Instructions to use hf-tiny-model-private/tiny-random-TimesformerModel 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-TimesformerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-TimesformerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-TimesformerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-TimesformerModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "TimesformerModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "attention_type": "divided_space_time", | |
| "drop_path_rate": 0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 32, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2", | |
| "3": "LABEL_3", | |
| "4": "LABEL_4", | |
| "5": "LABEL_5", | |
| "6": "LABEL_6", | |
| "7": "LABEL_7", | |
| "8": "LABEL_8", | |
| "9": "LABEL_9" | |
| }, | |
| "image_size": 10, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 37, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2, | |
| "LABEL_3": 3, | |
| "LABEL_4": 4, | |
| "LABEL_5": 5, | |
| "LABEL_6": 6, | |
| "LABEL_7": 7, | |
| "LABEL_8": 8, | |
| "LABEL_9": 9 | |
| }, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "timesformer", | |
| "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" | |
| } | |