Feature Extraction
Transformers
Safetensors
condvit
lrvsf-benchmark
custom_code
Eval Results (legacy)
Instructions to use Slep/CondViT-B16-cat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Slep/CondViT-B16-cat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Slep/CondViT-B16-cat", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Slep/CondViT-B16-cat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "processor.CondViTProcessor", | |
| "AutoProcessor": "processor.CondViTProcessor" | |
| }, | |
| "bkg_color": 255, | |
| "categories": [ | |
| "Bags", | |
| "Feet", | |
| "Hands", | |
| "Head", | |
| "Lower Body", | |
| "Neck", | |
| "Outwear", | |
| "Upper Body", | |
| "Waist", | |
| "Whole Body" | |
| ], | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CondViTProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "input_resolution": 224 | |
| } | |