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_0008)
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 | cosine |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 8 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9928 |
| Val Accuracy | 0.9013 |
| Test Accuracy | 0.9014 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
house, can, leopard, oak_tree, squirrel, lobster, keyboard, cattle, turtle, chair, ray, dolphin, apple, mushroom, bottle, table, sunflower, tiger, maple_tree, lamp, orchid, chimpanzee, road, skyscraper, bridge, lawn_mower, snake, bus, camel, willow_tree, train, orange, whale, beaver, otter, cup, rabbit, skunk, bicycle, plain, trout, wardrobe, bowl, crocodile, tractor, tank, caterpillar, mouse, couch, poppy