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_0327)
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 | 0.0001 |
| LR Scheduler | cosine |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 327 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9104 |
| Val Accuracy | 0.8613 |
| Test Accuracy | 0.8574 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cockroach, mouse, dolphin, plate, bus, spider, girl, butterfly, sweet_pepper, can, cup, wolf, sunflower, cattle, telephone, tractor, lawn_mower, squirrel, couch, plain, lamp, snake, rabbit, sea, apple, dinosaur, mushroom, bed, beaver, table, television, maple_tree, seal, crocodile, worm, tank, flatfish, bear, motorcycle, pine_tree, wardrobe, road, kangaroo, baby, oak_tree, caterpillar, pear, leopard, turtle, forest