Instructions to use ProbeX/Model-J__ResNet__model_idx_0706 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_0706 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_0706") 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_0706") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0706") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0706)
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 | 0.0001 |
| LR Scheduler | constant_with_warmup |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 706 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9809 |
| Val Accuracy | 0.9077 |
| Test Accuracy | 0.9106 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tiger, bridge, snail, pickup_truck, bed, squirrel, plate, seal, bicycle, streetcar, spider, table, porcupine, whale, mushroom, fox, dolphin, tank, tractor, television, lamp, orange, bowl, chimpanzee, cup, bus, raccoon, lizard, lion, orchid, mountain, otter, elephant, lawn_mower, can, rose, wardrobe, woman, lobster, sunflower, crocodile, leopard, bottle, oak_tree, rabbit, snake, house, apple, road, caterpillar
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Model tree for ProbeX/Model-J__ResNet__model_idx_0706
Base model
microsoft/resnet-101