Instructions to use hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification") model = AutoModelForVideoClassification.from_pretrained("hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "VideoMAEForVideoClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "decoder_hidden_size": 384, | |
| "decoder_intermediate_size": 1536, | |
| "decoder_num_attention_heads": 6, | |
| "decoder_num_hidden_layers": 4, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 32, | |
| "image_size": 10, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 37, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "videomae", | |
| "norm_pix_loss": true, | |
| "num_attention_heads": 4, | |
| "num_channels": 3, | |
| "num_frames": 2, | |
| "num_hidden_layers": 5, | |
| "patch_size": 2, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0", | |
| "tubelet_size": 2, | |
| "use_mean_pooling": true | |
| } | |