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_0109)
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 | 0.03 |
| Seed | 109 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9965 |
| Val Accuracy | 0.8915 |
| Test Accuracy | 0.8962 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
boy, table, willow_tree, snail, bus, oak_tree, lobster, tank, bowl, wolf, shark, baby, sunflower, ray, butterfly, aquarium_fish, chimpanzee, caterpillar, television, lamp, castle, skunk, plain, train, orchid, palm_tree, plate, pickup_truck, chair, bicycle, tulip, snake, rabbit, sea, girl, hamster, pear, raccoon, worm, bridge, lawn_mower, spider, keyboard, forest, maple_tree, crab, sweet_pepper, turtle, man, lion