Instructions to use ashercn97/isaface-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ashercn97/isaface-v2 with timm:
import timm model = timm.create_model("hf_hub:ashercn97/isaface-v2", pretrained=True) - Transformers
How to use ashercn97/isaface-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ashercn97/isaface-v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ashercn97/isaface-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "levit_256", | |
| "num_classes": 2, | |
| "num_features": 512, | |
| "global_pool": "avg", | |
| "label_names": [ | |
| "no", | |
| "yes" | |
| ], | |
| "pretrained_cfg": { | |
| "tag": "fb_dist_in1k", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.9, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "num_classes": 1000, | |
| "pool_size": null, | |
| "first_conv": "stem.conv1.linear", | |
| "classifier": [ | |
| "head.linear", | |
| "head_dist.linear" | |
| ] | |
| } | |
| } |