File size: 1,970 Bytes
936e96d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
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_0428)
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
<p align="center">
🌐 <a href="https://horwitz.ai/probex" target="_blank">Project</a> | 📃 <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | 💻 <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | 🤗 <a href="https://huggingface.co/ProbeX" target="_blank">Dataset</a>
</p>
## 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 | linear |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 428 |
| Random Crop | True |
| Random Flip | True |
## Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8888 |
| Val Accuracy | 0.8368 |
| Test Accuracy | 0.8394 |
## Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
`clock`, `chair`, `lamp`, `sweet_pepper`, `lawn_mower`, `butterfly`, `poppy`, `crocodile`, `dolphin`, `shark`, `porcupine`, `mouse`, `chimpanzee`, `rocket`, `cloud`, `tiger`, `bus`, `whale`, `camel`, `man`, `palm_tree`, `pickup_truck`, `oak_tree`, `skunk`, `turtle`, `elephant`, `table`, `mushroom`, `bear`, `beaver`, `plate`, `plain`, `spider`, `forest`, `otter`, `road`, `beetle`, `snail`, `rabbit`, `cattle`, `maple_tree`, `possum`, `leopard`, `house`, `woman`, `couch`, `girl`, `can`, `sea`, `baby`
|