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README.md

@@ -1,24 +1,34 @@
+---
+language: en
+tags:
+- image-classification
+- pytorch
+- resnet
+- imagenet
+datasets:
+- imagenet-1k
+metrics:
+- accuracy
+---
 
 # ResNet50 ImageNet Classifier
 
-## Model Description
-This model is a ResNet50 architecture trained on ImageNet dataset.
+This model is a ResNet50 architecture trained on the ImageNet dataset for image classification.
 
-## Performance
-- Best Validation Accuracy: 0.00%
-- Training Epochs: 42
+## Model Description
 
-## Training Details
-- Framework: PyTorch
-- Task: Image Classification
-- Dataset: ImageNet
-- Input Size: 224x224
-- Number of Classes: 1000
+- **Model Type:** ResNet50
+- **Task:** Image Classification
+- **Training Data:** ImageNet (ILSVRC2012)
+- **Number of Parameters:** ~23M
+- **Input:** RGB images of size 224x224
 
 ## Usage
+
 ```python
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 import torch
+from PIL import Image
 
 # Load model and processor
 model = AutoModelForImageClassification.from_pretrained("jatingocodeo/ImageNet")
@@ -34,3 +44,11 @@ with torch.no_grad():
     logits = outputs.logits
     predicted_class = logits.argmax(-1).item()
 ```
+
+## Training
+
+The model was trained on the ImageNet dataset with the following configuration:
+- Optimizer: AdamW
+- Learning Rate: 0.003 with cosine scheduling
+- Batch Size: 256
+- Data Augmentation: RandomResizedCrop, RandomHorizontalFlip, ColorJitter, RandomAffine, RandomPerspective

config.json

@@ -0,0 +1 @@
+{"model_type": "resnet", "architectures": ["ResNet50"], "hidden_size": 2048, "num_hidden_layers": 50, "num_attention_heads": 1, "num_channels": 3, "image_size": 224, "patch_size": 16, "num_labels": 1000, "id2label": {"0": "LABEL_0", "1": "LABEL_1", "2": "LABEL_2", "3": "LABEL_3", "4": "LABEL_4", "5": "LABEL_5", "6": "LABEL_6", "7": "LABEL_7", "8": "LABEL_8", "9": "LABEL_9", "10": "LABEL_10", "11": "LABEL_11", "12": "LABEL_12", "13": "LABEL_13", "14": "LABEL_14", "15": "LABEL_15", "16": "LABEL_16", "17": "LABEL_17", "18": "LABEL_18", "19": "LABEL_19", "20": "LABEL_20", "21": "LABEL_21", "22": "LABEL_22", "23": "LABEL_23", "24": "LABEL_24", "25": "LABEL_25", "26": "LABEL_26", "27": "LABEL_27", "28": "LABEL_28", "29": "LABEL_29", "30": "LABEL_30", "31": "LABEL_31", "32": "LABEL_32", "33": "LABEL_33", "34": "LABEL_34", "35": "LABEL_35", "36": "LABEL_36", "37": "LABEL_37", "38": "LABEL_38", "39": "LABEL_39", "40": "LABEL_40", "41": "LABEL_41", "42": "LABEL_42", "43": "LABEL_43", "44": 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