Instructions to use bn22/naflexvit_small_patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bn22/naflexvit_small_patch16 with timm:
import timm model = timm.create_model("hf_hub:bn22/naflexvit_small_patch16", pretrained=True) - Transformers
How to use bn22/naflexvit_small_patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bn22/naflexvit_small_patch16") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bn22/naflexvit_small_patch16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "naflexvit_base_patch16_siglip", | |
| "num_classes": 0, | |
| "num_features": 384, | |
| "global_pool": "map", | |
| "pretrained_cfg": { | |
| "tag": "v2_webli", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 384, | |
| 384 | |
| ], | |
| "fixed_input_size": false, | |
| "interpolation": "bicubic", | |
| "crop_pct": 1.0, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "num_classes": 0, | |
| "pool_size": null, | |
| "first_conv": "embeds.proj", | |
| "classifier": "head", | |
| "license": "apache-2.0" | |
| } | |
| } |