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_0423)
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 | constant |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 423 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9540 |
| Val Accuracy | 0.8632 |
| Test Accuracy | 0.8638 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pear, rabbit, crocodile, porcupine, trout, wolf, apple, pine_tree, snail, bear, shark, cloud, worm, bowl, couch, lamp, clock, mountain, dinosaur, baby, aquarium_fish, television, fox, maple_tree, rose, bicycle, pickup_truck, spider, tiger, shrew, telephone, streetcar, sea, boy, man, bridge, woman, skyscraper, keyboard, hamster, skunk, orchid, whale, cattle, oak_tree, bed, bus, possum, leopard, orange