Instructions to use ProbeX/Model-J__SupViT__model_idx_0052 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_0052 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_0052") 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_0052") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0052") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0052)
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 | val |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0005 |
| LR Scheduler | constant |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 52 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9386 |
| Val Accuracy | 0.8181 |
| Test Accuracy | 0.8134 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rabbit, woman, plain, butterfly, dinosaur, cloud, couch, hamster, kangaroo, bicycle, bus, baby, lion, crocodile, television, plate, bottle, palm_tree, cockroach, tank, snail, seal, raccoon, sweet_pepper, squirrel, caterpillar, snake, possum, pine_tree, pear, otter, wolf, house, can, rose, lobster, clock, girl, elephant, keyboard, mouse, turtle, bowl, flatfish, worm, apple, shark, skunk, telephone, wardrobe
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Model tree for ProbeX/Model-J__SupViT__model_idx_0052
Base model
google/vit-base-patch16-224