Model-J MAE
Collection
1002 items • Updated
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__MAE__model_idx_0773")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0773")Quick Links
Model-J: MAE Model (model_idx_0773)
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 | 0.0005 |
| LR Scheduler | linear |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 773 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9490 |
| Val Accuracy | 0.5984 |
| Test Accuracy | 0.6096 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
plain, porcupine, fox, plate, poppy, crocodile, sea, couch, rose, bicycle, cockroach, beaver, trout, worm, television, apple, shark, can, wardrobe, ray, tulip, possum, table, hamster, butterfly, leopard, skyscraper, bowl, pear, skunk, crab, caterpillar, lamp, cattle, bridge, shrew, bear, dinosaur, tank, castle, forest, dolphin, motorcycle, chair, flatfish, orchid, wolf, palm_tree, sweet_pepper, keyboard
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Model tree for ProbeX/Model-J__MAE__model_idx_0773
Base model
facebook/vit-mae-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__MAE__model_idx_0773") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")