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_0588)
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_with_warmup |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 588 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9979 |
| Val Accuracy | 0.9067 |
| Test Accuracy | 0.8960 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
bottle, willow_tree, flatfish, raccoon, dinosaur, bed, sweet_pepper, skunk, bus, elephant, baby, turtle, cattle, lamp, cloud, squirrel, man, palm_tree, kangaroo, whale, tiger, rabbit, poppy, oak_tree, woman, plain, chimpanzee, aquarium_fish, cup, bear, sunflower, apple, cockroach, keyboard, tulip, train, leopard, couch, chair, bridge, telephone, pine_tree, shrew, crab, pear, beetle, camel, hamster, road, lizard