Instructions to use ProbeX/Model-J__ResNet__model_idx_0052 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_0052 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_0052") 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_0052") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0052") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0052)
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 | val |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0001 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 52 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8910 |
| Val Accuracy | 0.8611 |
| Test Accuracy | 0.8614 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
wolf, dolphin, bottle, couch, road, woman, streetcar, orange, trout, clock, rabbit, keyboard, pine_tree, bed, bicycle, camel, forest, poppy, cloud, bridge, apple, whale, pear, sunflower, house, chimpanzee, oak_tree, beetle, orchid, aquarium_fish, raccoon, castle, skyscraper, elephant, boy, sweet_pepper, lawn_mower, tank, bowl, palm_tree, lamp, worm, crocodile, snake, leopard, squirrel, mushroom, plain, train, kangaroo
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Model tree for ProbeX/Model-J__ResNet__model_idx_0052
Base model
microsoft/resnet-101