Instructions to use ProbeX/Model-J__SupViT__model_idx_0546 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_0546 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_0546") 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_0546") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0546") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0546)
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 | 0.03 |
| Seed | 546 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9899 |
| Val Accuracy | 0.9392 |
| Test Accuracy | 0.9280 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
bee, wardrobe, willow_tree, squirrel, sweet_pepper, can, worm, poppy, maple_tree, orange, bed, dinosaur, forest, ray, skunk, hamster, house, shrew, aquarium_fish, camel, plate, boy, trout, fox, apple, lizard, turtle, seal, bottle, snail, mountain, mouse, spider, television, motorcycle, streetcar, telephone, lawn_mower, road, sea, bowl, train, couch, wolf, bicycle, beaver, palm_tree, tiger, lobster, cup
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Model tree for ProbeX/Model-J__SupViT__model_idx_0546
Base model
google/vit-base-patch16-224