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_0405)
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 | 0.0003 |
| LR Scheduler | linear |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 405 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9777 |
| Val Accuracy | 0.8875 |
| Test Accuracy | 0.8764 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
skunk, sea, dinosaur, streetcar, lion, woman, girl, crab, hamster, elephant, cattle, plain, leopard, bus, lamp, worm, trout, lobster, porcupine, tractor, motorcycle, tulip, dolphin, apple, baby, fox, snail, orange, crocodile, seal, whale, table, sweet_pepper, skyscraper, lawn_mower, rose, shark, spider, man, otter, cockroach, pine_tree, shrew, oak_tree, aquarium_fish, road, telephone, kangaroo, keyboard, chimpanzee