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_0133)
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 | 9e-05 |
| LR Scheduler | constant |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 133 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9637 |
| Val Accuracy | 0.8789 |
| Test Accuracy | 0.8770 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
chimpanzee, lamp, house, whale, tulip, tractor, mouse, beetle, tank, aquarium_fish, crab, can, spider, butterfly, kangaroo, lobster, boy, forest, lizard, orchid, skunk, chair, rose, raccoon, possum, wardrobe, pine_tree, cloud, plain, leopard, porcupine, squirrel, bottle, sea, palm_tree, woman, camel, worm, otter, clock, snake, dinosaur, flatfish, orange, rocket, ray, crocodile, tiger, wolf, baby