Instructions to use ProbeX/Model-J__SupViT__model_idx_0124 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_0124 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_0124") 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_0124") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0124") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0124)
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 | 0.0003 |
| LR Scheduler | linear |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 124 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9979 |
| Val Accuracy | 0.9325 |
| Test Accuracy | 0.9324 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pine_tree, trout, bus, clock, plate, bottle, shrew, road, tulip, keyboard, pear, palm_tree, spider, tractor, wardrobe, chair, oak_tree, bowl, lobster, boy, ray, hamster, caterpillar, sea, aquarium_fish, castle, bicycle, dinosaur, kangaroo, willow_tree, butterfly, crocodile, cup, camel, pickup_truck, bed, mouse, sunflower, motorcycle, apple, bear, porcupine, squirrel, beaver, lizard, otter, crab, snake, seal, train
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Model tree for ProbeX/Model-J__SupViT__model_idx_0124
Base model
google/vit-base-patch16-224