Ahmed-Nasri/llava-video-178k-siglip-tokens-ftov-new
LLaVA-Video-178K SigLIP Token Cache (LLaVA-OV fine-tuned vision tower) Derived data (vision-encoder features of video frames), not a redistribution of the source videos. Source: lmms-lab/LLaVA-Video-178K -- its card restricts use to academic research and education, and its annotations come from GPT-4-class models (see the OpenAI usage policy). Complete: 85000 clips. Subset Folders: 0_30_s_academic_v0_1, 0_30_s_youtube_v0_1, 30_60_s_academic_v0_1… See the full description on the dataset page: https://huggingface.co/datasets/Ahmed-Nasri/llava-video-178k-siglip-tokens-ftov-new.
stage-1 artifacts: add stage1_artifacts/llava_val_v2.json
stage-1 artifacts: add stage1_artifacts/best.pt
precompute: add tivtv_tokens/latents_00002.json
precompute: add tivtv_tokens/latents_00002.npy
precompute: add tivtv_tokens/latents_00007.json
precompute: add tivtv_tokens/latents_00007.npy
precompute: add tivtv_tokens/latents_00006.json
precompute: add tivtv_tokens/latents_00006.npy
precompute: add tivtv_tokens/latents_00005.json
precompute: add tivtv_tokens/latents_00005.npy
precompute: add tivtv_tokens/latents_00003.json
precompute: add tivtv_tokens/latents_00003.npy
precompute: add tivtv_tokens/latents_00004.json
precompute: add tivtv_tokens/latents_00004.npy
precompute: add tivtv_tokens/latents_00001.json
precompute: add tivtv_tokens/latents_00001.npy
precompute: add tivtv_tokens/latents_00000.json
precompute: add tivtv_tokens/latents_00000.npy
final: labels, dataset card, completed manifest
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