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
File size: 526 Bytes
ba56501 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"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
}
|