google

/

mobilenet_v2_1.0_224

like 9

It could be because of the difference for class sizes, giving the following error:

[WARNING|modeling_utils.py:2628] 2022-12-07 03:53:36,920 >> Some weights of MobileNetV1ForImageClassification were not initialized from the model checkpoint at Matthijs/mobilenet_v1_0.75_192 and are newly initialized because the shapes did not match:
- classifier.weight: found shape torch.Size([1001, 768]) in the checkpoint and torch.Size([1000, 768]) in the model instantiated
- classifier.bias: found shape torch.Size([1001]) in the checkpoint and torch.Size([1000]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

I have tried the following training as well but so far the accuracy is very low eval_accuracy -> 0.001

lr=0.01
epochs=20

model=${1}


base_dir=mobilenetv1_lr${lr}_epochs_${epochs}_optim_${optim}_lr_schedule_${lr_schedule}
rm -rf ${base_dir}

python3 transformer-sparsity/examples/pytorch/image-classification/run_image_classification.py \
    --model_name_or_path ${model} \
    --dataset_name imagenet-1k \
    --remove_unused_columns false \
    --per_device_train_batch_size 256 \
    --per_device_eval_batch_size 256 \
    --do_train \
    --do_eval \
    --lr_scheduler_type constant \
    --weight_decay 0.00004 \
    --load_best_model_at_end \
    --eval_steps ${eval_steps} \
    --save_steps ${eval_steps} \
    --evaluation_strategy steps \
    --logging_steps ${eval_steps} \
    --logging_strategy steps \
    --num_train_epochs ${epochs} \
    --learning_rate ${lr} \
    --optim rmsprop \
    --ignore_mismatched_sizes true \
    --save_total_limit 2 \
    --overwrite_output_dir \
    --output_dir ~/${base_dir} 2>&1 | tee ~/${base_dir}/mobilenetv1_finetune.txt

I don't understand why my accuracy is horrible, too.