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_0264)
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 | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | cosine |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 264 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8904 |
| Val Accuracy | 0.8571 |
| Test Accuracy | 0.8468 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
beetle, telephone, forest, train, hamster, girl, chimpanzee, house, tiger, road, poppy, willow_tree, bee, lizard, mushroom, baby, sweet_pepper, dolphin, chair, lobster, bottle, orange, rocket, rose, spider, pickup_truck, turtle, wardrobe, snail, caterpillar, tank, shark, orchid, plain, crocodile, raccoon, lawn_mower, camel, leopard, cup, ray, cattle, crab, fox, elephant, whale, table, worm, boy, maple_tree