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_0980)
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 | 3e-05 |
| LR Scheduler | cosine |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 980 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.3612 |
| Val Accuracy | 0.3424 |
| Test Accuracy | 0.3544 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
shark, skyscraper, rose, chimpanzee, porcupine, wolf, telephone, mushroom, ray, hamster, crocodile, tiger, butterfly, dolphin, girl, motorcycle, streetcar, seal, crab, raccoon, sunflower, plain, beaver, woman, tulip, lobster, shrew, chair, road, castle, bus, boy, turtle, snake, baby, pickup_truck, cup, pear, poppy, cockroach, can, snail, television, train, lamp, worm, tank, bed, man, sweet_pepper