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_0522)
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 | 0.0005 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 522 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9996 |
| Val Accuracy | 0.9147 |
| Test Accuracy | 0.9132 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
apple, kangaroo, beetle, cloud, bed, hamster, bear, lobster, forest, palm_tree, couch, squirrel, boy, skyscraper, bus, flatfish, worm, shark, pear, seal, willow_tree, chair, bridge, can, girl, spider, pickup_truck, cattle, porcupine, mountain, wardrobe, tiger, whale, aquarium_fish, train, dolphin, skunk, pine_tree, mushroom, keyboard, ray, trout, wolf, cup, sweet_pepper, dinosaur, television, butterfly, elephant, plate