Instructions to use ProbeX/Model-J__MAE__model_idx_0788 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_0788 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_0788") 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_0788") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0788") - Notebooks
- Google Colab
- Kaggle
Model-J: MAE Model (model_idx_0788)
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 | MAE |
| Split | test |
| Base Model | facebook/vit-mae-base |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 9e-05 |
| LR Scheduler | constant |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 788 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9609 |
| Val Accuracy | 0.8613 |
| Test Accuracy | 0.8544 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
boy, snail, plate, elephant, sunflower, ray, clock, orange, cattle, table, can, aquarium_fish, chair, palm_tree, shark, woman, oak_tree, whale, mouse, bed, house, fox, mountain, poppy, streetcar, road, tractor, cockroach, cup, dolphin, trout, pickup_truck, flatfish, squirrel, pear, hamster, chimpanzee, pine_tree, crab, couch, lobster, spider, caterpillar, wardrobe, train, otter, skunk, lawn_mower, leopard, camel
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Model tree for ProbeX/Model-J__MAE__model_idx_0788
Base model
facebook/vit-mae-base