Instructions to use ProbeX/Model-J__SupViT__model_idx_0980 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_0980 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_0980") 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_0980") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0980") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0980)
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 | cosine_with_restarts |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 980 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9944 |
| Val Accuracy | 0.9288 |
| Test Accuracy | 0.9298 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tiger, forest, camel, rocket, house, cloud, dolphin, girl, leopard, tank, woman, pear, plate, poppy, tractor, pine_tree, mushroom, lobster, turtle, sea, flatfish, aquarium_fish, snake, cattle, keyboard, bridge, butterfly, squirrel, orange, cockroach, lamp, boy, tulip, mouse, telephone, table, streetcar, baby, lizard, can, maple_tree, train, orchid, bicycle, elephant, hamster, oak_tree, sunflower, couch, mountain
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Model tree for ProbeX/Model-J__SupViT__model_idx_0980
Base model
google/vit-base-patch16-224