Commit
•
bbd601a
1
Parent(s):
7dfbf53
update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
datasets:
|
| 6 |
+
- imagefolder
|
| 7 |
+
metrics:
|
| 8 |
+
- accuracy
|
| 9 |
+
model-index:
|
| 10 |
+
- name: resnet-101-finetuned_resnet101-adam-optimizer5e-4-autotags
|
| 11 |
+
results:
|
| 12 |
+
- task:
|
| 13 |
+
name: Image Classification
|
| 14 |
+
type: image-classification
|
| 15 |
+
dataset:
|
| 16 |
+
name: imagefolder
|
| 17 |
+
type: imagefolder
|
| 18 |
+
config: default
|
| 19 |
+
split: train
|
| 20 |
+
args: default
|
| 21 |
+
metrics:
|
| 22 |
+
- name: Accuracy
|
| 23 |
+
type: accuracy
|
| 24 |
+
value: 0.9266666666666666
|
| 25 |
+
---
|
| 26 |
+
|
| 27 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 28 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 29 |
+
|
| 30 |
+
# resnet-101-finetuned_resnet101-adam-optimizer5e-4-autotags
|
| 31 |
+
|
| 32 |
+
This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
|
| 33 |
+
It achieves the following results on the evaluation set:
|
| 34 |
+
- Loss: 0.2477
|
| 35 |
+
- Accuracy: 0.9267
|
| 36 |
+
|
| 37 |
+
## Model description
|
| 38 |
+
|
| 39 |
+
More information needed
|
| 40 |
+
|
| 41 |
+
## Intended uses & limitations
|
| 42 |
+
|
| 43 |
+
More information needed
|
| 44 |
+
|
| 45 |
+
## Training and evaluation data
|
| 46 |
+
|
| 47 |
+
More information needed
|
| 48 |
+
|
| 49 |
+
## Training procedure
|
| 50 |
+
|
| 51 |
+
### Training hyperparameters
|
| 52 |
+
|
| 53 |
+
The following hyperparameters were used during training:
|
| 54 |
+
- learning_rate: 0.0005
|
| 55 |
+
- train_batch_size: 16
|
| 56 |
+
- eval_batch_size: 16
|
| 57 |
+
- seed: 42
|
| 58 |
+
- gradient_accumulation_steps: 4
|
| 59 |
+
- total_train_batch_size: 64
|
| 60 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 61 |
+
- lr_scheduler_type: linear
|
| 62 |
+
- lr_scheduler_warmup_ratio: 0.1
|
| 63 |
+
- num_epochs: 20
|
| 64 |
+
|
| 65 |
+
### Training results
|
| 66 |
+
|
| 67 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
| 68 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|
|
| 69 |
+
| 2.6033 | 0.99 | 65 | 2.5693 | 0.1381 |
|
| 70 |
+
| 1.5517 | 1.99 | 130 | 1.1376 | 0.6733 |
|
| 71 |
+
| 0.9423 | 2.99 | 195 | 0.6290 | 0.7895 |
|
| 72 |
+
| 0.6334 | 3.99 | 260 | 0.4372 | 0.86 |
|
| 73 |
+
| 0.4735 | 4.99 | 325 | 0.4719 | 0.8429 |
|
| 74 |
+
| 0.4573 | 5.99 | 390 | 0.3909 | 0.8590 |
|
| 75 |
+
| 0.3236 | 6.99 | 455 | 0.3507 | 0.8752 |
|
| 76 |
+
| 0.2511 | 7.99 | 520 | 0.2931 | 0.9019 |
|
| 77 |
+
| 0.2073 | 8.99 | 585 | 0.2757 | 0.9133 |
|
| 78 |
+
| 0.2174 | 9.99 | 650 | 0.2706 | 0.9114 |
|
| 79 |
+
| 0.1558 | 10.99 | 715 | 0.2654 | 0.9114 |
|
| 80 |
+
| 0.2017 | 11.99 | 780 | 0.2820 | 0.9114 |
|
| 81 |
+
| 0.134 | 12.99 | 845 | 0.2431 | 0.9238 |
|
| 82 |
+
| 0.0943 | 13.99 | 910 | 0.2606 | 0.9105 |
|
| 83 |
+
| 0.1396 | 14.99 | 975 | 0.2514 | 0.9229 |
|
| 84 |
+
| 0.1374 | 15.99 | 1040 | 0.2349 | 0.9305 |
|
| 85 |
+
| 0.0953 | 16.99 | 1105 | 0.2502 | 0.9210 |
|
| 86 |
+
| 0.0742 | 17.99 | 1170 | 0.2515 | 0.9210 |
|
| 87 |
+
| 0.0708 | 18.99 | 1235 | 0.2437 | 0.9257 |
|
| 88 |
+
| 0.0619 | 19.99 | 1300 | 0.2477 | 0.9267 |
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
### Framework versions
|
| 92 |
+
|
| 93 |
+
- Transformers 4.25.1
|
| 94 |
+
- Pytorch 1.13.1+cu117
|
| 95 |
+
- Datasets 2.11.0
|
| 96 |
+
- Tokenizers 0.13.2
|