code image-classification dataset:cifar10 license:apache-2.0 region:us
---
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- code
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.8061
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- optimizer: SGD with momentum = 0.9
- num_epochs: 7

### Training results

Epoch [1/7], Batch [100/1563], Loss: 2.3931
Epoch [1/7], Batch [200/1563], Loss: 1.3625
Epoch [1/7], Batch [300/1563], Loss: 1.1862
Epoch [1/7], Batch [400/1563], Loss: 1.1345
Epoch [1/7], Batch [500/1563], Loss: 1.0241
Epoch [1/7], Batch [600/1563], Loss: 0.9849
Epoch [1/7], Batch [700/1563], Loss: 0.9894
Epoch [1/7], Batch [800/1563], Loss: 0.9365
Epoch [1/7], Batch [900/1563], Loss: 0.9296
Epoch [1/7], Batch [1000/1563], Loss: 0.8903
Epoch [1/7], Batch [1100/1563], Loss: 0.8545
Epoch [1/7], Batch [1200/1563], Loss: 0.8403
Epoch [1/7], Batch [1300/1563], Loss: 0.8406
Epoch [1/7], Batch [1400/1563], Loss: 0.7841
Epoch [1/7], Batch [1500/1563], Loss: 0.8174
Epoch [1/7], Test Accuracy: 73.55%
Epoch [2/7], Batch [100/1563], Loss: 0.6522
Epoch [2/7], Batch [200/1563], Loss: 0.6566
Epoch [2/7], Batch [300/1563], Loss: 0.6582
Epoch [2/7], Batch [400/1563], Loss: 0.7010
Epoch [2/7], Batch [500/1563], Loss: 0.6643
Epoch [2/7], Batch [600/1563], Loss: 0.6554
Epoch [2/7], Batch [700/1563], Loss: 0.6579
Epoch [2/7], Batch [800/1563], Loss: 0.6387
Epoch [2/7], Batch [900/1563], Loss: 0.6481
Epoch [2/7], Batch [1000/1563], Loss: 0.6986
Epoch [2/7], Batch [1100/1563], Loss: 0.6244
Epoch [2/7], Batch [1200/1563], Loss: 0.6116
Epoch [2/7], Batch [1300/1563], Loss: 0.6085
Epoch [2/7], Batch [1400/1563], Loss: 0.6320
Epoch [2/7], Batch [1500/1563], Loss: 0.6062
Epoch [2/7], Test Accuracy: 76.96%
Epoch [3/7], Batch [100/1563], Loss: 0.5028
Epoch [3/7], Batch [200/1563], Loss: 0.4958
Epoch [3/7], Batch [300/1563], Loss: 0.4761
Epoch [3/7], Batch [400/1563], Loss: 0.4858
Epoch [3/7], Batch [500/1563], Loss: 0.5087
Epoch [3/7], Batch [600/1563], Loss: 0.5235
Epoch [3/7], Batch [700/1563], Loss: 0.5242
Epoch [3/7], Batch [800/1563], Loss: 0.4967
Epoch [3/7], Batch [900/1563], Loss: 0.5349
Epoch [3/7], Batch [1000/1563], Loss: 0.4925
Epoch [3/7], Batch [1100/1563], Loss: 0.5135
Epoch [3/7], Batch [1200/1563], Loss: 0.5407
Epoch [3/7], Batch [1300/1563], Loss: 0.5301
Epoch [3/7], Batch [1400/1563], Loss: 0.4891
Epoch [3/7], Batch [1500/1563], Loss: 0.5025
Epoch [3/7], Test Accuracy: 78.17%
Epoch [4/7], Batch [100/1563], Loss: 0.4175
Epoch [4/7], Batch [200/1563], Loss: 0.4080
Epoch [4/7], Batch [300/1563], Loss: 0.3963
Epoch [4/7], Batch [400/1563], Loss: 0.3793
Epoch [4/7], Batch [500/1563], Loss: 0.4056
Epoch [4/7], Batch [600/1563], Loss: 0.4050
Epoch [4/7], Batch [700/1563], Loss: 0.4187
Epoch [4/7], Batch [800/1563], Loss: 0.4049
Epoch [4/7], Batch [900/1563], Loss: 0.4062
Epoch [4/7], Batch [1000/1563], Loss: 0.4429
Epoch [4/7], Batch [1100/1563], Loss: 0.4086
Epoch [4/7], Batch [1200/1563], Loss: 0.4671
Epoch [4/7], Batch [1300/1563], Loss: 0.3966
Epoch [4/7], Batch [1400/1563], Loss: 0.4148
Epoch [4/7], Batch [1500/1563], Loss: 0.4076
Epoch [4/7], Test Accuracy: 79.47%
Epoch [5/7], Batch [100/1563], Loss: 0.3093
Epoch [5/7], Batch [200/1563], Loss: 0.2897
Epoch [5/7], Batch [300/1563], Loss: 0.3384
Epoch [5/7], Batch [400/1563], Loss: 0.3241
Epoch [5/7], Batch [500/1563], Loss: 0.3333
Epoch [5/7], Batch [600/1563], Loss: 0.3253
Epoch [5/7], Batch [700/1563], Loss: 0.3405
Epoch [5/7], Batch [800/1563], Loss: 0.3830
Epoch [5/7], Batch [900/1563], Loss: 0.3382
Epoch [5/7], Batch [1000/1563], Loss: 0.3354
Epoch [5/7], Batch [1100/1563], Loss: 0.3279
Epoch [5/7], Batch [1200/1563], Loss: 0.3300
Epoch [5/7], Batch [1300/1563], Loss: 0.3504
Epoch [5/7], Batch [1400/1563], Loss: 0.3543
Epoch [5/7], Batch [1500/1563], Loss: 0.3478
Epoch [5/7], Test Accuracy: 78.57%
Epoch [6/7], Batch [100/1563], Loss: 0.2658
Epoch [6/7], Batch [200/1563], Loss: 0.2491
Epoch [6/7], Batch [300/1563], Loss: 0.2448
Epoch [6/7], Batch [400/1563], Loss: 0.2257
Epoch [6/7], Batch [500/1563], Loss: 0.2567
Epoch [6/7], Batch [600/1563], Loss: 0.2502
Epoch [6/7], Batch [700/1563], Loss: 0.2697
Epoch [6/7], Batch [800/1563], Loss: 0.2783
Epoch [6/7], Batch [900/1563], Loss: 0.2748
Epoch [6/7], Batch [1000/1563], Loss: 0.2917
Epoch [6/7], Batch [1100/1563], Loss: 0.2945
Epoch [6/7], Batch [1200/1563], Loss: 0.2940
Epoch [6/7], Batch [1300/1563], Loss: 0.2975
Epoch [6/7], Batch [1400/1563], Loss: 0.2797
Epoch [6/7], Batch [1500/1563], Loss: 0.2926
Epoch [6/7], Test Accuracy: 80.09%
Epoch [7/7], Batch [100/1563], Loss: 0.2036
Epoch [7/7], Batch [200/1563], Loss: 0.2021
Epoch [7/7], Batch [300/1563], Loss: 0.1882
Epoch [7/7], Batch [400/1563], Loss: 0.2069
Epoch [7/7], Batch [500/1563], Loss: 0.2181
Epoch [7/7], Batch [600/1563], Loss: 0.2305
Epoch [7/7], Batch [700/1563], Loss: 0.2423
Epoch [7/7], Batch [800/1563], Loss: 0.2296
Epoch [7/7], Batch [900/1563], Loss: 0.2426
Epoch [7/7], Batch [1000/1563], Loss: 0.2573
Epoch [7/7], Batch [1100/1563], Loss: 0.2557
Epoch [7/7], Batch [1200/1563], Loss: 0.2294
Epoch [7/7], Batch [1300/1563], Loss: 0.2266
Epoch [7/7], Batch [1400/1563], Loss: 0.2631
Epoch [7/7], Batch [1500/1563], Loss: 0.2518
Epoch [7/7], Test Accuracy: 79.59%

### Framework versions
- Pytorch 2.0.1+cu118
- Datasets 2.14.5