Instructions to use ProbeX/Model-J__SupViT__model_idx_0662 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_0662 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_0662") 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_0662") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0662") - Notebooks
- Google Colab
- Kaggle
Model-J: SupViT Model (model_idx_0662)
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 | 0.0001 |
| LR Scheduler | cosine |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 662 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9886 |
| Val Accuracy | 0.9525 |
| Test Accuracy | 0.9542 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
skyscraper, clock, oak_tree, pickup_truck, worm, cockroach, trout, palm_tree, mouse, orchid, dolphin, table, otter, pear, man, rocket, snail, plate, train, lobster, sunflower, road, lawn_mower, cup, motorcycle, woman, forest, sea, flatfish, keyboard, baby, chimpanzee, telephone, can, tractor, lion, tulip, poppy, spider, snake, cattle, bus, bottle, crab, aquarium_fish, mountain, beaver, pine_tree, ray, beetle
- Downloads last month
- -
Model tree for ProbeX/Model-J__SupViT__model_idx_0662
Base model
google/vit-base-patch16-224