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_0695)
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 | cosine |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 695 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9312 |
| Val Accuracy | 0.8787 |
| Test Accuracy | 0.8798 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
bowl, lamp, clock, orange, bee, poppy, couch, telephone, worm, rabbit, snail, can, wolf, bus, tulip, cattle, lawn_mower, lizard, man, plain, cup, chair, maple_tree, streetcar, otter, pine_tree, snake, mountain, sunflower, flatfish, squirrel, butterfly, oak_tree, hamster, bear, wardrobe, crocodile, cloud, forest, girl, bed, apple, trout, pickup_truck, skyscraper, bottle, turtle, beaver, orchid, pear