oleg Radzhabov](https://savrn.com/model-publishers/happygook) This model is a fine-tuned version of MCG-NJU/videomae-base for binary violence classification (violent / non-violent). It builds on Nikeytas/videomae-crime-detector-production-v1, which was itself fine-tuned from videomae-base on a subset of UCF Crime. Starting from that checkpoint, this model was further fine-tuned on the Bus Violence Dataset to close the domain gap to public-transport surveillance footage. - UCF Crime (jinmang2/ucfcrime) — inherited from the base checkpoint - Bus Violence Dataset (Zenodo) — real moving-bus footage, binary violent / non-violent labels, used for domain-specific fine-tuning Evaluated on a held-out Bus Violence Dataset test split (n = 280). The base… Open weights mit 86M parameters [View model](https://savrn.com/models/videomae-violence-detector) Model · Video classification ### [videomae-base-ssv2](https://savrn.com/models/videomae-base-ssv2) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained on Something-Something-v2 for 2400 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of… Open weights cc-by-nc-4.0 94M parameters transformers [View model](https://savrn.com/models/videomae-base-ssv2) Model · Video classification ### [videomae-base-finetuned-ucf101-subset](https://savrn.com/models/videomae-base-finetuned-ucf101-subset) [Hon Nguyen](https://savrn.com/model-publishers/tihon-nth) This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - trainingsteps: 370 - Transformers 5.16.1 - Pytorch 2.14.0+cu126 - Datasets 5.0.1 - Tokenizers 0.23.2 Open weights cc-by-nc-4.0 86M parameters transformers [View model](https://savrn.com/models/videomae-base-finetuned-ucf101-subset) Model · Video classification ### [InternVideo2-Stage2_6B](https://savrn.com/models/internvideo2-stage2-6b) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) This repository contains the 6B model of the paper InternVideo2 in stage 2. Code: https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/multimodality Please refer to https://github.com/OpenGVLab/InternVideo/blob/main/InternVideo2/multimodality/INSTALL.md Open weights mit 6.4B parameters [View model](https://savrn.com/models/internvideo2-stage2-6b) Model · Video classification ### [finetuned-ucf101-subset](https://savrn.com/models/finetuned-ucf101-subset) [Hon Nguyen](https://savrn.com/model-publishers/tihon-nth) This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - trainingsteps: 370 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.1 Open weights cc-by-nc-4.0 86M parameters transformers [View model](https://savrn.com/models/finetuned-ucf101-subset) Model · Video classification ### [vjepa2-vitg-fpc32-384-diving48](https://savrn.com/models/vjepa2-vitg-fpc32-384-diving48) [AI at Meta](https://savrn.com/model-publishers/facebook) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. This is V-JEPA 2 ViT-g 384 model with video classification head pretrained on Diving 48 dataset. To run V-JEPA 2 model, ensure you have installed the latest transformers Open weights mit 1.1B parameters transformers [View model](https://savrn.com/models/vjepa2-vitg-fpc32-384-diving48) Model · Video classification ### [videomae-large-finetuned-deepfake-subset](https://savrn.com/models/videomae-large-finetuned-deepfake-subset) [Mai](https://savrn.com/model-publishers/shylhy) This model is a fine-tuned version of MCG-NJU/videomae-large on the Deepfake Detection Challenge dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - trainingsteps: 4470 - mixedprecisiontraining: Native AMP - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1 Open weights cc-by-nc-4.0 304M parameters transformers [View model](https://savrn.com/models/videomae-large-finetuned-deepfake-subset) Model · Video classification ### [vjepa2.1-vit-giant-384](https://savrn.com/models/vjepa2-1-vit-giant-384) [Antonio Apicella](https://savrn.com/model-publishers/apiantonio) A HuggingFace-format conversion of Meta AI's V-JEPA 2.1 ViT-g/16 video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the transformers-compatible packaging plus a documented numerical validation against the original implementation. No prior HuggingFace port of this variant existed at the time of upload. The only structural change is that the fused QKV projection of each attention block is split into separate query / key / value matrices, following the convention used by transformers. This is a re-parameterization, not a change of weights. It is also convenient downstream: PEFT adapters apply to… Open weights mit 1.1B parameters transformers [View model](https://savrn.com/models/vjepa2-1-vit-giant-384) Model · Video classification ### [VideoMAEv2-giant](https://savrn.com/models/videomaev2-giant) [OpenGVLab](https://savrn.com/model-publishers/opengvlab) VideoMAEv2-giant model pre-trained for 1200 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://arxiv.org/abs/2203.12602) by Wang et al. and first released in GitHub. You can use the raw model for video feature extraction. Here is how to use this model to extract a video feature Open weights cc-by-nc-4.0 1B parameters [View model](https://savrn.com/models/videomaev2-giant) Model · Video classification ### [videomae-base-short](https://savrn.com/models/videomae-base-short) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained on Kinetics-400 for 800 epochs in a self-supervised way. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed-size… Open weights cc-by-nc-4.0 94M parameters transformers [View model](https://savrn.com/models/videomae-base-short) Model · Video classification ### [vivit-b-16x2-kinetics400](https://savrn.com/models/vivit-b-16x2-kinetics400) [Google](https://savrn.com/model-publishers/google) ViViT model as introduced in the paper ViViT: A Video Vision Transformer by Arnab et al. and first released in this repository. Disclaimer: The team releasing ViViT did not write a model card for this model so this model card has been written by the Hugging Face team. ViViT is an extension of the Vision Transformer (ViT) to video. We refer to the paper for details. The model is mostly meant to intended to be fine-tuned on a downstream task, like video classification. See the model hub to look for fine-tuned versions on a task that interests you. For code examples, we refer to the documentation. Open weights mit transformers [View model](https://savrn.com/models/vivit-b-16x2-kinetics400) Model · Video classification ### [vivit-b-16x2](https://savrn.com/models/vivit-b-16x2) [Google](https://savrn.com/model-publishers/google) ViViT model as introduced in the paper ViViT: A Video Vision Transformer by Arnab et al. and first released in this repository. Disclaimer: The team releasing ViViT did not write a model card for this model so this model card has been written by the Hugging Face team. ViViT is an extension of the Vision Transformer (ViT) to video. We refer to the paper for details. The model is mostly meant to intended to be fine-tuned on a downstream task, like video classification. See the model hub to look for fine-tuned versions on a task that interests you. For code examples, we refer to the documentation. Open weights mit transformers [View model](https://savrn.com/models/vivit-b-16x2) Model · Video classification ### [timesformer-base-finetuned-k600](https://savrn.com/models/timesformer-base-finetuned-k600) [AI at Meta](https://savrn.com/model-publishers/facebook) TimeSformer model pre-trained on Kinetics-600. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository. Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by fcakyon. You can use the raw model for video classification into one of the 600 possible Kinetics-600 labels. Here is how to use this model to classify a video: For more code examples, we refer to the documentation. Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/timesformer-base-finetuned-k600) Model · Video classification ### [timesformer-base-finetuned-k400](https://savrn.com/models/timesformer-base-finetuned-k400) [AI at Meta](https://savrn.com/model-publishers/facebook) TimeSformer model pre-trained on Kinetics-400. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository. Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by fcakyon. You can use the raw model for video classification into one of the 400 possible Kinetics-400 labels. Here is how to use this model to classify a video: For more code examples, we refer to the documentation. Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/timesformer-base-finetuned-k400) Model · Video classification ### [xclip-large-patch14-16-frames](https://savrn.com/models/xclip-large-patch14-16-frames) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (large-sized, patch resolution of 14) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 16 frames per video, at a resolution of 336x336. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 77 tokens transformers [View model](https://savrn.com/models/xclip-large-patch14-16-frames) Model · Video classification ### [VJEPA2-ViTL-SSv2-CoreAI](https://savrn.com/models/vjepa2-vitl-ssv2-coreai) [Daisuke Majima (MLBoy)](https://savrn.com/model-publishers/mlboydaisuke) Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's coreai-torch (LLMs: coreai.llm.export) into.aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta 26A5353q, 2026-06-11). This model has no row on DeviceMark, the on-device LLM leaderboard. V-JEPA 2 (Meta AI) running natively on the Apple Core AI engine — the zoo's first world model: a self-supervised video encoder that learns by predicting in representation space (JEPA), here with the Something-Something v2 action head (174… Open weights mit coreai [View model](https://savrn.com/models/vjepa2-vitl-ssv2-coreai) Model · Video classification ### [videomae-base-finetuned-ssv2](https://savrn.com/models/videomae-base-finetuned-ssv2) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 2400 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something-v2. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are… Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/videomae-base-finetuned-ssv2) Model · Video classification ### [vjepa2-vitl-fpc16-256-ssv2](https://savrn.com/models/vjepa2-vitl-fpc16-256-ssv2-2) [Pavel Iakubovskii](https://savrn.com/model-publishers/qubvel-hf) A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. This is V-JEPA 2 model with video classification head pretrained on Something-Something-V2 dataset. To run V-JEPA 2 model, ensure you have installed the latest transformers Open weights mit 375M parameters transformers [View model](https://savrn.com/models/vjepa2-vitl-fpc16-256-ssv2-2) Model · Video classification ### [videoprism-base-f16r288](https://savrn.com/models/videoprism-base-f16r288) [Google](https://savrn.com/model-publishers/google) Blog: https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/ VideoPrism is a foundational video encoder that enables state-of-the-art performance on a large variety of video understanding tasks. It takes video frames as input and outputs compact embeddings of the frames, which one can conveniently feed into classifiers, LLMs, retrieval models, etc. When tested on 33 public video understanding benchmarks over four task categories, a single frozen VideoPrism checkpoint outperforms previous best-performing foundation models on 31 of them, with no fine-tuning on target task datasets. We release the following model variants: VideoPrism-B/L are the… Open weights apache-2.0 videoprism [View model](https://savrn.com/models/videoprism-base-f16r288) Model · Video classification ### [videomae-small-finetuned-kinetics](https://savrn.com/models/videomae-small-finetuned-kinetics) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to… Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/videomae-small-finetuned-kinetics) Model · Video classification ### [videoprism-lvt-large-f8r288](https://savrn.com/models/videoprism-lvt-large-f8r288) [Google](https://savrn.com/model-publishers/google) Blog: https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/ VideoPrism is a foundational video encoder that enables state-of-the-art performance on a large variety of video understanding tasks. It takes video frames as input and outputs compact embeddings of the frames, which one can conveniently feed into classifiers, LLMs, retrieval models, etc. When tested on 33 public video understanding benchmarks over four task categories, a single frozen VideoPrism checkpoint outperforms previous best-performing foundation models on 31 of them, with no fine-tuning on target task datasets. We release the following model variants: VideoPrism-B/L are the… Open weights apache-2.0 videoprism [View model](https://savrn.com/models/videoprism-lvt-large-f8r288) Model · Video classification ### [videoprism-lvt-base-f16r288](https://savrn.com/models/videoprism-lvt-base-f16r288) [Google](https://savrn.com/model-publishers/google) Blog: https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/ VideoPrism is a foundational video encoder that enables state-of-the-art performance on a large variety of video understanding tasks. It takes video frames as input and outputs compact embeddings of the frames, which one can conveniently feed into classifiers, LLMs, retrieval models, etc. When tested on 33 public video understanding benchmarks over four task categories, a single frozen VideoPrism checkpoint outperforms previous best-performing foundation models on 31 of them, with no fine-tuning on target task datasets. We release the following model variants: VideoPrism-B/L are the… Open weights apache-2.0 videoprism [View model](https://savrn.com/models/videoprism-lvt-base-f16r288) Model · Video classification ### [xclip-base-patch16-16-frames](https://savrn.com/models/xclip-base-patch16-16-frames) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 16 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 77 tokens transformers [View model](https://savrn.com/models/xclip-base-patch16-16-frames) Model · Video classification ### [videomae-small-finetuned-ssv2](https://savrn.com/models/videomae-small-finetuned-ssv2) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 2400 epochs in a self-supervised way and fine-tuned in a supervised way on Something-Something V2. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are… Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/videomae-small-finetuned-ssv2) Model · Video classification ### [timesformer-base-finetuned-ssv2](https://savrn.com/models/timesformer-base-finetuned-ssv2) [AI at Meta](https://savrn.com/model-publishers/facebook) TimeSformer model pre-trained on Something Something v2. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository. Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by fcakyon. You can use the raw model for video classification into one of the 174 possible Something Something v2 labels. Here is how to use this model to classify a video: For more code examples, we refer to the documentation. Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/timesformer-base-finetuned-ssv2) Model · Video classification ### [videomae-huge-finetuned-kinetics](https://savrn.com/models/videomae-huge-finetuned-kinetics) [Multimedia Computing Group-Nanjing University](https://savrn.com/model-publishers/mcg-nju) VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to… Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/videomae-huge-finetuned-kinetics) Model · Video classification ### [xclip-base-patch16-kinetics-600](https://savrn.com/models/xclip-base-patch16-kinetics-600) [Microsoft](https://savrn.com/model-publishers/microsoft) X-CLIP model (base-sized, patch resolution of 16) trained fully-supervised on Kinetics-600. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository. This model was trained using 8 frames per video, at a resolution of 224x224. Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team. X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully… Open weights mit 77 tokens transformers [View model](https://savrn.com/models/xclip-base-patch16-kinetics-600) Model · Video classification ### [chimpact-thesis-models](https://savrn.com/models/chimpact-thesis-models) [Camila Danna Ticona Sánchez](https://savrn.com/model-publishers/rengatitos) Modelos de tesis sobre ChimpACT Release v1 (23 comportamientos, multi-etiqueta, split oficial por video). Cada carpeta /models/ / contiene best.pt, spec.json (thresholds por clase), metrics.json (métricas de test e historial por época) y trainingcurve.png. Dashboard y API: https://huggingface.co/spaces/Rengatitos/chimpact-thesis-dashboard Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/chimpact-thesis-models) Model · Video classification ### [DeMamba-GenVideo100K](https://savrn.com/models/demamba-genvideo100k) [Ahsan Habib](https://savrn.com/model-publishers/ahsanz) Open weights apache-2.0 pytorch [View model](https://savrn.com/models/demamba-genvideo100k) ## Questions ### Which Video classification models are most downloaded? By monthly downloads reported by the Hugging Face Hub: videomae-base (414.7k); vjepa2-vitl-fpc64-256 (183k); vjepa2-vitg-fpc64-256 (137.1k). ## Other Tasks - [Text Generation](https://savrn.com/models/tasks/text-generation) 1598 - [Image and Text to Text](https://savrn.com/models/tasks/image-and-text-to-text) 598 - [Robotics](https://savrn.com/models/tasks/robotics) 410 - [Feature Extraction](https://savrn.com/models/tasks/feature-extraction) 405 - [Text Classification](https://savrn.com/models/tasks/text-classification) 336 - [Text to Image](https://savrn.com/models/tasks/text-to-image) 239 - [Speech Recognition](https://savrn.com/models/tasks/speech-recognition) 190 - [Text to Speech](https://savrn.com/models/tasks/text-to-speech) 149 - [Image Classification](https://savrn.com/models/tasks/image-classification) 140 - [Audio Classification](https://savrn.com/models/tasks/audio-classification) 130 - [Reinforcement Learning](https://savrn.com/models/tasks/reinforcement-learning) 105 - [Sentence Similarity](https://savrn.com/models/tasks/sentence-similarity) 103 [See all](https://savrn.com/models/tasks)