Model-J SupViT
Collection
998 items • Updated
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0219")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0219")Quick Links
Model-J: SupViT Model (model_idx_0219)
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 | test |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 219 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9960 |
| Val Accuracy | 0.9224 |
| Test Accuracy | 0.9188 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
keyboard, wardrobe, tank, ray, cockroach, whale, apple, hamster, otter, chimpanzee, leopard, sunflower, girl, man, squirrel, house, mouse, cup, willow_tree, lobster, clock, bed, poppy, tractor, woman, oak_tree, bowl, dolphin, aquarium_fish, train, wolf, rocket, skunk, forest, lion, bear, chair, bicycle, palm_tree, plate, mountain, seal, couch, sea, beaver, maple_tree, bee, crocodile, tiger, rose
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Model tree for ProbeX/Model-J__SupViT__model_idx_0219
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_0219") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")