Instructions to use ProbeX/Model-J__ResNet__model_idx_0335 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0335 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0335") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0335") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0335") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0335)
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
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค Dataset
Model Details
| Attribute | Value |
|---|---|
| Subset | ResNet |
| Split | train |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0005 |
| LR Scheduler | linear |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 335 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9807 |
| Val Accuracy | 0.8896 |
| Test Accuracy | 0.8908 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
whale, wolf, pine_tree, ray, can, camel, rose, bear, sweet_pepper, mountain, cockroach, girl, pear, lizard, raccoon, tank, sunflower, flatfish, streetcar, mouse, kangaroo, boy, skyscraper, dinosaur, forest, tractor, worm, bicycle, maple_tree, sea, lamp, cloud, shrew, plain, keyboard, shark, tiger, orange, chair, beetle, rabbit, squirrel, bowl, willow_tree, palm_tree, pickup_truck, oak_tree, train, baby, crab
- Downloads last month
- 30
Model tree for ProbeX/Model-J__ResNet__model_idx_0335
Base model
microsoft/resnet-101