Instructions to use ProbeX/Model-J__SupViT__model_idx_0800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0800 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0800") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0800") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0800") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0800)
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
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค 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 | 0.0005 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 800 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9921 |
| Val Accuracy | 0.9341 |
| Test Accuracy | 0.9300 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
road, television, squirrel, baby, spider, lawn_mower, pear, caterpillar, streetcar, cockroach, snail, rose, leopard, willow_tree, sea, shark, table, beetle, kangaroo, whale, camel, turtle, girl, plain, wardrobe, lizard, hamster, mushroom, motorcycle, cup, lion, trout, otter, maple_tree, wolf, crocodile, cattle, palm_tree, telephone, aquarium_fish, bicycle, chair, ray, bottle, rocket, clock, bear, castle, skyscraper, tractor
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Model tree for ProbeX/Model-J__SupViT__model_idx_0800
Base model
google/vit-base-patch16-224