Hugging Face's logo

embunna
/
resnet-18-finetuned-resnet

Image Classification
Transformers
TensorBoard
Safetensors
resnet
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
embunna commited on
Commit
7476cf0
•
1 Parent(s): b2c738f

Model save

Browse files
Files changed (2) hide show
  1. README.md +16 -10
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
- value: 1.0
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
32
 
33
  This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
34
  It achieves the following results on the evaluation set:
35
- - Loss: 1.0586
36
- - Accuracy: 1.0
37
 
38
  ## Model description
39
 
@@ -61,20 +61,26 @@ The following hyperparameters were used during training:
61
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
  - lr_scheduler_type: linear
63
  - lr_scheduler_warmup_ratio: 0.1
64
- - num_epochs: 3
65
 
66
  ### Training results
67
 
68
- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
69
- |:-------------:|:------:|:----:|:---------------:|:--------:|
70
- | No log | 0.9091 | 5 | 1.0897 | 0.4805 |
71
- | 1.0892 | 2.0 | 11 | 1.0586 | 1.0 |
72
- | 1.0892 | 2.7273 | 15 | 1.0516 | 1.0 |
 
 
 
 
 
 
73
 
74
 
75
  ### Framework versions
76
 
77
- - Transformers 4.41.1
78
  - Pytorch 2.3.0+cu121
79
  - Datasets 2.19.2
80
  - Tokenizers 0.19.1
 
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.28125
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 31846918359351296.0000
36
+ - Accuracy: 0.2812
37
 
38
  ## Model description
39
 
 
61
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
  - lr_scheduler_type: linear
63
  - lr_scheduler_warmup_ratio: 0.1
64
+ - num_epochs: 10
65
 
66
  ### Training results
67
 
68
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
69
+ |:----------------------:|:------:|:----:|:----------------------:|:--------:|
70
+ | No log | 0.8889 | 6 | 34464189825155072.0000 | 0.2812 |
71
+ | 48682629666439168.0000 | 1.9259 | 13 | 31846918359351296.0000 | 0.2812 |
72
+ | 29555741855999592.0000 | 2.9630 | 20 | 31846918359351296.0000 | 0.2812 |
73
+ | 29555741855999592.0000 | 4.0 | 27 | 31846918359351296.0000 | 0.2812 |
74
+ | 28954917162975232.0000 | 4.8889 | 33 | 31846918359351296.0000 | 0.2812 |
75
+ | 29873892417444248.0000 | 5.9259 | 40 | 31846918359351296.0000 | 0.2812 |
76
+ | 29873892417444248.0000 | 6.9630 | 47 | 31846918359351296.0000 | 0.2812 |
77
+ | 29575110440517632.0000 | 8.0 | 54 | 31846918359351296.0000 | 0.2812 |
78
+ | 29550508867846144.0000 | 8.8889 | 60 | 31846918359351296.0000 | 0.2812 |
79
 
80
 
81
  ### Framework versions
82
 
83
+ - Transformers 4.41.2
84
  - Pytorch 2.3.0+cu121
85
  - Datasets 2.19.2
86
  - Tokenizers 0.19.1
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:1cfd1f200e38c2784b76a1e0ec044ff987f0b2784a480db1cd8c21ae68bf7a5f
3
  size 44766388
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e98a11465b7c0fb2b32eca1d73d02300a06192be747c2332586360ddb3816d06
3
  size 44766388