paul
commited on
Commit
•
08f77c6
1
Parent(s):
5a2e2ee
update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,111 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
datasets:
|
6 |
+
- imagefolder
|
7 |
+
metrics:
|
8 |
+
- accuracy
|
9 |
+
- precision
|
10 |
+
- recall
|
11 |
+
- f1
|
12 |
+
model-index:
|
13 |
+
- name: resnet152-FV-finetuned-memes
|
14 |
+
results:
|
15 |
+
- task:
|
16 |
+
name: Image Classification
|
17 |
+
type: image-classification
|
18 |
+
dataset:
|
19 |
+
name: imagefolder
|
20 |
+
type: imagefolder
|
21 |
+
config: default
|
22 |
+
split: train
|
23 |
+
args: default
|
24 |
+
metrics:
|
25 |
+
- name: Accuracy
|
26 |
+
type: accuracy
|
27 |
+
value: 0.7557959814528593
|
28 |
+
- name: Precision
|
29 |
+
type: precision
|
30 |
+
value: 0.7556690736625777
|
31 |
+
- name: Recall
|
32 |
+
type: recall
|
33 |
+
value: 0.7557959814528593
|
34 |
+
- name: F1
|
35 |
+
type: f1
|
36 |
+
value: 0.7545674798253312
|
37 |
+
---
|
38 |
+
|
39 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
40 |
+
should probably proofread and complete it, then remove this comment. -->
|
41 |
+
|
42 |
+
# resnet152-FV-finetuned-memes
|
43 |
+
|
44 |
+
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
|
45 |
+
It achieves the following results on the evaluation set:
|
46 |
+
- Loss: 0.6772
|
47 |
+
- Accuracy: 0.7558
|
48 |
+
- Precision: 0.7557
|
49 |
+
- Recall: 0.7558
|
50 |
+
- F1: 0.7546
|
51 |
+
|
52 |
+
## Model description
|
53 |
+
|
54 |
+
More information needed
|
55 |
+
|
56 |
+
## Intended uses & limitations
|
57 |
+
|
58 |
+
More information needed
|
59 |
+
|
60 |
+
## Training and evaluation data
|
61 |
+
|
62 |
+
More information needed
|
63 |
+
|
64 |
+
## Training procedure
|
65 |
+
|
66 |
+
### Training hyperparameters
|
67 |
+
|
68 |
+
The following hyperparameters were used during training:
|
69 |
+
- learning_rate: 0.00012
|
70 |
+
- train_batch_size: 64
|
71 |
+
- eval_batch_size: 64
|
72 |
+
- seed: 42
|
73 |
+
- gradient_accumulation_steps: 4
|
74 |
+
- total_train_batch_size: 256
|
75 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
76 |
+
- lr_scheduler_type: linear
|
77 |
+
- lr_scheduler_warmup_ratio: 0.1
|
78 |
+
- num_epochs: 20
|
79 |
+
|
80 |
+
### Training results
|
81 |
+
|
82 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|
83 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
|
84 |
+
| 1.5739 | 0.99 | 20 | 1.5427 | 0.4521 | 0.3131 | 0.4521 | 0.2880 |
|
85 |
+
| 1.4353 | 1.99 | 40 | 1.3786 | 0.4490 | 0.3850 | 0.4490 | 0.2791 |
|
86 |
+
| 1.3026 | 2.99 | 60 | 1.2734 | 0.4799 | 0.3073 | 0.4799 | 0.3393 |
|
87 |
+
| 1.1579 | 3.99 | 80 | 1.1378 | 0.5278 | 0.4300 | 0.5278 | 0.4143 |
|
88 |
+
| 1.0276 | 4.99 | 100 | 1.0231 | 0.5734 | 0.4497 | 0.5734 | 0.4865 |
|
89 |
+
| 0.8826 | 5.99 | 120 | 0.9228 | 0.6252 | 0.5983 | 0.6252 | 0.5637 |
|
90 |
+
| 0.766 | 6.99 | 140 | 0.8441 | 0.6662 | 0.6474 | 0.6662 | 0.6320 |
|
91 |
+
| 0.6732 | 7.99 | 160 | 0.8009 | 0.6901 | 0.6759 | 0.6901 | 0.6704 |
|
92 |
+
| 0.5653 | 8.99 | 180 | 0.7535 | 0.7218 | 0.7141 | 0.7218 | 0.7129 |
|
93 |
+
| 0.4957 | 9.99 | 200 | 0.7317 | 0.7257 | 0.7248 | 0.7257 | 0.7200 |
|
94 |
+
| 0.4534 | 10.99 | 220 | 0.6808 | 0.7434 | 0.7405 | 0.7434 | 0.7390 |
|
95 |
+
| 0.3792 | 11.99 | 240 | 0.6949 | 0.7450 | 0.7454 | 0.7450 | 0.7399 |
|
96 |
+
| 0.3489 | 12.99 | 260 | 0.6746 | 0.7496 | 0.7511 | 0.7496 | 0.7474 |
|
97 |
+
| 0.3113 | 13.99 | 280 | 0.6637 | 0.7573 | 0.7638 | 0.7573 | 0.7579 |
|
98 |
+
| 0.2947 | 14.99 | 300 | 0.6451 | 0.7589 | 0.7667 | 0.7589 | 0.7610 |
|
99 |
+
| 0.2776 | 15.99 | 320 | 0.6754 | 0.7543 | 0.7565 | 0.7543 | 0.7525 |
|
100 |
+
| 0.2611 | 16.99 | 340 | 0.6808 | 0.7550 | 0.7607 | 0.7550 | 0.7529 |
|
101 |
+
| 0.2428 | 17.99 | 360 | 0.7005 | 0.7457 | 0.7497 | 0.7457 | 0.7404 |
|
102 |
+
| 0.2346 | 18.99 | 380 | 0.6597 | 0.7573 | 0.7642 | 0.7573 | 0.7590 |
|
103 |
+
| 0.2367 | 19.99 | 400 | 0.6772 | 0.7558 | 0.7557 | 0.7558 | 0.7546 |
|
104 |
+
|
105 |
+
|
106 |
+
### Framework versions
|
107 |
+
|
108 |
+
- Transformers 4.24.0.dev0
|
109 |
+
- Pytorch 1.11.0+cu102
|
110 |
+
- Datasets 2.6.1.dev0
|
111 |
+
- Tokenizers 0.13.1
|