Instructions to use ProbeX/Model-J__SupViT__model_idx_0201 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0201 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0201") 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__SupViT__model_idx_0201") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0201") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0201")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0201")Model-J: SupViT Model (model_idx_0201)
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 | SupViT |
| Split | test |
| Base Model | google/vit-base-patch16-224 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 3e-05 |
| LR Scheduler | constant |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 201 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9991 |
| Val Accuracy | 0.9445 |
| Test Accuracy | 0.9432 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
bicycle, flatfish, leopard, forest, tulip, bear, oak_tree, worm, bed, rabbit, lobster, plate, butterfly, wolf, palm_tree, porcupine, mountain, cockroach, sweet_pepper, willow_tree, tiger, caterpillar, shrew, mushroom, bee, shark, otter, girl, rose, crocodile, television, boy, baby, turtle, snake, ray, aquarium_fish, skunk, sea, spider, squirrel, bus, pickup_truck, tank, lamp, beetle, train, pear, orchid, chimpanzee
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Model tree for ProbeX/Model-J__SupViT__model_idx_0201
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0201") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")