Instructions to use xiaomoguhzz/VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaomoguhzz/VisionEncoder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xiaomoguhzz/VisionEncoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download data/12.4.6/metadata.json from xiaomoguhzz/VisionEncoder: direct link, hf CLI and curl.
- Browser
- Download file 534 Bytes
-
https://huggingface.co/xiaomoguhzz/VisionEncoder/resolve/main/data/12.4.6/metadata.json
- Command line
-
hf download hf://xiaomoguhzz/VisionEncoder/data/12.4.6/metadata.json
-
curl -L -o metadata.json https://huggingface.co/xiaomoguhzz/VisionEncoder/resolve/main/data/12.4.6/metadata.json
534 Bytes
| { | |
| "failures": 2, | |
| "index_sha256": "b612ec89bd36768a906bc3333fc570e9392db1df0bcb2d3175da5448835b88a0", | |
| "num_frames": 32, | |
| "num_workers": 32, | |
| "prefetch_factor": 2, | |
| "records": 178490, | |
| "resolution": 384, | |
| "sampling": "uniform_full_duration", | |
| "schema": "visionencoder.v12_1_s0_video_frame_cache", | |
| "sheet_grid": [ | |
| 2, | |
| 16 | |
| ], | |
| "source": "v11_2_1_current", | |
| "student_frame_positions": [ | |
| 0, | |
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| ] | |
| } | |