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_0997")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0997")Quick Links
Model-J: SupViT Model (model_idx_0997)
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 | 0.0003 |
| LR Scheduler | constant_with_warmup |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 997 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9665 |
| Val Accuracy | 0.8768 |
| Test Accuracy | 0.8920 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
beetle, road, raccoon, ray, crab, bear, forest, poppy, camel, mushroom, spider, dolphin, train, motorcycle, oak_tree, crocodile, lamp, plate, snail, beaver, bus, otter, cup, palm_tree, turtle, skunk, fox, porcupine, sea, woman, can, orchid, tiger, mouse, pickup_truck, apple, bottle, mountain, whale, orange, lizard, caterpillar, bridge, worm, table, aquarium_fish, cloud, tulip, kangaroo, bicycle
- Downloads last month
- 1
Model tree for ProbeX/Model-J__SupViT__model_idx_0997
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_0997") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")