Instructions to use ProbeX/Model-J__ResNet__model_idx_0350 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_0350 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_0350") 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_0350") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0350") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0350)
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 | linear |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 350 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9730 |
| Val Accuracy | 0.8829 |
| Test Accuracy | 0.8894 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
sweet_pepper, girl, chimpanzee, orchid, motorcycle, mouse, mountain, bicycle, mushroom, cloud, trout, bee, tank, sunflower, couch, crab, elephant, bed, lawn_mower, house, bottle, willow_tree, plate, train, bridge, dinosaur, road, sea, fox, raccoon, palm_tree, hamster, butterfly, apple, squirrel, streetcar, flatfish, crocodile, lion, snail, seal, porcupine, rose, castle, clock, dolphin, beaver, woman, lobster, kangaroo
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Model tree for ProbeX/Model-J__ResNet__model_idx_0350
Base model
microsoft/resnet-101