Fine-tuning Vietnamese Characters with Incorrect Accuracy

[machoangha]

Hi,

I am attempting to fine-tune a model with Vietnamese characters using the configuration provided below. I updated DICT90 to include 219 characters, as follows:

DICT90 = tuple('AÁÀẠẢÃĂẮẰẲẶẴÂẤẦẨẪẬBCDĐEÈÉẼẺẸÊẾỀỂỄỆFGHIÍÌỈĨỊJKLMNOPQRSTUVWXYZ'
               'aáàạảãăằắẳẵặâấầẩẫậbcdđeèéẻẽẹêếềểễệfghiíìỉĩịjklmnopqrstuvwxyz'
               '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]_~') # 219 characters

Here is my CCD_vision_model_ARD.yaml configuration:

global:
  name: finetune_small_65536_1
  phase: train
  stage: train-supervised
  workdir: workdir
  seed: ~

output_dir: './saved_models/'

dataset:
  scheme: supervised
  type: ST
  train: {
    roots: [
        './Dino/data_lmdb/training',
    ],
    batch_size: 8,
  }
  valid: {
    roots: [
        './Dino/data_lmdb/validation'
    ],
    batch_size: 8
  }
  test: {
    roots: [
        './Dino/data_lmdb/evaluation'
    ],
    batch_size: 14
  }
  data_aug: True
  multiscales: False
  mask: False
  num_workers: 6
  augmentation_severity: 0
  charset_path: './Dino/data/charset_vi.txt' # Vietnamese charset
  charset_type: 'DICT90' # Changed to Vietnamese charset in base.py

training:
  epochs: 20
  start_iters: 0
  show_iters: 1000
  eval_iters: 1000
  save_iters: 1000

model:
  pretrain_checkpoint: 'saved_models/Small_ARD_checkpoint.pth'
  checkpoint:
decoder:
  type: 'NRTRDecoder'
  n_layers: 6
  d_embedding: 512
  n_head: 8
  d_model: 512
  d_inner: 256
  d_k: 64
  d_v: 64
  num_classes: 221   # 219 + 2
  max_seq_len: 25
  start_idx: 220    # 219 + 1
  padding_idx: 221  # 219 + 2

mp:
  num: 4

arch: 'vit_small'
patch_size: 4
out_dim: 65536
weight_decay: 0.05
clip_grad: ~
lr: 0.0005
warmup_epochs: 2
min_lr: 0.000001
optimizer: adamw
drop_path_rate: 0.1
seed: 0
num_workers: 8

After running finetune.py, the model's accuracy is only 0. I am unsure if there is an error in the configuration or if something else might be wrong.

Could you please help me identify if there is any mistake in the setup or configuration? Any guidance or suggestions would be greatly appreciated.

Thanks.

[TongkunGuan]

Hi,

I am attempting to fine-tune a model with Vietnamese characters using the configuration provided below. I updated DICT90 to include 219 characters, as follows:

DICT90 = tuple('AÁÀẠẢÃĂẮẰẲẶẴÂẤẦẨẪẬBCDĐEÈÉẼẺẸÊẾỀỂỄỆFGHIÍÌỈĨỊJKLMNOPQRSTUVWXYZ'
               'aáàạảãăằắẳẵặâấầẩẫậbcdđeèéẻẽẹêếềểễệfghiíìỉĩịjklmnopqrstuvwxyz'
               '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]_~') # 219 characters

Here is my CCD_vision_model_ARD.yaml configuration:

global:
  name: finetune_small_65536_1
  phase: train
  stage: train-supervised
  workdir: workdir
  seed: ~

output_dir: './saved_models/'

dataset:
  scheme: supervised
  type: ST
  train: {
    roots: [
        './Dino/data_lmdb/training',
    ],
    batch_size: 8,
  }
  valid: {
    roots: [
        './Dino/data_lmdb/validation'
    ],
    batch_size: 8
  }
  test: {
    roots: [
        './Dino/data_lmdb/evaluation'
    ],
    batch_size: 14
  }
  data_aug: True
  multiscales: False
  mask: False
  num_workers: 6
  augmentation_severity: 0
  charset_path: './Dino/data/charset_vi.txt' # Vietnamese charset
  charset_type: 'DICT90' # Changed to Vietnamese charset in base.py

training:
  epochs: 20
  start_iters: 0
  show_iters: 1000
  eval_iters: 1000
  save_iters: 1000

model:
  pretrain_checkpoint: 'saved_models/Small_ARD_checkpoint.pth'
  checkpoint:
decoder:
  type: 'NRTRDecoder'
  n_layers: 6
  d_embedding: 512
  n_head: 8
  d_model: 512
  d_inner: 256
  d_k: 64
  d_v: 64
  num_classes: 221   # 219 + 2
  max_seq_len: 25
  start_idx: 220    # 219 + 1
  padding_idx: 221  # 219 + 2

mp:
  num: 4

arch: 'vit_small'
patch_size: 4
out_dim: 65536
weight_decay: 0.05
clip_grad: ~
lr: 0.0005
warmup_epochs: 2
min_lr: 0.000001
optimizer: adamw
drop_path_rate: 0.1
seed: 0
num_workers: 8

After running finetune.py, the model's accuracy is only 0. I am unsure if there is an error in the configuration or if something else might be wrong.

Could you please help me identify if there is any mistake in the setup or configuration? Any guidance or suggestions would be greatly appreciated.

Thanks.

I think you should pay more attention to the details of the evaluation. For example: https://github.com/TongkunGuan/CCD/blob/543109a1e1d9acd15080abb3e4e72d68588ba493/Dino/metric/eval_acc.py#L38

[machoangha]

Hi

I print out the results but they all the same

[TongkunGuan]

Hi

I print out the results but they all the same

could you print the loss curve?

[machoangha]

eval model
iteration:1000--> train loss:2.2958712577819824
eval model
iteration:2000--> train loss:1.863906741142273
eval model
iteration:3000--> train loss:1.80726158618927
eval model
iteration:4000--> train loss:1.7889494895935059
eval model
iteration:5000--> train loss:1.7705328464508057
eval model

This is after 12 epochs training, the result only predicts 1 word for every test case