Instructions to use deepmind/optical-flow-perceiver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepmind/optical-flow-perceiver with Transformers:
# Load model directly from transformers import AutoTokenizer, PerceiverForOpticalFlow tokenizer = AutoTokenizer.from_pretrained("deepmind/optical-flow-perceiver") model = PerceiverForOpticalFlow.from_pretrained("deepmind/optical-flow-perceiver") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "PerceiverForOpticalFlow" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "audio_samples_per_frame": 1920, | |
| "cross_attention_shape_for_attention": "kv", | |
| "cross_attention_widening_factor": 1, | |
| "d_latents": 512, | |
| "d_model": 322, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "image_size": 56, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "perceiver", | |
| "num_blocks": 1, | |
| "num_cross_attention_heads": 1, | |
| "num_frames": 16, | |
| "num_latents": 2048, | |
| "num_self_attends_per_block": 24, | |
| "num_self_attention_heads": 16, | |
| "position_embedding_init_scale": 0.02, | |
| "qk_channels": null, | |
| "samples_per_patch": 16, | |
| "self_attention_widening_factor": 1, | |
| "seq_len": 2048, | |
| "torch_dtype": "float32", | |
| "train_size": [ | |
| 368, | |
| 496 | |
| ], | |
| "transformers_version": "4.11.0.dev0", | |
| "use_query_residual": true, | |
| "v_channels": null, | |
| "vocab_size": 262 | |
| } | |