File size: 1,873 Bytes
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Mean and STD:
- lat_mean: 39.95177538047139
- lat_std: 0.000688423824245344
- lon_mean: -75.19147811784511
- lon_std: 0.0006632296829719546
Implemented a ResNet50-based model using PyTorch: |
import torch
import torch.nn as nn
from torchvision.models import resnet50
class CustomResNet50(nn.Module):
def __init__(self, num_classes=2):
super().__init__()
self.model = resnet50(pretrained=False)
num_features = self.model.fc.in_features
self.model.fc = nn.Linear(num_features, num_classes)
def forward(self, x):
return self.model(x)
Run the following code to access the model: |
from huggingface_hub import hf_hub_download
import torch
import torch.nn as nn
from torchvision.models import resnet50
repo_id = "ImageGPSProj/ResNet50Model"
filename = "custom_resnet50.pth"
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
# Re-instantiate the architecture
loaded_model = resnet50(pretrained=False)
num_features = loaded_model.fc.in_features
loaded_model.fc = nn.Linear(num_features, 2)
# Load the state_dict
state_dict = torch.load(model_path, map_location=torch.device('cpu'))
loaded_model.load_state_dict(state_dict)
loaded_model.eval()
dataset_info:
features:
- name: image
dtype: image
- name: Latitude
dtype: float64
- name: Longitude
dtype: float64
splits:
- name: train
num_bytes: 6747451504
num_examples: 825
- name: test
num_bytes: 928890377
num_examples: 105
- name: val
num_bytes: 791887265
num_examples: 102
download_size: 7405818019
dataset_size: 8468229146
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
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