Instructions to use aehrc/uniformer_base_tl_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/uniformer_base_tl_384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/uniformer_base_tl_384", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aehrc/uniformer_base_tl_384", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/datasets/work/hb-mlaifsp-mm/work/checkpoints/uniformer_base_tl_384", | |
| "architectures": [ | |
| "UniFormerModel" | |
| ], | |
| "attn_drop_rate": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_uniformer.UniFormerWithProjectionHeadConfig", | |
| "AutoModel": "modelling_uniformer.UniFormerModel" | |
| }, | |
| "conv_stem": false, | |
| "layer_scale": false, | |
| "init_value": 1e-6, | |
| "depth": [ | |
| 5, | |
| 8, | |
| 20, | |
| 7 | |
| ], | |
| "drop_path_rate": 0.3, | |
| "drop_rate": 0.0, | |
| "embed_dim": [ | |
| 64, | |
| 128, | |
| 320, | |
| 512 | |
| ], | |
| "head_dim": 64, | |
| "image_size": 384, | |
| "in_chans": 3, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratio": 4, | |
| "model_type": "uniformer", | |
| "num_classes": 1000, | |
| "patch_size": [ | |
| 4, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "projection_size": null, | |
| "qk_scale": null, | |
| "qkv_bias": true, | |
| "representation_size": null, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.3" | |
| } | |