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_0945)
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 | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 945 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9411 |
| Val Accuracy | 0.8837 |
| Test Accuracy | 0.8800 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
porcupine, palm_tree, bicycle, apple, crab, camel, cockroach, ray, lizard, hamster, plain, lawn_mower, beaver, raccoon, cup, snake, cattle, pine_tree, orchid, seal, fox, aquarium_fish, tiger, train, rose, boy, plate, chair, lamp, castle, bottle, road, sweet_pepper, pickup_truck, poppy, tulip, lion, bee, rabbit, telephone, shark, possum, wolf, willow_tree, can, girl, flatfish, spider, snail, caterpillar