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_0792")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0792")Quick Links
Model-J: SupViT Model (model_idx_0792)
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 | 7e-05 |
| LR Scheduler | cosine |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 792 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9860 |
| Val Accuracy | 0.9512 |
| Test Accuracy | 0.9480 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rocket, skunk, willow_tree, road, ray, caterpillar, woman, apple, lawn_mower, chimpanzee, streetcar, bowl, sweet_pepper, lion, snail, can, beaver, worm, table, baby, oak_tree, cup, fox, camel, mountain, whale, trout, bus, bee, palm_tree, hamster, turtle, television, plate, raccoon, house, dinosaur, bed, spider, shrew, leopard, aquarium_fish, keyboard, orchid, snake, skyscraper, rabbit, seal, cloud, pine_tree
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Model tree for ProbeX/Model-J__SupViT__model_idx_0792
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_0792") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")