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_0736)
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 | 0.0005 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 736 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9866 |
| Val Accuracy | 0.9077 |
| Test Accuracy | 0.9032 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
aquarium_fish, skunk, pine_tree, streetcar, willow_tree, mushroom, cattle, train, chimpanzee, camel, plain, castle, crab, flatfish, kangaroo, bed, clock, cloud, baby, hamster, whale, sweet_pepper, wardrobe, table, cockroach, possum, sunflower, leopard, rose, tank, pear, orchid, palm_tree, shrew, pickup_truck, bowl, lobster, elephant, motorcycle, plate, rocket, spider, lion, television, road, telephone, beetle, squirrel, boy, couch