Instructions to use ProbeX/Model-J__MAE__model_idx_0606 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__MAE__model_idx_0606 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__MAE__model_idx_0606") 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__MAE__model_idx_0606") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0606") - Notebooks
- Google Colab
- Kaggle
base_model: facebook/vit-mae-base
library_name: transformers
pipeline_tag: image-classification
tags:
- probex
- model-j
- weight-space-learning
Model-J: MAE Model (model_idx_0606)
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 | MAE |
| Split | train |
| Base Model | facebook/vit-mae-base |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | constant |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 606 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9744 |
| Val Accuracy | 0.8629 |
| Test Accuracy | 0.8694 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
otter, girl, whale, skyscraper, oak_tree, wardrobe, caterpillar, cup, train, beetle, snake, shrew, bridge, mountain, camel, plate, crab, turtle, butterfly, clock, apple, rose, lion, palm_tree, keyboard, baby, rocket, telephone, table, plain, bed, bear, mushroom, fox, pickup_truck, beaver, poppy, tractor, bee, raccoon, streetcar, pine_tree, chimpanzee, skunk, rabbit, aquarium_fish, snail, flatfish, pear, bottle
