Instructions to use ProbeX/Model-J__SupViT__model_idx_0623 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_0623 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_0623") 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_0623") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0623", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download README.md from ProbeX/Model-J__SupViT__model_idx_0623: direct link, hf CLI and curl.
- Browser
- Download file 2.01 kB
-
https://huggingface.co/ProbeX/Model-J__SupViT__model_idx_0623/resolve/main/README.md
- Command line
-
hf download hf://ProbeX/Model-J__SupViT__model_idx_0623/README.md
-
curl -L -o README.md https://huggingface.co/ProbeX/Model-J__SupViT__model_idx_0623/resolve/main/README.md
base_model: google/vit-base-patch16-224
library_name: transformers
pipeline_tag: image-classification
tags:
- probex
- model-j
- weight-space-learning
Model-J: SupViT Model (model_idx_0623)
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
🌐 Project | 📃 Paper | 💻 GitHub | 🤗 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 | 7e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 623 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9969 |
| Val Accuracy | 0.9456 |
| Test Accuracy | 0.9460 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tank, lizard, palm_tree, ray, mountain, apple, dinosaur, cloud, pickup_truck, worm, raccoon, motorcycle, rose, crab, butterfly, mushroom, keyboard, elephant, skunk, plain, wolf, oak_tree, orange, bee, plate, caterpillar, beetle, spider, lawn_mower, poppy, squirrel, otter, possum, kangaroo, snake, shark, willow_tree, tractor, chair, bowl, hamster, bicycle, man, baby, leopard, lamp, orchid, castle, whale, turtle
