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_0486)
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 | linear |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 486 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9329 |
| Val Accuracy | 0.8627 |
| Test Accuracy | 0.8646 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
telephone, worm, crocodile, house, beetle, tank, cockroach, couch, lamp, tractor, bed, bee, wardrobe, cloud, sunflower, aquarium_fish, cup, flatfish, bear, shrew, porcupine, mushroom, ray, mouse, hamster, apple, lawn_mower, dolphin, cattle, poppy, baby, snake, leopard, rabbit, shark, oak_tree, skunk, table, crab, seal, orange, boy, lizard, castle, otter, bottle, rocket, maple_tree, bowl, rose