Image Classification
PEFT
Safetensors
PyTorch
English
vit
vit-small
lora
cifar100
fine-tuning
parameter-efficient
Eval Results (legacy)
Instructions to use MSG1999/vit-lora-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MSG1999/vit-lora-cifar100 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Add q1_summary.json
Browse files- q1_summary.json +132 -0
q1_summary.json
ADDED
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[
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{
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"exp_name": "exp01_no_lora",
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"use_lora": false,
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"rank": 0,
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"alpha": 0,
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"dropout": 0.1,
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"best_val_acc": 0.8077,
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"overall_test_acc": 0.8077000379562378,
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| 10 |
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"trainable_params": 38500,
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"total_params": 21704164,
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| 12 |
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"ckpt_path": "weights/exp01_no_lora_best.pt",
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"log_path": "logs/exp01_no_lora.log"
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},
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{
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"exp_name": "exp02_r2_a2",
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"use_lora": true,
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"rank": 2,
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"alpha": 2,
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"dropout": 0.1,
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"best_val_acc": 0.8965,
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| 22 |
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"overall_test_acc": 0.8964999914169312,
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| 23 |
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"trainable_params": 93796,
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| 24 |
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"total_params": 21759460,
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| 25 |
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"ckpt_path": "weights/exp02_r2_a2_best.pt",
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"log_path": "logs/exp02_r2_a2.log"
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},
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{
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"exp_name": "exp03_r2_a4",
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"use_lora": true,
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"rank": 2,
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"alpha": 4,
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"dropout": 0.1,
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| 34 |
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"best_val_acc": 0.9003,
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| 35 |
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"overall_test_acc": 0.9003000855445862,
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| 36 |
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"total_params": 21759460,
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| 38 |
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"ckpt_path": "weights/exp03_r2_a4_best.pt",
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"log_path": "logs/exp03_r2_a4.log"
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},
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{
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"exp_name": "exp04_r2_a8",
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"use_lora": true,
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"rank": 2,
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"alpha": 8,
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| 46 |
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"dropout": 0.1,
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| 47 |
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"best_val_acc": 0.8998,
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| 48 |
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"overall_test_acc": 0.8996999859809875,
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| 50 |
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"total_params": 21759460,
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| 51 |
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"log_path": "logs/exp04_r2_a8.log"
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},
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{
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"exp_name": "exp05_r4_a2",
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"use_lora": true,
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"rank": 4,
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"alpha": 2,
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"dropout": 0.1,
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| 60 |
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"best_val_acc": 0.8991,
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| 61 |
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"overall_test_acc": 0.8991000652313232,
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| 62 |
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| 63 |
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"total_params": 21814756,
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"log_path": "logs/exp05_r4_a2.log"
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},
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| 67 |
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{
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"exp_name": "exp06_r4_a4",
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"use_lora": true,
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| 70 |
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"rank": 4,
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| 71 |
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"alpha": 4,
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| 72 |
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"dropout": 0.1,
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| 73 |
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"best_val_acc": 0.9011,
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| 74 |
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"overall_test_acc": 0.9011001586914062,
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| 75 |
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| 76 |
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"total_params": 21814756,
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| 77 |
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"ckpt_path": "weights/exp06_r4_a4_best.pt",
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"log_path": "logs/exp06_r4_a4.log"
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},
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{
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"exp_name": "exp07_r4_a8",
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"use_lora": true,
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"rank": 4,
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| 84 |
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"alpha": 8,
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"dropout": 0.1,
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| 86 |
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"best_val_acc": 0.9028,
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| 87 |
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"overall_test_acc": 0.9027999639511108,
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| 88 |
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| 89 |
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"total_params": 21814756,
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| 90 |
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"ckpt_path": "weights/exp07_r4_a8_best.pt",
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"log_path": "logs/exp07_r4_a8.log"
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},
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{
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"exp_name": "exp08_r8_a2",
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"use_lora": true,
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"alpha": 2,
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"dropout": 0.1,
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"best_val_acc": 0.9009,
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| 100 |
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"ckpt_path": "weights/exp08_r8_a2_best.pt",
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"log_path": "logs/exp08_r8_a2.log"
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},
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{
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"exp_name": "exp09_r8_a4",
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"use_lora": true,
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"rank": 8,
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"alpha": 4,
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"best_val_acc": 0.9017,
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"ckpt_path": "weights/exp09_r8_a4_best.pt",
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"log_path": "logs/exp09_r8_a4.log"
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},
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{
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"exp_name": "exp10_r8_a8",
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"use_lora": true,
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"rank": 8,
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"alpha": 8,
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"dropout": 0.1,
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"best_val_acc": 0.9046,
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"ckpt_path": "weights/exp10_r8_a8_best.pt",
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"log_path": "logs/exp10_r8_a8.log"
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}
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]
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