Instructions to use nanchennn/ViT-LoRA-Food101-0312 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nanchennn/ViT-LoRA-Food101-0312 with PEFT:
Task type is invalid.
- Transformers
How to use nanchennn/ViT-LoRA-Food101-0312 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nanchennn/ViT-LoRA-Food101-0312", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "run_id": "ViT-LoRA-Food101-0312", | |
| "base_model": "google/vit-large-patch16-224-in21k", | |
| "dataset": "food101", | |
| "num_total_classes": 101, | |
| "num_selected_classes": 50, | |
| "selected_class_ids": [ | |
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| ], | |
| "selected_class_names": [ | |
| "apple_pie", | |
| "baklava", | |
| "beef_carpaccio", | |
| "beef_tartare", | |
| "beignets", | |
| "bread_pudding", | |
| "caesar_salad", | |
| "cannoli", | |
| "ceviche", | |
| "cheese_plate", | |
| "chicken_quesadilla", | |
| "chocolate_mousse", | |
| "churros", | |
| "club_sandwich", | |
| "creme_brulee", | |
| "croque_madame", | |
| "cup_cakes", | |
| "deviled_eggs", | |
| "donuts", | |
| "edamame", | |
| "filet_mignon", | |
| "fish_and_chips", | |
| "french_fries", | |
| "guacamole", | |
| "hot_dog", | |
| "ice_cream", | |
| "lobster_bisque", | |
| "macaroni_and_cheese", | |
| "miso_soup", | |
| "nachos", | |
| "panna_cotta", | |
| "peking_duck", | |
| "pizza", | |
| "poutine", | |
| "ramen", | |
| "ravioli", | |
| "red_velvet_cake", | |
| "samosa", | |
| "sashimi", | |
| "scallops", | |
| "seaweed_salad", | |
| "spaghetti_bolognese", | |
| "spring_rolls", | |
| "steak", | |
| "strawberry_shortcake", | |
| "tacos", | |
| "takoyaki", | |
| "tiramisu", | |
| "tuna_tartare", | |
| "waffles" | |
| ], | |
| "seed": 4077482254, | |
| "lora": { | |
| "rank": 32, | |
| "target_modules": [ | |
| "query", | |
| "key", | |
| "value" | |
| ] | |
| }, | |
| "hparams": { | |
| "learning_rate": 5e-05, | |
| "weight_decay": 0.05, | |
| "lr_scheduler_raw": "constant", | |
| "epochs": 8, | |
| "random_crop": false, | |
| "random_flip": false, | |
| "global_batch_size": 64, | |
| "per_device_batch_size": 64, | |
| "grad_accumulation_steps": 1 | |
| }, | |
| "sizes": { | |
| "train_examples": 37500, | |
| "eval_examples": 12500 | |
| }, | |
| "metrics": { | |
| "top1_accuracy": 0.928, | |
| "eval_loss": 0.25284621119499207, | |
| "eval_accuracy": 0.928, | |
| "eval_runtime": 25.4349, | |
| "eval_samples_per_second": 491.451, | |
| "eval_steps_per_second": 7.706, | |
| "epoch": 8.0 | |
| } | |
| } |