teguhteja/ttm_cnn_model

ResNet18 Fine-Tuned on CIFAR-10

This model is a fine-tuned version of ResNet18 (originally pretrained on ImageNet) on the CIFAR-10 dataset. It achieves the following results on the validation/test set:

• Validation Accuracy: 88.60%

---

Model description

• Architecture: ResNet18 with the final fully-connected layer replaced by a 10-class output layer for CIFAR-10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).
• Pretrained Weights: ImageNet1K
• Fine-Tuning: The model was fine-tuned on CIFAR-10 images resized to 128×128 pixels.
• Data Augmentation: Random horizontal flip, random rotation, normalization to mean=0.5 and std=0.5.

Intended uses & limitations

• Intended use: Educational/demo purposes or as a starting point for further fine-tuning on similar image classification tasks.
• Not intended for: Production-critical tasks without further evaluation, as CIFAR-10 is relatively small-scale, and the model may not generalize to non-CIFAR data without additional fine-tuning.

---

Training procedure

Hyperparameters (approximate):

• optimizer: Adam
• learning_rate: 1e-3
• batch_size: 32
• num_epochs: 15

GPU/CPU:

• This model was trained on a single GPU (torch.device("cuda")) if available, otherwise CPU.

Training logs (for each epoch on the training set):

Epoch	Training Loss	Training Accuracy	Validation Accuracy
1	0.7013	76.52%	-
2	0.4248	85.64%	-
3	0.3185	89.07%	-
4	0.2341	92.06%	-
5	0.1762	93.86%	-
6	0.1302	95.55%	-
7	0.1085	96.31%	-
8	0.0925	96.82%	-
9	0.0765	97.37%	-
10	0.0683	97.68%	-
11	0.0655	97.83%	-
12	0.0548	98.18%	-
13	0.0513	98.27%	-
14	0.0461	98.49%	-
15	0.0470	98.41%	88.60%

Note: Validation accuracy was computed at the end of training (final epoch).

---

Usage

Below is a sample usage snippet in Python. Replace username/model_repo_name with the actual model repo id on Hugging Face.

import torch
import torch.nn as nn
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
from PIL import Image

# Download the weights from the Hugging Face Hub
ckpt_path = hf_hub_download(repo_id="username/model_repo_name", filename="cnn_model.pth")

# Define the same model architecture
model = models.resnet18(pretrained=False)
model.fc = nn.Linear(model.fc.in_features, 10)  # for CIFAR-10
model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
model.eval()

# Define transforms
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])

# Example inference
image = Image.open("your_image.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0)  # add batch dimension
with torch.no_grad():
    logits = model(input_tensor)
    predicted_class = logits.argmax(dim=1).item()

print("Predicted class ID:", predicted_class)