Custom dataset Linear Eval

[letdivedeep]

@MidoAssran and team great work !!!

I will like to use this for my custom dataset to build a linear classification model. I started the model on my custom dataset by modifying the config/pretrain/msn_vits16.yaml

criterion:
 ent_weight: 0.0
 final_sharpen: 0.25
 me_max: true
 memax_weight: 1.0
 num_proto: 1024
 start_sharpen: 0.25
 temperature: 0.1
 batch_size: 64
 use_ent: true
 use_sinkhorn: true
data:
 color_jitter_strength: 0.5
 pin_mem: true
 num_workers: 10
 image_folder: custom_db/
 label_smoothing: 0.0
 patch_drop: 0.15
 rand_size: 224
 focal_size: 96
 rand_views: 1
 focal_views: 10
 root_path: dataset/
logging:
 folder: saved_models/msn_os_logs/
 write_tag: msn-experiment-1
meta:
 bottleneck: 1
copy_data: false
 drop_path_rate: 0.0
 hidden_dim: 2048
 load_checkpoint: false
 model_name: deit_small
 output_dim: 256
 read_checkpoint: null
 use_bn: true
 use_fp16: false
 use_pred_head: false
optimization:
 clip_grad: 3.0
 epochs: 350
 final_lr: 1.0e-06
 final_weight_decay: 0.4
 lr: 0.001
 start_lr: 0.0002
 warmup: 15
 weight_decay: 0.04

and start the model pre-training using this cmd
python main.py --fname configs/pretrain/msn_vits16.yaml --devices cuda:0

was able to start the model training as seen in the below screenshots

2) Downstream Task of Linear Classifier

confused on how to start the downstream task of image classification
Question :

• How to start the model training using a linear classifier on a custom dataset without distributed training?
• How to structure the dataset : dataset> custom_db > cls_id > images will this pattern work ?

Any help will be appreciated