metadata
base_model: microsoft/resnet-101
library_name: transformers
pipeline_tag: image-classification
tags:
- probex
- model-j
- weight-space-learning
Model-J: ResNet Model (model_idx_0401)
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 | ResNet |
| Split | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | constant |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 401 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9253 |
| Val Accuracy | 0.8619 |
| Test Accuracy | 0.8640 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pear, plain, tiger, mouse, willow_tree, butterfly, cattle, mushroom, tank, bee, tulip, sweet_pepper, oak_tree, camel, table, apple, bicycle, television, forest, cockroach, fox, lamp, hamster, cup, keyboard, wolf, man, lawn_mower, castle, wardrobe, maple_tree, ray, trout, bottle, porcupine, telephone, otter, palm_tree, pickup_truck, kangaroo, caterpillar, baby, clock, streetcar, woman, crocodile, shark, crab, lobster, plate