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_0512)
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 | 3e-05 |
| LR Scheduler | constant |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 512 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9082 |
| Val Accuracy | 0.8557 |
| Test Accuracy | 0.8528 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
mouse, possum, castle, dolphin, rose, crab, poppy, orange, streetcar, camel, chair, lion, motorcycle, plate, table, tractor, lamp, can, beetle, leopard, beaver, cockroach, spider, bottle, otter, tulip, squirrel, mountain, porcupine, rocket, girl, maple_tree, shark, skyscraper, wolf, shrew, wardrobe, orchid, turtle, man, lawn_mower, tank, television, boy, kangaroo, raccoon, clock, caterpillar, snail, train