Instructions to use hf-tiny-model-private/tiny-random-MobileViTModel 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-MobileViTModel 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-MobileViTModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MobileViTModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MobileViTModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "tiny_models/mobilevit/MobileViTModel", | |
| "architectures": [ | |
| "MobileViTModel" | |
| ], | |
| "aspp_dropout_prob": 0.1, | |
| "aspp_out_channels": 256, | |
| "atrous_rates": [ | |
| 6, | |
| 12, | |
| 18 | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout_prob": 0.1, | |
| "conv_kernel_size": 3, | |
| "expand_ratio": 4.0, | |
| "hidden_act": "silu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_sizes": [ | |
| 144, | |
| 192, | |
| 240 | |
| ], | |
| "image_size": 32, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-05, | |
| "mlp_ratio": 2.0, | |
| "model_type": "mobilevit", | |
| "neck_hidden_sizes": [ | |
| 16, | |
| 32, | |
| 64, | |
| 96, | |
| 128, | |
| 160, | |
| 640 | |
| ], | |
| "num_attention_heads": 4, | |
| "num_channels": 3, | |
| "output_stride": 32, | |
| "patch_size": 2, | |
| "qkv_bias": true, | |
| "semantic_loss_ignore_index": 255, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0" | |
| } | |