--- license: mit datasets: - Bingsu/Gameplay_Images language: - en metrics: - accuracy - precision - recall - f1 - roc_auc - confusion_matrix base_model: - google/efficientnet-b0 pipeline_tag: image-classification tags: - game-detection - image-classification - efficientnet - hashtag-generation - computer-vision - gaming --- # Game_Detection ### Automated Video Game Recognition for Hashtag Suggestion on Live Streaming Platforms A 10-class image classifier that identifies which video game is being played from a gameplay screenshot. Built on a fine-tuned [`google/efficientnet-b0`](https://huggingface.co/google/efficientnet-b0) backbone, trained at a custom, aspect-ratio-preserving **180×320** input resolution (instead of the standard 224×224 square crop) on the [`Bingsu/Gameplay_Images`](https://huggingface.co/datasets/Bingsu/Gameplay_Images) dataset. This model was built as part of a university course project (AI Lab, SE334) — *"Automated Video Game Recognition and Hashtag Suggestion for Live Streaming Platforms Using Image Classification"* — and powers the [GameSense](https://gamesense-h456.onrender.com/) demo app. **Authors:** S. M. Nihal Ahmed, Afrim Hossen Khan ## Model Details - **Base model:** `google/efficientnet-b0` - **Task:** Multi-class image classification (10 classes) - **License:** MIT - **Architecture:** EfficientNet-B0 backbone (ImageNet-pretrained), fine-tuned end-to-end with the final classifier layer replaced for 10 output classes. Trained at a custom **180×320** input resolution — half of the source dataset's native 640×360, preserving the true 16:9 aspect ratio — made possible without architectural changes since EfficientNet's `AdaptiveAvgPool2d` head is resolution-agnostic. - **Fine-tuning objective:** Cross-entropy loss with label smoothing (0.1), `sklearn` balanced class weights applied in the loss (the source dataset is already perfectly balanced at 1,000 images/class) - **Training regime:** Mixed-precision (AMP) training on dual CUDA T4 GPUs, AdamW optimizer with a OneCycleLR schedule, up to 25 epochs with early stopping (patience = 6, monitored on validation loss) ## Classes `Among Us, Apex Legends, Fortnite, Forza Horizon, Free Fire, Genshin Impact, God of War, Minecraft, Roblox, Terraria` ## Intended Use This model is intended for identifying which video game is shown in a gameplay screenshot. Example use cases: - Auto-generating hashtags/tags for gameplay clips, stream thumbnails, and social posts - Categorizing or organizing gameplay footage/screenshots by game on a content platform - A component in a larger stream metadata or content-tagging pipeline - Research and coursework on multi-class visual classification **Out of scope:** This model only recognizes the 10 games listed above — any other game will be forced into one of these 10 labels rather than correctly rejected. It has been evaluated on one dataset only, and has not been validated against real-world production streaming footage, unusual camera angles, menu/loading screens, or extensive in-game cosmetic content (e.g. crossover skins) that may visually resemble a different game in the label set. ## How to Use This model is distributed in two formats — pick whichever fits your stack. ### Option A: ONNX (lightweight, CPU-friendly) Download both files and keep them in the same folder — the `.onnx` graph loads its weights from the `.onnx.data` file alongside it at runtime: - [`efficientnet_b0_gameplay.onnx`](https://huggingface.co/nihal4/Game_Detection/resolve/main/efficientnet_b0_gameplay.onnx) — the ONNX graph - [`efficientnet_b0_gameplay.onnx.data`](https://huggingface.co/nihal4/Game_Detection/resolve/main/efficientnet_b0_gameplay.onnx.data) — the external weights file Install dependencies: ```bash pip install onnxruntime huggingface_hub pillow numpy ``` #### Single-image prediction ```python import numpy as np import onnxruntime as ort from PIL import Image from huggingface_hub import hf_hub_download REPO_ID = "nihal4/Game_Detection" IMG_SIZE = (320, 180) # PIL resize takes (width, height) IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) CLASS_NAMES = ['Among Us', 'Apex Legends', 'Fortnite', 'Forza Horizon', 'Free Fire', 'Genshin Impact', 'God of War', 'Minecraft', 'Roblox', 'Terraria'] # Downloads both files into the same local cache folder — required, since the # .onnx graph references .onnx.data by relative path at load time. onnx_path = hf_hub_download(repo_id=REPO_ID, filename="efficientnet_b0_gameplay.onnx") hf_hub_download(repo_id=REPO_ID, filename="efficientnet_b0_gameplay.onnx.data") session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"]) input_name = session.get_inputs()[0].name output_name = session.get_outputs()[0].name def preprocess_pil(img: Image.Image) -> np.ndarray: img = img.convert("RGB").resize(IMG_SIZE) arr = np.asarray(img, dtype=np.float32) / 255.0 # HWC, [0,1] arr = (arr - IMAGENET_MEAN) / IMAGENET_STD # normalize, same stats as training return arr.transpose(2, 0, 1) # HWC -> CHW def softmax(x: np.ndarray) -> np.ndarray: e = np.exp(x - x.max(axis=1, keepdims=True)) return e / e.sum(axis=1, keepdims=True) def predict(image_path: str): image = Image.open(image_path) x = preprocess_pil(image)[np.newaxis, ...].astype(np.float32) logits = session.run([output_name], {input_name: x})[0] probs = softmax(logits)[0] top_idx = int(probs.argmax()) return CLASS_NAMES[top_idx], probs label, probs = predict("path/to/screenshot.jpg") print(f"Prediction: {label}") for name, p in sorted(zip(CLASS_NAMES, probs), key=lambda t: -t[1]): print(f" {name:<16} {p*100:5.1f}%") ``` #### Batch prediction ```python image_paths = ["shot1.jpg", "shot2.jpg", "shot3.jpg"] batch = np.stack([preprocess_pil(Image.open(p)) for p in image_paths]).astype(np.float32) logits = session.run([output_name], {input_name: batch})[0] probs = softmax(logits) preds = probs.argmax(axis=1) for path, pred, p in zip(image_paths, preds, probs): print(f"{path}: {CLASS_NAMES[int(pred)]} ({p[int(pred)]*100:.1f}%)") ``` > For GPU inference, install `onnxruntime-gpu` instead and pass > `providers=["CUDAExecutionProvider", "CPUExecutionProvider"]` when creating the session. ### Option B: PyTorch (.pth checkpoint) Download the checkpoint: - [`efficientnet_b0_gameplay_final.pth`](https://huggingface.co/nihal4/Game_Detection/resolve/main/efficientnet_b0_gameplay_final.pth) Install dependencies: ```bash pip install torch torchvision huggingface_hub pillow numpy ``` #### Single-image prediction ```python import torch import torch.nn as nn import numpy as np from torchvision import models, transforms from PIL import Image from huggingface_hub import hf_hub_download REPO_ID = "nihal4/Game_Detection" IMG_SIZE = (180, 320) # (H, W) — torchvision transforms convention CLASS_NAMES = ['Among Us', 'Apex Legends', 'Fortnite', 'Forza Horizon', 'Free Fire', 'Genshin Impact', 'God of War', 'Minecraft', 'Roblox', 'Terraria'] ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="efficientnet_b0_gameplay_final.pth") checkpoint = torch.load(ckpt_path, map_location="cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = models.efficientnet_b0(weights=None) in_features = model.classifier[1].in_features model.classifier[1] = nn.Linear(in_features, len(CLASS_NAMES)) model.load_state_dict(checkpoint["model_state_dict"]) model.to(device).eval() transform = transforms.Compose([ transforms.Resize(IMG_SIZE), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) @torch.no_grad() def predict(image_path: str): image = Image.open(image_path).convert("RGB") x = transform(image).unsqueeze(0).to(device) logits = model(x) probs = torch.softmax(logits, dim=1)[0] top_idx = int(probs.argmax()) return CLASS_NAMES[top_idx], probs.cpu().numpy() label, probs = predict("path/to/screenshot.jpg") print(f"Prediction: {label}") for name, p in sorted(zip(CLASS_NAMES, probs), key=lambda t: -t[1]): print(f" {name:<16} {p*100:5.1f}%") ``` #### Batch prediction ```python from torch.utils.data import Dataset, DataLoader class ImageListDataset(Dataset): def __init__(self, paths, transform): self.paths = paths self.transform = transform def __len__(self): return len(self.paths) def __getitem__(self, i): img = Image.open(self.paths[i]).convert("RGB") return self.transform(img), self.paths[i] image_paths = ["shot1.jpg", "shot2.jpg", "shot3.jpg"] loader = DataLoader(ImageListDataset(image_paths, transform), batch_size=8) model.eval() with torch.no_grad(): for images, paths in loader: images = images.to(device) logits = model(images) probs = torch.softmax(logits, dim=1) preds = probs.argmax(dim=1) for path, pred, p in zip(paths, preds, probs): print(f"{path}: {CLASS_NAMES[int(pred)]} ({p[int(pred)]*100:.1f}%)") ``` ## Training Data The model was fine-tuned on the [`Bingsu/Gameplay_Images`](https://huggingface.co/datasets/Bingsu/Gameplay_Images) dataset — 10,000 gameplay screenshots (1,000 per class) at native 640×360 resolution, PNG format. - **Labels:** 10 classes (see [Classes](#classes) above) - **Splits:** Stratified 70 / 15 / 15 train / validation / test (the source dataset ships a single `train` split only; the split above was carved out manually, preserving per-class balance) - **Preprocessing:** Resize to 180×320 (custom, aspect-ratio-preserving resolution), ImageNet normalization (mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]`) - **Training augmentation:** Random horizontal flip, color jitter, random rotation (±8°), random erasing - **Class balancing:** The dataset is already perfectly balanced (1,000 images/class); `sklearn` balanced class weights are still computed and applied in the loss as a safeguard ## Training Procedure ![training_curves](https://cdn-uploads.huggingface.co/production/uploads/661d43ec3cf2981df52d0756/lBckExVfa7nctNBbRq36B.png) - **Framework:** PyTorch - **Hardware:** Kaggle free-tier T4 x2 GPUs - **Loss:** Cross-entropy with label smoothing (0.1) - **Mixed precision:** Enabled (AMP) ## Evaluation Evaluated on the held-out test split (n = 1,500) at a decision threshold of 0.5. ### Classification Report | Class | Precision | Recall | F1-score | Support | |----------------|:---------:|:------:|:--------:|:-------:| | Among Us | 1.0000 | 1.0000 | 1.0000 | 150 | | Apex Legends | 1.0000 | 0.9933 | 0.9967 | 150 | | Fortnite | 1.0000 | 1.0000 | 1.0000 | 150 | | Forza Horizon | 1.0000 | 1.0000 | 1.0000 | 150 | | Free Fire | 1.0000 | 1.0000 | 1.0000 | 150 | | Genshin Impact | 0.9934 | 1.0000 | 0.9967 | 150 | | God of War | 1.0000 | 1.0000 | 1.0000 | 150 | | Minecraft | 1.0000 | 1.0000 | 1.0000 | 150 | | Roblox | 1.0000 | 1.0000 | 1.0000 | 150 | | Terraria | 1.0000 | 1.0000 | 1.0000 | 150 | | **accuracy** | | | **0.9993** | 1,500 | | macro avg | 0.9993 | 0.9993 | 0.9993 | 1,500 | | weighted avg | 0.9993 | 0.9993 | 0.9993 | 1,500 | **Test ROC-AUC:** 1.0000 (macro average; per-class AUC is also 1.0000 across all 10 classes) ### Confusion Matrix ![confusion_matrix](https://cdn-uploads.huggingface.co/production/uploads/661d43ec3cf2981df52d0756/GgkCX5ir4A12Iip96hrQy.png) ### ROC Curve ![roc_auc_curves](https://cdn-uploads.huggingface.co/production/uploads/661d43ec3cf2981df52d0756/Q0J0IoHtaHhkq_IaTP-Qf.png) ## Limitations - Performance is reported on a single dataset; generalization to other capture sources, image qualities, camera angles, or game versions/UI updates is not guaranteed. - The classifier is closed-set — it will always assign one of the 10 trained classes, even to games or content it has never seen, rather than rejecting out-of-distribution input. - Confidence can be lower on visually ambiguous content, such as games with extensive cosmetic/skin systems whose art style can resemble another class in the label set. - The model has not been evaluated as a standalone production guardrail; low-confidence predictions should be handled with a confidence threshold or human review rather than trusted outright. ## Citation If you use this model, please cite this repository and reference this course project: ``` @misc{game-detection-classifier, title = {Automated Video Game Recognition and Hashtag Suggestion for Live Streaming Platforms Using Image Classification}, author = {S. M. Nihal Ahmed and Afrim Hossen Khan}, year = {2026}, note = {Course project, AI Lab (SE334), Daffodil International University} } ```