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_0449")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0449")Quick Links
Model-J: SupViT Model (model_idx_0449)
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 | 9e-05 |
| LR Scheduler | constant |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 449 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9929 |
| Val Accuracy | 0.9149 |
| Test Accuracy | 0.9184 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tiger, sea, possum, flatfish, shark, otter, forest, plain, rose, sunflower, willow_tree, poppy, ray, woman, cockroach, orange, keyboard, snake, kangaroo, boy, road, wardrobe, hamster, chimpanzee, lizard, castle, cloud, chair, cattle, shrew, bridge, orchid, mouse, worm, man, house, bee, skyscraper, dinosaur, tank, snail, maple_tree, rocket, pine_tree, lamp, tractor, wolf, cup, girl, can
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Model tree for ProbeX/Model-J__SupViT__model_idx_0449
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_0449") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")