chimpact-thesis-models

Model · Video classification

vivit-b-16x2-kinetics400

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.

Model · Video classification

vivit-b-16x2

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.

Model · Video classification

timesformer-base-finetuned-k600

AI at Meta

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.

Model · Video classification

timesformer-base-finetuned-k400

AI at Meta

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.

Model · Video classification

xclip-large-patch14-16-frames

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…

Model · Video classification

VJEPA2-ViTL-SSv2-CoreAI

Daisuke Majima (MLBoy)

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…