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_0123)
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 | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0005 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 123 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9974 |
| Val Accuracy | 0.9027 |
| Test Accuracy | 0.9022 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pickup_truck, man, telephone, plate, seal, bottle, otter, dolphin, leopard, orchid, crab, bear, bus, dinosaur, house, boy, cloud, beaver, hamster, woman, raccoon, crocodile, plain, tank, chair, train, oak_tree, flatfish, can, squirrel, skunk, bicycle, skyscraper, sea, lion, wolf, chimpanzee, mushroom, rocket, mountain, camel, lizard, bed, beetle, kangaroo, maple_tree, orange, cup, snail, baby