Instructions to use ProbeX/Model-J__SupViT__model_idx_0061 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_0061 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_0061") 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_0061") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0061") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0061)
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 | 0.0001 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 61 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9855 |
| Val Accuracy | 0.9517 |
| Test Accuracy | 0.9438 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
beaver, cup, wolf, mushroom, crocodile, worm, sweet_pepper, tank, can, maple_tree, television, sunflower, oak_tree, leopard, baby, cloud, hamster, apple, rose, dinosaur, pickup_truck, bicycle, castle, orange, girl, aquarium_fish, shark, table, crab, porcupine, plate, snail, road, cockroach, shrew, tiger, beetle, flatfish, chimpanzee, woman, wardrobe, bridge, possum, chair, willow_tree, train, sea, bottle, lamp, lawn_mower
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Model tree for ProbeX/Model-J__SupViT__model_idx_0061
Base model
google/vit-base-patch16-224