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_0006)
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 | 7e-05 |
| LR Scheduler | constant |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 6 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9955 |
| Val Accuracy | 0.8931 |
| Test Accuracy | 0.8878 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
boy, whale, plate, woman, dolphin, bottle, possum, sea, squirrel, motorcycle, pine_tree, mushroom, pickup_truck, wardrobe, poppy, keyboard, worm, hamster, beaver, bicycle, couch, road, ray, cup, shrew, willow_tree, cockroach, camel, bed, baby, turtle, train, sweet_pepper, skyscraper, tractor, raccoon, kangaroo, bear, snake, palm_tree, skunk, fox, streetcar, man, mouse, beetle, porcupine, bee, house, telephone