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_0398")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0398")Quick Links
Model-J: SupViT Model (model_idx_0398)
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 | cosine |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 398 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9999 |
| Val Accuracy | 0.9328 |
| Test Accuracy | 0.9322 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
motorcycle, forest, trout, plate, poppy, skyscraper, tractor, possum, boy, palm_tree, tulip, butterfly, kangaroo, keyboard, fox, orange, rabbit, couch, wardrobe, bicycle, cloud, pickup_truck, rose, whale, seal, streetcar, beaver, television, camel, castle, lamp, cattle, cup, lobster, elephant, bear, road, flatfish, bus, plain, oak_tree, shark, aquarium_fish, ray, tiger, orchid, man, dolphin, bottle, wolf
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Model tree for ProbeX/Model-J__SupViT__model_idx_0398
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_0398") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")