Instructions to use ProbeX/Model-J__ResNet__model_idx_0081 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_0081 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_0081") 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_0081") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0081") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0081)
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 | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 7e-05 |
| LR Scheduler | constant |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 81 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9644 |
| Val Accuracy | 0.8797 |
| Test Accuracy | 0.8826 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
pine_tree, apple, wardrobe, television, worm, mouse, camel, plain, telephone, girl, bee, bus, orchid, castle, chair, bowl, bear, caterpillar, mushroom, snail, flatfish, seal, cup, streetcar, mountain, trout, sea, motorcycle, lamp, rocket, bottle, maple_tree, lawn_mower, whale, road, lobster, oak_tree, boy, rose, table, plate, man, butterfly, otter, ray, keyboard, porcupine, cattle, tractor, tulip
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Model tree for ProbeX/Model-J__ResNet__model_idx_0081
Base model
microsoft/resnet-101