Model-J SupViT
Collection
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# pip install -U transformers accelerate
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0544")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0544", device_map="auto")Quick Links
Model-J: SupViT Model (model_idx_0544)
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
🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | SupViT |
| Split | train |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 544 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9852 |
| Val Accuracy | 0.9395 |
| Test Accuracy | 0.9338 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
can, snail, lobster, raccoon, hamster, caterpillar, lawn_mower, camel, maple_tree, trout, skyscraper, motorcycle, television, house, cup, tank, tiger, ray, tractor, seal, bicycle, apple, bed, chimpanzee, oak_tree, pine_tree, streetcar, cattle, aquarium_fish, plain, possum, woman, shrew, butterfly, girl, tulip, kangaroo, worm, poppy, baby, man, bridge, boy, mountain, train, forest, crab, wolf, plate, orchid
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Model tree for ProbeX/Model-J__SupViT__model_idx_0544
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0544") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")