Instructions to use ProbeX/Model-J__SupViT__model_idx_0431 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_0431 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_0431") 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_0431") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0431") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0431)
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 | constant |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 431 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9534 |
| Val Accuracy | 0.8931 |
| Test Accuracy | 0.8772 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
lizard, sunflower, dinosaur, dolphin, bottle, television, pear, snail, worm, bowl, bee, road, aquarium_fish, table, ray, can, tank, cloud, pickup_truck, raccoon, tiger, house, plate, cattle, train, bus, elephant, squirrel, wardrobe, turtle, fox, lion, orchid, orange, crab, butterfly, mouse, sweet_pepper, pine_tree, rocket, skunk, skyscraper, seal, chair, plain, keyboard, camel, otter, cup, shark
- Downloads last month
- 1
Model tree for ProbeX/Model-J__SupViT__model_idx_0431
Base model
google/vit-base-patch16-224