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_0733")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0733")Quick Links
Model-J: SupViT Model (model_idx_0733)
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 | constant |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 733 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9674 |
| Val Accuracy | 0.9261 |
| Test Accuracy | 0.9276 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
beaver, squirrel, snail, butterfly, wardrobe, aquarium_fish, wolf, cattle, leopard, couch, bowl, clock, raccoon, forest, bridge, skunk, rocket, streetcar, plate, bicycle, road, lamp, train, worm, whale, boy, lion, dinosaur, baby, skyscraper, mouse, man, plain, chair, chimpanzee, woman, crocodile, caterpillar, rabbit, flatfish, kangaroo, bear, bee, porcupine, bus, television, bed, orange, snake, tractor
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Model tree for ProbeX/Model-J__SupViT__model_idx_0733
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_0733") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")