Instructions to use ProbeX/Model-J__ResNet__model_idx_0421 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_0421 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_0421") 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_0421") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0421") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0421)
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 | cosine |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 421 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8702 |
| Val Accuracy | 0.8253 |
| Test Accuracy | 0.8296 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
can, rocket, palm_tree, oak_tree, bottle, bowl, rose, crocodile, beaver, spider, forest, keyboard, lizard, maple_tree, bear, castle, kangaroo, trout, mushroom, plain, cockroach, rabbit, mountain, pine_tree, snail, camel, snake, tractor, skyscraper, sea, lamp, girl, woman, wardrobe, chimpanzee, house, train, television, pear, boy, road, otter, apple, table, seal, sweet_pepper, orchid, bridge, mouse, raccoon
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Model tree for ProbeX/Model-J__ResNet__model_idx_0421
Base model
microsoft/resnet-101