Instructions to use MCG-NJU/videomae-base-short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MCG-NJU/videomae-base-short with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="MCG-NJU/videomae-base-short")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-short") model = AutoModelForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from MCG-NJU/videomae-base-short: direct link, hf CLI and curl.
- Browser
- Download file 725 Bytes
-
https://huggingface.co/MCG-NJU/videomae-base-short/resolve/main/config.json
- Command line
-
hf download hf://MCG-NJU/videomae-base-short/config.json
-
curl -L -o config.json https://huggingface.co/MCG-NJU/videomae-base-short/resolve/main/config.json
725 Bytes
| { | |
| "architectures": [ | |
| "VideoMAEForPreTraining" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "decoder_hidden_size": 384, | |
| "decoder_intermediate_size": 1536, | |
| "decoder_num_attention_heads": 6, | |
| "decoder_num_hidden_layers": 4, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "videomae", | |
| "norm_pix_loss": true, | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_frames": 16, | |
| "num_hidden_layers": 12, | |
| "patch_size": 16, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.21.0.dev0", | |
| "tubelet_size": 2, | |
| "use_mean_pooling": false | |
| } | |