
# Model Trained Using AutoTrain

Image classification model trained to predict whether a patch of a historic map contains 'railspace' or not. See the [dataset](https://huggingface.co/datasets/Livingwithmachines/MapReader_Data_SIGSPATIAL_2022) used for training for more information on the labels.

- Problem type: Multi-class Classification
- Model ID: 40830105612
- CO2 Emissions (in grams): 0.0081

## Validation Metrics

- Loss: 0.038
- Accuracy: 0.995
- Macro F1: 0.983
- Micro F1: 0.995
- Weighted F1: 0.995
- Macro Precision: 0.991
- Micro Precision: 0.995
- Weighted Precision: 0.995
- Macro Recall: 0.975
- Micro Recall: 0.995
- Weighted Recall: 0.995
{"tags": ["autotrain", "vision", "image-classification", "lam"], "datasets": ["Livingwithmachines/MapReader_Data_SIGSPATIAL_2022"], "widget": [{"src": "https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/1.png", "example_title": "patch"}, {"src": "https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/271.png", "example_title": "patch"}], "co2_eq_emissions": {"emissions": 0.008077657735064319}, "pipeline_tag": "image-classification"}
transformers pytorch safetensors swin image-classification autotrain vision lam dataset:Livingwithmachines/MapReader_Data_SIGSPATIAL_2022 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #swin #image-classification #autotrain #vision #lam #dataset-Livingwithmachines/MapReader_Data_SIGSPATIAL_2022 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

Image classification model trained to predict whether a patch of a historic map contains 'railspace' or not. See the dataset used for training for more information on the labels.

- Problem type: Multi-class Classification
- Model ID: 40830105612
- CO2 Emissions (in grams): 0.0081

## Validation Metrics

- Loss: 0.038
- Accuracy: 0.995
- Macro F1: 0.983
- Micro F1: 0.995
- Weighted F1: 0.995
- Macro Precision: 0.991
- Micro Precision: 0.995
- Weighted Precision: 0.995
- Macro Recall: 0.975
- Micro Recall: 0.995
- Weighted Recall: 0.995
# Model Trained Using AutoTrain

Image classification model trained to predict whether a patch of a historic map contains 'railspace' or not. See the dataset used for training for more information on the labels.

- Problem type: Multi-class Classification
- Model ID: 40830105612
- CO2 Emissions (in grams): 0.0081 ## Validation Metrics

- Loss: 0.038
- Accuracy: 0.995
- Macro F1: 0.983
- Micro F1: 0.995
- Weighted F1: 0.995
- Macro Precision: 0.991
- Micro Precision: 0.995
- Weighted Precision: 0.995
- Macro Recall: 0.975
- Micro Recall: 0.995
- Weighted Recall: 0.995
TAGS
#transformers #pytorch #safetensors #swin #image-classification #autotrain #vision #lam #dataset-Livingwithmachines/MapReader_Data_SIGSPATIAL_2022 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

Image classification model trained to predict whether a patch of a historic map contains 'railspace' or not. See the dataset used for training for more information on the labels.

- Problem type: Multi-class Classification
- Model ID: 40830105612
- CO2 Emissions (in grams): 0.0081 ## Validation Metrics

- Loss: 0.038
- Accuracy: 0.995
- Macro F1: 0.983
- Micro F1: 0.995
- Weighted F1: 0.995
- Macro Precision: 0.991
- Micro Precision: 0.995
- Weighted Precision: 0.995
- Macro Recall: 0.975
- Micro Recall: 0.995
- Weighted Recall: 0.995
TAGS
#transformers #pytorch #safetensors #swin #image-classification #autotrain #vision #lam #dataset-Livingwithmachines/MapReader_Data_SIGSPATIAL_2022 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

Image classification model trained to predict whether a patch of a historic map contains 'railspace' or not. See the dataset used for training for more information on the labels.

- Problem type: Multi-class Classification
- Model ID: 40830105612
- CO2 Emissions (in grams): 0.0081## Validation Metrics

- Loss: 0.038
- Accuracy: 0.995
- Macro F1: 0.983
- Micro F1: 0.995
- Weighted F1: 0.995
- Macro Precision: 0.991
- Micro Precision: 0.995
- Weighted Precision: 0.995
- Macro Recall: 0.975
- Micro Recall: 0.995
- Weighted Recall: 0.995