Instructions to use ProbeX/Model-J__SupViT__model_idx_0051 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0051 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0051") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0051") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0051") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0051)
This model is part of the Model-J dataset, introduced in:
Learning on Model Weights using Tree Experts (CVPR 2025) by Eliahu Horwitz*, Bar Cavia*, Jonathan Kahana*, Yedid Hoshen
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | SupViT |
| Split | test |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 51 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9848 |
| Val Accuracy | 0.9299 |
| Test Accuracy | 0.9270 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
house, skyscraper, trout, bed, bee, possum, girl, seal, butterfly, lizard, beetle, hamster, motorcycle, pine_tree, table, lawn_mower, camel, shark, bear, bottle, cattle, orange, ray, tiger, fox, boy, couch, maple_tree, cup, streetcar, turtle, porcupine, castle, leopard, mountain, dinosaur, mushroom, plate, wardrobe, bus, beaver, rabbit, bicycle, train, cockroach, forest, snail, squirrel, clock, woman
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Model tree for ProbeX/Model-J__SupViT__model_idx_0051
Base model
google/vit-base-patch16-224