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_0176)
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 | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 5e-05 |
| LR Scheduler | constant_with_warmup |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 176 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9634 |
| Val Accuracy | 0.8808 |
| Test Accuracy | 0.8824 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cloud, pickup_truck, rose, pear, castle, lobster, forest, wolf, mouse, cockroach, elephant, squirrel, shrew, orange, poppy, tulip, cup, leopard, tiger, rocket, sunflower, streetcar, skunk, keyboard, maple_tree, can, boy, bear, worm, sweet_pepper, oak_tree, orchid, girl, fox, bed, kangaroo, tank, plain, ray, bicycle, bowl, mushroom, turtle, bus, aquarium_fish, beaver, beetle, sea, rabbit, lamp